In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.

We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting‐based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly–mask–caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template‐based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal‐category image using user‐defined prompts, establishing a versatile foundation model for anomaly generation.

Pre-trained models have demonstrated exceptional generalization capabilities in time-series forecasting; however, adapting them to evolving data distributions remains a significant challenge. A key hurdle lies in accessing the original training data, as fine-tuning solely on new data often leads to catastrophic forgetting. To address this issue, we propose Replay Tuning (R-Tuning), a novel framework designed for the continual adaptation of pre-trained time-series models. R-Tuning constructs a unified latent space that captures both prior and current task knowledge through a frequency-aware replay strategy. Specifically, it augments model-generated samples via wavelet-based decomposition across multiple frequency bands, generating trend-preserving and fusion-enhanced variants to improve representation diversity and replay efficiency. To further reduce reliance on synthetic samples, R-Tuning introduces a latent consistency constraint that aligns new representations with the prior task space. This constraint guides joint optimization within a compact and semantically coherent latent space, ensuring robust knowledge retention and adaptation. Extensive experimental results demonstrate the superiority of R-Tuning, which reduces MAE and MSE by up to 46.9% and 46.8%, respectively, on new tasks, while preserving prior knowledge with gains of up to 5.7% and 6.0% on old tasks. Notably, under few-shot settings, R-Tuning outperforms all state-of-the-art baselines even when synthetic proxy samples account for only 5% of the new task dataset.

Industrial Internet of Things applications like aircraft assembly impose stringent demands on Computing Power Networks. Existing deep reinforcement learning (DRL)-based schedulers not only operate under rigid time-step decision mechanisms but also inadequately handle multi-priority tasks owing to oversimplified queue modeling. To resolve these fundamental limitations, we propose an innovative integrated framework that synergistically combines three components. First, the framework establishes a pioneering formalization of preemptive priority scheduling problem, simultaneously optimizing task response time and violation rate. Second, it incorporates the Counterfactual-Aware Semi-Markov Decision Process (CA-SMDP), which employs counterfactual intervention to tackle temporal credit assignment under event-driven decision epochs. Third, we propose a novel Topology-context fusion Event-driven Scheduler (TESer) where specialized modules for latency minimization and SLA assurance collaboratively achieve optimization synergy. Experimental results demonstrate consistent superiority over state-of-the-art baselines across critical scheduling metrics.

Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However, these methods may suffer from over-generalization, enabling them to reconstruct anomalies well, which leads to poor detection performance. To address this issue, instead of focusing solely on normality reconstruction, we propose an innovative Uncertainty-Integrated Anomaly Perception and Restoration Attention Network (URA-Net), which explicitly restores abnormal patterns to their corresponding normality. First, unlike traditional image reconstruction methods, we utilize a pre-trained convolutional neural network to extract multi-level semantic features as the reconstruction target. To assist the URA-Net learning to restore anomalies, we introduce a novel feature-level artificial anomaly synthesis module to generate anomalous samples for training. Subsequently, a novel uncertainty-integrated anomaly perception module based on Bayesian neural networks is introduced to learn the distributions of anomalous and normal features. This facilitates the estimation of anomalous regions and ambiguous boundaries, laying the foundation for subsequent anomaly restoration. Then, we propose a novel restoration attention mechanism that leverages global normal semantic information to restore detected anomalous regions, thereby obtaining defect-free restored features. Finally, we employ residual maps between input features and restored features for anomaly detection and localization. The comprehensive experimental results on two industrial datasets, MVTec AD and BTAD, along with a medical image dataset, OCT-2017, unequivocally demonstrate the effectiveness and superiority of the proposed method.

Few-shot anomaly detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual semantics to complement visual data, they predominantly rely on features pretrained on natural scenes, thereby neglecting the granular, domain-specific semantics essential for industrial inspection. Furthermore, prevalent fusion strategies often resort to superficial concatenation, failing to address the inherent semantic misalignment between visual and textual modalities, which compromises robustness against cross-modal interference. To bridge these gaps, this study proposes VTFusion, a vision-text multimodal fusion framework tailored for FSAD. The framework rests on two core designs. First, adaptive feature extractors for both image and text modalities are introduced to learn task-specific representations, bridging the domain gap between pretrained models and industrial data; this is further augmented by generating diverse synthetic anomalies to enhance feature discriminability. Second, a dedicated multimodal prediction fusion module is developed, comprising a fusion block that facilitates rich cross-modal information exchange and a segmentation network that generates refined pixel-level anomaly maps under multimodal guidance. VTFusion significantly advances FSAD performance, achieving image-level area under the receiver operating characteristics (AUROCs) of 96.8% and 86.2% in the 2-shot scenario on the MVTec AD and VisA datasets, respectively. Furthermore, VTFusion achieves an AUPRO of 93.5% on a real-world dataset of industrial automotive plastic parts introduced in this article, further demonstrating its practical applicability in demanding industrial scenarios.

To meet personalized user demands, customized and personalized production (CPP) has become an effective manufacturing paradigm. However, wired network connections inhibit flexible production line reconfiguration and current DRL methods cannot converge and obtain eligible scheduling results for CPP due to the high-dimensional solution space and the negligence of significant machine reconfiguration time. To address this challenge, we first propose a wireless manufacturing system framework to support ultra-flexible reconfiguration and resource scheduling. Next, we build a reconfiguration oriented scheduling model to reflect the significant impact of reconfiguration time. Then, we design a knowledge guided deep reinforcement learning algorithm to effectively solve the CPP scheduling problem facing the dimension explosion problem. The knowledge guidance incorporates reconfiguration time and machine workload to significantly reduce the feasible action space, enabling the rapid convergence of KGDRL. The experiment results show that our approach provides a robust and scalable solution and obtains shorter total makespan of whole production during scheduling.

Heterogeneous fault diagnosis scenarios are in evitable in industrial systems due to potential communication issues, sensor failures and different downstream enterprise diagnostic capabilities. Traditional manual models with only a single type structure are difficult to handle heterogeneous inputs with different signal dimensions. This article proposes a multi-model collaborative neural architecture search (MMCNAS) framework for heterogeneous fault diagnosis. Unlike traditional neural architecture search that focuses on parameter-level fine-grained search, a model-level coarse-grained search space is adopted to fully utilize the existing knowledge and architectures, which greatly reduces search time and computational consumption. An avoidance-based diversity search (ADS) strategy is specifically developed for this pattern to avoid repeated combinations of models and positions during the search process. The proposed automated multi-model design and optimization method not only endows intelligent diagnostic systems with universal processing capabilities for heterogeneous signals, but also enables self evolution of the system without the need for time-consuming and labor-intensive manual design for multiple tasks. The proposed method was validated on nine tasks in a real industrial motor production line and achieved over 90% F1 scores.

Detecting anomalies within point clouds is crucial for various industrial applications, but traditional unsupervised methods face challenges due to data acquisition costs, early stage production constraints, and limited generalization across product categories. To overcome these challenges, we introduce the multiview projection (MVP) framework, leveraging pretrained vision-language models (VLMs) to detect anomalies. Specifically, MVP projects point cloud data into multiview depth images, thereby translating point cloud anomaly detection into image anomaly detection. Following zero-shot image anomaly detection methods, pretrained VLMs are utilized to detect anomalies on these depth images. Given that pretrained VLMs are not inherently tailored for zero-shot point cloud anomaly detection and may lack specificity, we propose the integration of learnable visual and adaptive text prompting techniques to fine-tune these VLMs, thereby enhancing their detection performance. Extensive experiments on the MVTec 3-D-AD and Real3D-AD demonstrate our proposed MVP framework’s superior zero-shot anomaly detection performance and the prompting techniques’ effectiveness. Real-world evaluations on automotive plastic part inspection further showcase that the proposed method can also be generalized to practical, unseen scenarios.

Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks. However, existing language models for time series forecasting encounter several obstacles, including aliasing distortion and prolonged inference times, primarily due to the limitations of quantization processes and the computational demands of large models. This paper introduces Apollo-Forecast, a novel framework that tackles these challenges with two key innovations: the Anti-Aliasing Quantization Module (AAQM) and the Race Decoding (RD) technique. AAQM adeptly encodes sequences into tokens while mitigating high-frequency noise in the original signals, thus enhancing both signal fidelity and overall quantization efficiency. RD employs a draft model to enable parallel processing and results integration, which markedly accelerates the inference speed for long-term predictions, particularly in large-scale models. Extensive experiments on various real-world datasets show that Apollo-Forecast outperforms state-of-the-art methods by 35.41% and 18.99% in WQL and MASE metrics, respectively, in zero-shot scenarios. Furthermore, our method achieves an acceleration of 1.9X-2.7X in inference speed over the baseline methods.

Compound faults inevitably occur in system-level equipment. Complex fault coupling and various combination types make diagnosis very challenging. This article proposes a multi-model collaborative decision-making approach for compound fault diagnosis, especially considering the generalization scenario for unseen compound fault combinations. The diagnosis results are determined by two indicators: decision quantity by category and model voting. In addition, batch sample diagnosis replaces the traditional one sample one-time diagnosis to further improve the accuracy of diagnosis and is beneficial for reducing false alarms. Experiments are conducted on a system-level subway bogie with compound faults. The results show that the proposed method achieves the highest accuracy of 90.95%, which is superior to advanced solutions.

Iron and steel industry is a significant basic industry of national economy. Steelmaking-Refining-Continuous Casting (SRCC) is one of the bottlenecks of the iron and steel production process. SRCC scheduling problems are world-wide and NP-hard problems. SRCC scheduling problems considering energy saving are named Energy-Efficient SRCC (EESRCC) scheduling problems. Effective EESRCC scheduling algorithms would not only help to enhance the production efficiency, but also help to reduce the energy saving. This paper proposed a Discrete Growth Optimizer (DGO) to solve the EESRCC scheduling problems. Differ from the traditional GO, the proposed DGO is enhanced by incorporating five strategies. More specifically, a population initialization heuristic is designed to generate a relatively ‘good’ initial population. The control based local search is devised to enhance the intensification and diversification abilities of the proposed DGO. The restricted local search is designed to further improve the three best solutions found so far. The enhanced learning phase is devised to learn from the three best solutions found so far and elite solutions in the population. The multi-type reflection phase is developed to further enhance the solutions in the population. The effectiveness of the DGO has been verified by the experiments.

This paper investigated Integrated Hybrid Flowshop Scheduling with Sequence-Dependent Setup Times and Vehicle Routing Problems Considering Customer Priority (IHFSS-VRPC). The IHFSS-VRPC are joint optimization problems, which integrate the Hybrid Flowshop Scheduling with Sequence-Dependent Setup Times (HFSP-SDST) with the Vehicle Routing Problems (VRP) Considering Customer Priority and maximum driving distance constraint. The problems are different from the Integrated Hybrid Flowshop Scheduling Problems (HFSP) and VRP. To deal with the IHFSS-VRPC effectively, this paper proposed a Hybrid Fireworks Algorithm (HFWA). In the proposed HFWA, six key components, i.e., Two stage decoding, Population initialization, Variable Neighbourhood Search (VNS) based local search, Explosion amplitude calculation, Mutation operator and Selection strategy, were elaborately to enhance the search abilities. More specifically, the Two stage decoding was presented to compile effective schedules, the Population initialization, Explosion amplitude calculation and Selection strategy were devised to balance the exploitation and exploration capacities. The VNS based local search was developed to improve the exploitation capacities, while the Mutation operator was devised to enhance the exploration capacities. The performance of the proposed HFWA has been demonstrated by conducting comparison experiments on a set of instances.

Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on “comparing” test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at: https://github.com/luow23/INPFormer.

The traditional intelligent models developed based on the source device fault diagnosis (SDFD) framework have achieved high accuracy, but may lead to unreliable diagnostic results for unseen individual diagnostic scenarios, as they ignore potential individual differences (such as assembly changes, noise interference). This paper proposes a refined trigonometric activation representation transformer (RTART), which is a signal processing informed neural network (SPINN) tailored for cross individual fault diagnosis (CIFD). By integrating the theory of Sparse Short Time Fourier Transform (DSTFT) and Kolmogorov Arnold Representation Theorem (KART) into structural design of the Transformer, RTART enhances the extraction of periodic vibration features and frequency modulation diversity. Specifically, the original vibration signals are first segmented into regional patches based on one-dimensional convolutional patch (1-DCP) tokenizer, and then input into a multi-scale region pruning (MSRP) module for global feature refinement, which aims to refine decision features for reducing the risk of overfitting due to individual differences. A trigonometric activation representation (TrigAR) module is developed to enhance the reliable feature expression of periodic vibration signals and improve the model generalization. The proposed method is validated on a well-known public dataset using the CIFD benchmark. Compared with the most advanced methods, RTART has better generalization ability and achieves a cross individual accuracy rate of over 95%.

3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6% object-wise AU-ROC and 95.7% point-wise AU-ROC on the Real3D-AD benchmark.

This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-free training data, but real-world contamination leads models to absorb anomalies as normal, degrading detection performance. To mitigate this, we propose a two-stage framework that systematically exploits inherent learning bias in models. The learning bias stems from: (1) the statistical dominance of normal samples, driving models to prioritize learning stable normal patterns over sparse anomalies, and (2) feature-space divergence, where normal data exhibit high intra-class consistency while anomalies display high diversity, leading to unstable model responses. Leveraging the learning bias, stage 1 partitions the training set into subsets, trains sub-models, and aggregates cross-model anomaly scores to filter a purified dataset. Stage 2 trains the final detector on this dataset. Experiments on the Real-IAD benchmark demonstrate superior anomaly detection and localization performance under different noise conditions. Ablation studies further validate the framework’s contamination resilience, emphasizing the critical role of learning bias exploitation. The model-agnostic design ensures compatibility with diverse unsupervised backbones, offering a practical solution for real-world scenarios with imperfect training data. Code is available at https://github.com/hustzhangyuxin/LLBNAD.

Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, necessitating the development of multi-class unsupervised methods. However, the feature similarity between normal and anomalous points from different class data leads to the feature confusion problem, which greatly hinders the performance of multi-class methods. Therefore, we introduce a multi-class point cloud anomaly detection method, named GLFM, leveraging global-local feature matching to progressively separate data that are prone to confusion across multiple classes. Specifically, GLFM is structured into three stages: Stage-I proposes an anomaly synthesis pipeline that stretches point clouds to create abundant anomaly data that are utilized to adapt the point cloud feature extractor for better feature representation. Stage-II establishes the global and local memory banks according to the global and local feature distributions of all the training data, weakening the impact of feature confusion on the establishment of the memory bank. Stage-III implements anomaly detection of test data leveraging its feature distance from global and local memory banks. Extensive experiments on the MVTec 3D-AD, Real3D-AD and actual industry parts dataset showcase our proposed GLFM’s superior point cloud anomaly detection performance.Note to Practitioners—The proposed GLFM is employed for point cloud anomaly detection in industrial inspection, capable of simultaneously processing data across multiple classes. GLFM requires the collection of a set of normal product samples for model training, where the features of these samples are stored. If the feature distribution of a test sample deviates substantially from that of the normal samples, it is flagged as anomalous. GLFM not only exhibits outstanding performance on public datasets but has also been validated on a real-world industrial parts point cloud dataset.

This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond the traditional focus on structural inconsistencies. By harnessing LLMs for logical reasoning, LogiCode autonomously generates Python codes to pinpoint anomalies such as incorrect component quantities or missing elements, marking a significant leap forward in anomaly detection technologies. A custom dataset “LOCO-Annotations” and a benchmark “LogiBench” are introduced to evaluate the LogiCode’s performance across various metrics including binary classification accuracy, code generation success rate, and precision in reasoning. Findings demonstrate LogiCode’s enhanced interpretability, significantly improving the accuracy of logical anomaly detection and offering detailed explanations for identified anomalies. This represents a notable shift towards more intelligent, LLM-driven approaches in industrial anomaly detection, promising substantial impacts on industry-specific applications. Our code are available athttps://github.com/22strongestme/LOCO-Annotations. Note to Practitioners—This work introduces LogiCode, an innovative system leveraging Large Language Models (LLMs) for logical anomaly detection in industrial settings, shifting the paradigm from traditional visual inspection methods. LogiCode autonomously generates Python codes for logical anomaly detection, enhancing interpretability and accuracy. Our novel approach, validated through the “LOCO-Annotations” dataset and LogiBench benchmark, demonstrates superior performance in identifying logical anomalies, a challenge often encountered in complex industrial components like assembly and packaging. LogiCode provides a significant advancement in addressing the nuanced requirements of detecting logical anomalies, offering a robust and interpretable solution to practitioners seeking to enhance quality control and reduce manual inspection efforts.

Surface defect is a crucial factor affecting the product quality of steel products. Current studies mainly focus on defect recognition and classification using machine vision-based algorithms, which lack the trace of potential causes and the reuse of experiential knowledge. To address this issue, we construct a knowledge graph for steel surface defects by fusing the multi-source and heterogeneous industrial data, including process parameters, chemical compositions, defect images, operation logs and empirical knowledge. A policy-based reinforcement learning approach is developed to solve the path reasoning problem over the industrial knowledge graph in defect detection and diagnosis. The approach employs two agents to explore the path efficiently from opposite directions, utilizes an integrated reward function that comprehensively considers the path direction, path length and entity distance to perform action selection, and adopts the path sharing mechanism and the prior knowledge to update selection policy. Experimental comparisons with the state-of-the-art knowledge reasoning algorithms on two benchmark datasets, NELL-995 and FB15K-237, validate the performance and merits of the proposed method. The effectiveness of the proposed method is also evaluated on a practical steel surface defect dataset, and the results show that our approach performs well in knowledge reasoning on the surface defect graph.Note to Practitioners—The surface quality of products has become a widely concerned focus in manufacturing industries. With the development of industrial IoT and Cyber-physical system technologies, more and more industrial data has been collected, and machine learning-based algorithms have been developed and applied to the recognition of detect defects. However, the algorithms do not take full advantage of the multi-source and heterogeneous defect-related data. On the other hand, it is also difficult to accumulate, inherit and reuse the experts’ knowledge of solving historical cases in the long-term production process. In order to deal with the above obstacles, we apply the knowledge graph for steel surface defect detection. In the proposed approach, a policy-based reinforcement learning algorithm is developed to solve the path reasoning problem over the industrial knowledge graph. To further improve the performance of our algorithm, we employ two agents to explore the path efficiently from opposite directions, utilize an integrated reward function which comprehensively considers the path direction, path length and entity distance to perform action selection, adopt the path sharing mechanism and updated selection policy to reuse the prior knowledge. As a result, our algorithm obtains high precision in knowledge reasoning tasks on two benchmark datasets and a practical steel surface defect dataset compared with some existing algorithms. Hence, it can be readily applied to real surface defect detection problems and facilitates intelligent manufacturing in steel production.

Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct interactions between users and items, ignoring indirect interactions among users-to-users and items-to-items. This limits the further exploration of potential interests between users and items. Moreover, during the model training, classical recommendation methods usually randomly select uninteracted items of a user as their negative samples. This may introduce significant learning bias, as uninteracted items could be false negatives and still potentially interest the user. To this end, we propose a novel indirect interactions discovery and true negative sampling multimodal recommendation (ITMRec) method to further explore potential user interests and mitigate the issue of false negative samples during learning. Specifically, we propose an indirect interactions discovering (IID) model to explore the latent interests among users-to-users and items-to-items. Then, we propose a true negative sampling (TNS) model to refine negative sampling that can alleviate the false negative sample problem. Finally, we enhance existing collaborative filtering methods by integrating representations derived from multimodal content, indirect interactions discovery, and refined negative sampling strategies, allowing for more precise alignment with users’ latent interests. Extensive experiments on three benchmark datasets demonstrate that our ITMRec significantly outperforms state-of-the-art recommendation baselines, achieving a 3.64% improvement over peer methods. The code is available athttps://github.com/long-best/ITMRec.git.

Zero-shot anomaly detection (ZSAD) aims to develop a foundational model capable of detecting anomalies across arbitrary categories without relying on reference images. However, since "abnormality" is inherently defined in relation to "normality" within specific categories, detecting anomalies without reference images describing the corresponding normal context remains a significant challenge. As an alternative to reference images, this study explores the use of widely available product standards to characterize normal contexts and potential abnormal states. Specifically, this study introduces AnomalyVLM, which leverages generalized pretrained vision-language models (VLMs) to interpret these standards and detect anomalies. Given the current limitations of VLMs in comprehending complex textual information, AnomalyVLM generates hybrid prompts-comprising prompts for abnormal regions, symbolic rules, and region numbers-from the standards to facilitate more effective understanding. These hybrid prompts are incorporated into various stages of the anomaly detection process within the selected VLMs, including an anomaly region generator and an anomaly region refiner. By utilizing hybrid prompts, VLMs are personalized as anomaly detectors for specific categories, offering users flexibility and control in detecting anomalies across novel categories without the need for training data. Experimental results on four public industrial anomaly detection datasets, as well as a practical automotive part inspection task, highlight the superior performance and enhanced generalization capability of AnomalyVLM, especially in texture categories. An online demo of AnomalyVLM is available at https://github.com/caoyunkang/Segment-Any-Anomaly.

This article addresses a practical task: high-resolution image anomaly detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD imposes a heavier computational burden and necessitates superior global information capture capacity. To tackle HRIAD, this article translates image anomaly detection into visual token prediction and proposes visual autoregressive modeling-based anomaly detection (VarAD) based on visual autoregressive modeling for token prediction. Specifically, VarAD first extracts multihierarchy and multidirectional visual token sequences, and then employs an advanced model, Mamba, for visual autoregressive modeling and token prediction. During the prediction process, VarAD effectively exploits information from all preceding tokens to predict the target token. Finally, the discrepancies between predicted tokens and original tokens are utilized to score anomalies. Comprehensive experiments on four publicly available datasets and a real-world button inspection dataset demonstrate that the proposed VarAD achieves superior HRIAD performance while maintaining lightweight, rendering VarAD a viable solution for HRIAD.

Few-shot anomaly detection (FSAD) denotes the identification of anomalies within a target category with a limited number of normal samples. Existing FSAD methods largely rely on pretrained feature representations to detect anomalies, but the inherent domain gap between pretrained representations and target FSAD scenarios is often overlooked. This study proposes a prototypical learning-guided context-aware segmentation network (PCSNet) to address the domain gap, thereby improving feature descriptiveness in target scenarios and enhancing FSAD performance. In particular, PCSNet comprises a prototypical feature adaption (PFA) subnetwork and a context-aware segmentation (CAS) subnetwork. PFA extracts prototypical features as guidance to ensure better feature compactness for normal data while distinct separation from anomalies. A pixel-level disparity classification (PDC) loss is also designed to make subtle anomalies more distinguishable. Then a CAS subnetwork is introduced for pixel-level anomaly localization, where pseudo anomalies are exploited to facilitate the training process. Experimental results on MVTec AD and metal part defect detection (MPDD) demonstrate the superior FSAD performance of PCSNet, with 94.9% and 80.2% image-level area under the receiver operating characteristics (AUROCs) in an eight-shot scenario, respectively. Real-world applications on automotive plastic part inspection further demonstrate that PCSNet can achieve promising results with limited training samples. The code is available at https://github.com/yuxin-jiang/PCSNet.

Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. Moreover, they predominantly operate within a closed-set detection framework, limiting their ability to identify anomalies beyond the training dataset. To tackle these challenges, this article introduces a novel zero-shot texture anomaly detection method named global-regularized neighborhood regression (GRNR). Unlike conventional approaches, GRNR can detect anomalies on arbitrary textured surfaces without any training data or cost. Drawing from human visual cognition, GRNR derives two intrinsic prior supports directly from the test texture image: local neighborhood priors characterized by coherent similarities and global normality priors featuring typical normal patterns. The fundamental principle of GRNR involves utilizing the two extracted intrinsic support priors for self-reconstructive regression of the query sample. This process employs the transformation facilitated by local neighbor support while being regularized by global normality support, aiming to not only achieve visually consistent reconstruction results but also preserve normality properties. We validate the effectiveness of GRNR across various industrial scenarios using eight benchmark datasets, demonstrating its superior detection performance without the need for training data. Remarkably, our method is applicable for open-set texture defect detection and can even surpass existing vanilla approaches that require extensive training.

This paper considers a flexible job shop rescheduling problem under machine breakdowns to minimize the sum of the deviations from the preschedule. A deep reinforcement learning (DRL)-based rescheduling method is proposed for the problem. Total slack transmission graph is proposed as the input of the deep neural network and graph attention network is adopted to extract the features of the graph. Operation selection network and machine selection network are designed to perform a series of actions with the aim of repairing the preschedule that affected by a machine breakdown. Especially, a "done" network is designed to stop the action selections. The numerical results show that the proposed DRL-based rescheduling method is effective with comparisons of reactive recovery rescheduling heuristics.

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re- sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. Note to Practitioners—To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

Cloud manufacturing (CMfg) provides on-demand services offered by the cloud platform to satisfy the individual requirements of users. However, the dynamics and uncertainty of the CMfg environment pose significant challenges to implementation of efficient synchronous scheduling of processing and logistic services. This paper proposes a game theory-based collaborative scheduling approach for effective utilization of distributed manufacturing and logistic resources with fuzzy uncertain task migration in CMfg, and the Nash equilibrium in this game theory is realized by a decision tree optimization algorithm. With this model, manufacturing and logistic services can cope with unexpected events whether or not task migration occurs. Moreover, the proposed approach takes into account independent and shared logistics, as well as delayed logistics, to improve the efficiency of transportation along the same route. Simulation results demonstrate that this approach is not only effective for the relevant optimization objective but also can achieve great performance under dynamic CMfg environments.Note to Practitioners—To formulate optimal production planning, how to solve the optimization of manufacturing and logistic resources is the main focus of smart manufacturing systems. In this paper, game theory is introduced to address the collaborative service scheduling issues in cloud manufacturing. Previous dynamic scheduling methods cannot further distinguish between independent logistics and shared logistics. Also, they rarely evaluate the impact of the task migration strategy on the occurrence of unexpected events from game point of view. Therefore, in this paper, a unique two-layer scheduling method based on game theory model that is composed of a processing service scheduling sub-game and a logistic service scheduling sub-game is presented. This model is implemented by adopting a proposed decision tree optimization algorithm that settles constrained non-linear programming. Simulation experiments show that the proposed method outperforms existing scheduling methods in terms of operational efficiency and fuzzy task migration decisions. Additionally, this method can be readily implemented and incorporated into real production settings. Future work could improve the proposed method by analyzing the uncertainties in point-to-point and hub-and-spoke networks across different transportation routes.

In real-life production systems, arrivals of jobs are usually unpredictable, which makes it necessary to develop solid reactive scheduling policies to meet delivery requirements. Deep reinforcement learning (DRL) based scheduling methods are capable of quickly responding to dynamic events by learning from the training data. However, most of policy networks in DRL algorithms are trained to choose priority dispatching rules (PDR), thus, to some extent, the efficiency of obtained scheduling plans is limited by the performance of PDRs. This paper investigates a dynamic flexible job shop scheduling problem with random job arrivals for the total tardiness minimization. A DRL-based reactive scheduling method, proximal policy optimization with attention-based policy network (PPO-APN), is proposed to make real-time decisions for the dynamic scheduling environment, where the attention-based policy network (APN) is able to directly select pending jobs distinguished from the action space that consists of PDRs. Additionally, a global/local reward function (GLRF) is designed to address the reward sparsity issue during training processes. The proposed PPO-APN is tested on randomly generated instances with different production configurations, and is compared with frequently-used PDRs and DRL-based methods. Numerical experimental results indicate that APN and GLRF components significantly improve the training efficiency, and the PPO-APN shows better overall performance compared with other methods.Note to Practitioners—This work is motivated by a typical production scenario in discrete manufacturing systems, where orders randomly arrive at the shop floor and require to be scheduled in a short time to ensure the on-time delivery. Previous research work tends to apply DRL algorithms to choose suitable dispatching rules for the ease of implementation. Nevertheless, the jobs that can be selected by dispatching rules are rather limited, thus many possible high-quality scheduling plans are ignored. This work first sorts all the unscheduled jobs by a heuristic algorithm, and puts some of top-ranked jobs to a pool. When a machine becomes available, it will directly choose a job from the pool as the next processing task. The job selection policy is represented by a novel attention-based network, and is trained by a powerful DRL algorithm. The aforementioned process is repeatedly executed in a simulation environment to collect the training data. Therefore, after being trained for a certain period of time, the policy will become smarter and can be applied to make right decisions in real-time. The proposed reactive scheduling method has been proved to be more efficient than dispatching rules and DRL-based approaches, and is effective in the production scheduling for a wide variety of discrete manufacturing scenarios, such as automobile and electronics industries. Moreover, the proposed method can be further extended to address dynamic scheduling problems with some production characteristics via adding constraints for the job selection or re-defining calculations for the completion time of operations accordingly.

Some local search methods have been incorporated into surrogate-assisted multi-objective evolutionary algorithms to accelerate the search toward the real Pareto front (PF). In this article, a PF model-based local search method is proposed to accelerate the exploration and exploitation of the PF. It first builds a predicted PF model with current nondominated solutions. Then, some sparse points in the predicted PF are selected to guide the search directions of the local search in order to promote the search of promising sparse areas. The approximation degree of the predicted and real PFs will influence the speed of the local search, while extreme points can significantly influence the shape of the PF. To accelerate the search progress, the optima of surrogate models are utilized to promote the progress of finding extreme points. The proposed local search method is incorporated into a surrogate-assisted multi-objective evolutionary algorithm. The proposed surrogate-assisted multi-objective evolutionary algorithm with the proposed local search method is tested with Zitzler-Deb-Thiele (ZDT), Deb-Thiele-Laummans-Zitzler (DTLZ), and MAF instances. The experimental results demonstrated the efficiency of the proposed local search method and the superiority of the proposed algorithm.

Intelligent fault diagnosis of industrial motors under different operating conditions is a valuable and challenging topic. Compound faults are inevitable to occur under different operating conditions. Due to the high cost of dismantling and testing, it is impractical to collect and label all compound fault types under different operating conditions. This article proposes a cross-domain compound fault diagnosis framework of machine-level motors without target domain information. A novel time-frequency self-contrastive learning (TFSCL) strategy is proposed to enhance domain-irrelevant feature extraction. TFSCL generates homogeneous and heterogeneous information of the input itself from time domain and frequency domain to construct self-contrastive pairs. The multiscale spatial convolution structure and the cross time–frequency information interaction strategy are designed to further provide fusion and interaction. Ablation experiments are performed on real industrial motor signals. TFSCL achieved F1-scores of over 90% on six cross-domain tasks, which is superior to compared advanced lightweight models.

Optimizing resource allocation is critical to achieving energy-efficient industrial Internet-of-Things (IIoT). Many tasks that require grouping IIoT devices with rich connectivity can be modeled as the well-known graph partitioning problem. However, little attention has been paid to those tasks where nodes with few connections are expected to be clustered together, which is the inverse graph partitioning (IGP) problem. Here, we focus on the IGP problem abstracted from real IIoT applications, such as spectrum allocation. First, we build a unified mathematical model for the IGP problem and analyze its characteristics in detail. Then, a novel optimization approach is proposed to provide compelling solutions, which incorporates a node clustering model based on a graph convolutional network (GCN) and a node swap procedure for local optimization. We compare the proposed approach with various baselines on substantial synthetic and real-world networks. Empirical results show that the proposed approach achieves excellent performance, especially in large networks.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multiclass anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multiclass scenarios encounters challenges, such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the prior normality prompt transformer (PNPT) method for multiclass image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the “identical mapping” problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: 1) class-specific normality prompting pool, 2) hierarchical patch embedding, 3) semantic alignment coupling encoding, and 4) contextual semantic conditional decoding. Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multiclass industrial anomaly detection.

The maturation of sensor network technologies has promoted the emergence of the Industrial Internet of Things, which has been collecting an increasing volume of monitoring data. Transforming these data into actionable intelligence for equipment fault diagnosis can reduce unscheduled downtime and performance degradation. In conventional artificial intelligence paradigms, abundant individual data distributed across clients’ devices needs to be delivered to a central storage for data analysis and knowledge extraction, which may violate data privacy requirements and neglect distribution discrepancy across different clients. To tackle the issue of privacy disclosure, an edge-cloud integrated federated learning framework is developed. Then, a two-stage training mechanism is designed to establish a domain-agnostic fault diagnosis model that can achieve satisfactory diagnostic performance on unseen target domains. Comprehensive simulated experiments on two rotating machines indicate that the proposed method possesses good generalization ability and can meet the requirement of privacy protection.

Real-Time Traffic Flow Prediction (RT-TFP) is one of the critical technologies for implementing the Intelligent Transportation System (ITS), enabling rapid and accurate prediction of real-time traffic flow at intersections. RT-TFP typically needs to be deployed on-site edge devices for real-time traffic flow calculation that requires low inference latency and minimal computational resources. However, the existing Traffic Flow Prediction (TFP) models are generally based on spatiotemporal graph neural networks (STGNNs), which are complex and require high computational resources and relatively high inference times that can hardly be deployed on edge devices. To this end, this work proposes a simple RT-TFP model, SpatioTemporal-MultiLayer Perceptron (ST-MLP), which requires low computational resources and inference times. The base idea of this work is to establish a spatio-temporal MLP model to replace the STGNN model for conducting the TFP, which is much faster and simpler. Specifically, first, a TempEncoder is proposed to encode the temporal information into the MLP features. Then, a Spatiotemporal Mixer is proposed to mix spatial information into the temporal-enriched MLP features. After, MLP features are distilled from a complex STGNN model to obtain a simple MLP that inherits complete Spatial-Temporal information of the traffic graph. The experimental results on four real-world datasets show the proposed model achieves competitive prediction accuracy with STGNN models in much fewer computational resources and lower prediction time costs. It is worth noting that, the proposed method is faster than the compared STGNNs by an average of 21.62 times (~10.81s$\rightsquigarrow ~\sim 0.50$s). Interestingly, the proposed ST-MLP even has a −3.23% error rate decreasing on average compared to the corresponding STGNN model. Moreover, the error rate of the proposed ST-MLP decreases over pure MLPs by −3.92%$\sim -42.62$%. The source code is available at:https://github.com/zhangjunfeng1234/ST-MLP

Recent advances in distributed machine learning and wireless network technologies are bringing new opportunities for Internet of Things (IoT) systems, where smart devices are often wirelessly connected to collaborate, jointly completing tasks known ascommunication-dependent computing (CDC)tasks. However, due to the dependence of computing on communication and the presence of concurrent tasks, it remains a challenge to optimize CDC task performance and efficiency while fulfilling multi-dimensional requirements, particularly with incomplete system information and dynamic environmental impacts. To overcome these, we present a concurrent CDC task framework to model the correlated communication and computing stages and multi-dimensional requirements of CDC tasks. We then formulate a task orchestration and resource management problem to optimize overall utility, where each task's utility is designed as a joint metric including the cumulative computing deviation and time efficiency of task completion. To solve this, we employ auxiliary graphs to capture the topological information of tasks and resources, and update weights based on the utility in dynamic environments. Subsequently, a multi-agent reinforcement learning algorithm is leveraged to make distributed decisions with incomplete information. Experiments demonstrate the proposed approach outperforms baselines in terms of task performance and efficiency, indicating our solution holds great potential.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Cross-machine fault diagnosis (CMFD) of complex equipment is necessary for modern intelligent manufacturing systems. Manufacturing and assembly errors lead to inherent individual differences in machine-level computer numerical control (CNC) spindle motors, resulting in more challenging diagnostic requirements. The verification of the CMFD task is essential to ensure the reliability and effectiveness of machine-level diagnosis, but is often ignored in current data driven approaches. The latest transformer architecture, known for its excellent global feature extraction ability, is an ideal solution but has not yet been applied in this scenario. This article proposes a novel multichannel signal transformer (MSiT) method specifically toward CMFD task of machine-level CNC spindle motors. Specifically, this article presents a special tokenizer that is suitable for processing multichannel signals as the inputs of transformer, namely the unidirectional patch (UDP). It performs on all the channels to capture channel correlation features without additional transformations. The effect of structural hyperparameters on fault diagnosis performance is analyzed in detail for engineering reference. The superiority of the proposed method is validated using real industrial motor signals in comparison with the benchmark models and some state-of-the-art methods. Besides, the bidirectional decision-making mechanism of MSiT is revealed based on t-SNE and heatmaps.

Image anomaly localization is a pivotal technique in industrial inspection, often manifesting as a supervised task where abundant normal samples coexist with rare abnormal samples. Existing supervised methods in this context are prone to overfitting, as they primarily encounter anomalies that represent only a fraction of the open-world anomalies. Conversely, unsupervised methods excel in performance, yet they disregard the essential biased knowledge pertaining to both seen and unseen anomalies within the open world. To bridge this gap and refine unsupervised methods for supervised applications, this study introduces a comprehensive framework called biased students (BiaS), mainly comprising a three-step strategy. This strategy encompasses biased knowledge generation, transfer, and fusion. BiaS effectively segregates the vast anomaly space into two subsets: 1) unseen anomalies and 2) seen anomalies. Subsequently, it generates specialized biased knowledge for these subsets and transfers this knowledge to two distinct subnetworks. As a result, one subnetwork becomes adept at detecting unseen anomalies, while the other excels in localizing seen anomalies. To optimize their capabilities, BiaS synergistically fuses these subnetworks based on their expertise. Rigorous experimentation has empirically validated the effectiveness, generality, and scalability of BiaS, underscoring its potential to enhance unsupervised methods and effectively address the challenges of supervised anomaly localization.

Location awareness and time consensus, which are two intertwined aspects of distributed systems, have become more important in vertical industrial Internet of Things (IoT) applications. Existing integrated localization and synchronization (ILAS) in a connected system relies on collaborative measurement of time of arrival, as well as exchange of estimated location and clock related states. However, with the growing scale and dynamics of wireless IoT systems, the unselected and excessive information obtained from the collaborating nodes becomes less effective in ILAS. To enhance the performance of ILAS with controlled complexity, we first propose an optimal measurement geometry directed collaborating nodes selection scheme in this paper. Specifically, the optimal measurement geometry evaluated by the dilution of precision is utilized to prioritize the corresponding subset collaborating nodes for the best estimation accuracy with limited complexity. Moreover, to further reduce the computation complexity in increased-scale systems, a sequential state stacking belief propagation algorithm is proposed for the related states estimation, where the matrix inversions and square root calculations reduce to the dimensions of a subset of the overall collaborating states. Numerical simulations demonstrate a significant enhancement in the robustness of the ILAS estimation and reduction in the computational complexity compared to the baseline schemes.

This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.

This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.

Future smart factories need to use intelligent transport devices like automated guided vehicles (AGVs) for connecting intelligent production and logistics. To address the lack of edge side functions in the current AGV systems, this paper proposes an agent-based system architecture for AGVs in cloud-edge computing environments. The system is divided into three main components: the cloud center, edge nodes, and AGV agents. The cloud center is largely responsible for AGV transport route planning, while the edge nodes are in charge of AGV transport control and equipment management. The driving function and executing commands are handled by AGV agents. AGV agents can communicate and collaborate with each other to address emergent issues. The proposed approach has been validated through simulations.

Traditional data-driven fault diagnosis methods generally assume that the training and testing distributions are the same, which does not hold in real-world industrial applications. To address domain shift problems, domain generalization-based fault diagnosis (DGFD) methods have been explored to achieve real-time cross-domain fault diagnosis. Some progress has been made in the area of DGFD for years. This paper presents an overview of recent advances in DGFD. First, we provide a formal definition of domain generalization and discuss several related learning paradigms used in intelligent fault diagnosis. Second, we define several major applications of domain generalization in intelligent fault diagnosis. Then, the motivations and challenges of these applications are discussed, and current solutions are summarized.

This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data. A model-free and off-policy deep reinforcement learning (DRL) algorithm is employed to train the driving agent, which incorporates a carefully designed reward function and multi-task learning technique to enhance generalization across diverse driving tasks and scenarios. Experimental results demonstrate the effectiveness of the proposed approach in improving safety and efficiency in the task of traversing unsignalized intersections in mixed-autonomy traffic, and its generalizability to previously unseen scenarios, such as roundabouts. The integration of V2X communication offers a significant data source for autonomous vehicles (AVs) to perceive their surroundings beyond onboard sensors, resulting in a more accurate and comprehensive perception of the driving environment and more safe and robust driving behavior.

The process of recycling and remanufacturing begins with disassembly. Through disassembly, the components with recycling value are decomposed. However, with the rapid development of production automation, designers often ignore the fact that manual operation is flexible but fails to achieve maximum production efficiency and profit. Therefore, the consideration of human factors in disassembly lines holds significant importance. This study delves into the multi-objective optimization of a U-shaped disassembly line balancing problem involving multiple products. A comprehensive objective function is developed, taking into account various factors including employee fatigue and other factors. To address the aforementioned problem, this study uses a collaborative resource allocation strategy within a multi-objective evolutionary algorithm based on decomposition. By comparing the results of different experimental cases, this paper shows that the proposed algorithm is more competitive than the carnivorous plant algorithm, fruit fly optimization algorithm, and Pareto archiving evolutionary strategy.

The cross-machine diagnosis of CNC spindle motors with compound faults is essential and challenging because of the subsystem coupling and individual difference. This paper proposed an in-situ fault diagnosis method for cross machine-level individual diagnosis. Plug-and-play modules are specifically designed inspired by signal processing theory, and are embedded into mainstream CNN-based models as an effective industrial diagnostic model, the multiscale spatial–temporal residual capsule neural networks (MSRCN). The internal mechanism of these new modules is explored through ablation experiments and visualization on real industrial motor signals, which shows MSRCN-based models can enrich the multi-scale feature extraction capabilities and benefits the interference resistance of individual related features. In addition, new evaluation operators for degree of confidence are proposed to comprehensively evaluate the performance of deep learning in classification tasks and the reliability of the decision-making.

As an extension of the classical flow-shop scheduling problem, the hybrid flow-shop scheduling problem (HFSP) widely exists in large-scale industrial production systems and has been considered to be challenging for its complexity and flexibility. Evolutionary algorithms based on encoding and heuristic decoding approaches are shown effective in solving the HFSP. However, frequently used encoding and decoding strategies can only search a limited area of the solution space, thus leading to unsatisfactory performance during the later period. In this article, a hybrid evolutionary algorithm (HEA) using two solution representations is proposed to solve the HFSP for makespan minimization. First, the proposed HEA searches the solution space by a permutation-based encoding representation and two heuristic decoding methods to find some promising areas. Afterward, a Tabu search (TS) procedure based on a disjunctive graph representation is introduced to expand the searching space for further optimization. Two classical neighborhood structures focusing on critical paths are extended to the problem-specific backward schedules to generate candidate solutions for the TS. The proposed HEA is tested on three public HFSP benchmark sets from the existing literature, including 567 instances in total, and is compared with some state-of-the-art algorithms. Extensive experimental results indicate that the proposed HEA performs much better than the other algorithms. Moreover, the proposed method finds new best solutions for 285 hard instances.

Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this article proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO.

Camber is a typical asymmetrical defect of slabs in the hot-rolling process. The identification of camber plays a key role in improving the quality of the finished strip. To obtain the shape information of the camber, we select the derivative dynamic time warping (DDTW) distance as the measure of curve similarity. An iterative self-organizing data analysis clustering algorithm integrated with DDTW is proposed to divide the sample into different clusters. A curve template is generated via polynomial fitting in each cluster based on the dynamic mechanism of camber. Subsequently, the dynamic time warping (DTW) and DDTW distances are adopted as the input and a weighted random forest (WRF) algorithm is developed for the camber classification to address the sample imbalance problem. Comparative experiments are conducted on the camber sample collected in the actual factory to test the clustering and classification performances. The experimental results indicate that the error rates (ERRs) of four distance-based methods─Euclidean distance, Pearson distance, DTW, and DDTW are 32.7%, 28.5%, 19.1% and 12.3%, respectively, and ERRs of the four classification algorithms—DDTW-SVM, DDTW-KNN, DDTW-RF, and DDTW-WRF are 9.8%, 5.7%, 4.7%, and 2.1%, respectively, which verifies the superior performance of the proposed method in this article.

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most of these methods fail to learn relations between samples and thus perform poorly without sufficient labeled data. In this article, we propose a new few-shot learning method named dual graph neural network (DGNNet) with residual blocks to address fault diagnosis problems with limited data. First, the residual module learns the feature of samples with image data transferred from original signals. Second, two complete graphs built on the sample features are used to extract the instance-level and distribution-level relations between samples. In particular, an alternate update policy between the instance and distribution graphs integrates the multilevel relations to propagate the label information of a few labeled samples to unlabeled samples. This technique leverages labeled and unlabeled samples to identify unseen faults, encouraging DGNNet competency in fault diagnosis tasks with very few labeled samples. Extensive results on various datasets show that DGNNet achieves excellent performance in supervised fault diagnosis tasks and outperforms baselines by a great margin in semisupervised cases.

Domain generalization-based fault diagnosis has recently emerged to address domain shift problems. Most existing methods learn domain-invariant representations from multiple source domains. However, valuable fault samples from polytropic working conditions are difficult to be collected, and it is quite common that available data are from a single working condition. Therefore, this article proposes an adversarial mutual information-guided single domain generalization network for machinery fault diagnosis. To enhance the model generalization ability, a domain generation module is designed to generate fake target domains that have significant distribution discrepancies with the source domain. Then, an iterative min–max game of mutual information between the domain generation module and task diagnosis module is implemented to learn generalized features for resisting the unknown domain shift. Extensive diagnosis experiments conducted on two mechanical rigs validated the effectiveness of the proposed method.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

Image anomaly detection can automatically detect defects using images of products, which is crucial for product quality controls. Because of insufficient abnormal data, unsupervised image anomaly detection based on knowledge distillation has attracted broad attention recently. However, fully unsupervised methods suffer from detecting tiny anomalies that widely exist in industrial products because the features of tiny anomalies and normal features extracted by the teacher network are similar. This paper extends current unsupervised anomaly detection methods into a semi-supervised manner, simultaneously leveraging normal data and a limited amount of abnormal data. An automobile plastic parts dataset is established to prove the effectiveness of the proposed method. Experiments show that the proposed method can accurately detect small anomalies and largely surpass a powerful baseline (6% in AU-ROC, 10% in F1-score, 11% in Accuracy).

This paper investigates a longitudinal trajectory optimization problem of connected and automated vehicles with an energy-aware non-linear objective. In this paper, we first approximate each vehicle trajectory with a cubic spline function using the proposed solution representation scheme, while the curve shape can be controlled by the knot vectors. After that, we propose a new coevolutionary algorithm that decomposes the initially high-dimensional problem and performs as the optimizer for subproblems. In the local exploitation phase, a problem-specific steepest ascent hill-climbing algorithm is developed to escape from local minimum points and speed up convergences. This proposed approach is compared with several state-of-the-art algorithms in multiple scenarios with different traffic densities and platooning sizes. Simulation results indicate that it can yield near-optimal solutions with reasonable computation times for real-life applications.

Ultra-wideband (UWB) technology has been widely applied in the high-precision indoor positioning system. However, the complicated indoor environment makes signals propagate in non-line-of-sight (NLoS) situations, which seriously deteriorates the positioning accuracy. This work proposes an outlier-aware method to improve the positioning accuracy under NLoS scenarios. End-to-end optimization and positioning are achieved by combining the measurement error mitigation process with the positioning process. Experiments on public benchmarks illustrate that the proposed method enhances the performance of indoor positioning in NLoS situations.

Deep learning is an important method to deal with point cloud, but its ability is limited to extract local features of point cloud. Many deep learning networks are designed to capture the local information, but they ignore the importance of non-local features to the point cloud. This paper proposes an improved non-local neural networks for point cloud classification. The non-local module can extract local and non-local features of the point cloud simultaneously. The local information is obtained based on the feature distance between neighborhood points searched by k-nearest neighbor method. The extracted local features are integrated into the non-local network, which can capture non-local features from the entire point cloud. The designed non-local module can be easily inserted into the existing point cloud processing network. The proposed method is evaluated on well-known ModelNet40 shape classification benchmark. Experimental results show that the proposed method achieves a significant improvement in classification accuracy.

With the increasingly developed industry and more comprehensive product offerings, customized and personalized products (CPPs) gradually become a main business model of many enterprises. However, the characteristics of CPPs, such as large differences in product modules and short product delivery cycles, put forward very high demands for the intelligence, flexibility and real-time performance of cloud manufacturing (CMfg). To satisfy the above typical demands, a cloud-edge-device collaborative framework of CMfg is proposed to support distributed data processing and fast decision-making. In the context of Cloud-edge-device collaboration, the vertically and horizontally distributed deployment and update mechanisms of deep learning models (DLMs) are brought forward and analyzed in detail to provide rapid response and high-performance decision-making services for CPPs. In addition, related key technologies are presented to provide references for the technical research direction.

Job shop scheduling problem (JSSP) is a typical scheduling problem in manufacturing. Traditional scheduling methods fail to guarantee both efficiency and quality in complex and changeable production environments. This paper proposes an end-to-end deep reinforcement learning (DRL) method to address the JSSP. In order to improve the quality of solutions, a network model based on transformer and attention mechanism is constructed as the actor to enable a DRL agent to search in its solution space. The Proximal policy optimization (PPO) algorithm is utilized to train the network model to learn optimal scheduling policies. The trained model generates sequential decision actions as the scheduling solution. Numerical experiment results demonstrate the superiority and generality of the proposed method compared with other three classic heuristic rules.

We are pleased to present this Special Issue of TASE, including 12 extended articles selected from the technical program of the 2020 International Conference on Automation Science and Engineering (CASE2020). CASE2020 was held virtually due to the COVID19 pandemics, August 20–21, 2020, and was originally scheduled in Hong Kong, China. CASE is an offspring of TASE and is the flagship automation conference of the IEEE Robotics and Automation Society, constituting the primary forum for cross-industry and multidisciplinary research in automation. The 2020 CASE theme was Automation Analytics, a global challenge emphasized at the conference by several invited and regular sessions, as well as specific workshops.

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

Evolutionary algorithms and swarm intelligence algorithms have been widely used for constrained optimization problems for decades and numerous techniques for constraint handling have been proposed. The${\varepsilon }$-constrained method is a very effective one. In the literature, the${\varepsilon }$value was usually controlled via an exponential function, which is not competent for solving certain types of constrained optimization problems, e.g., whose global optima are located near the boundary of the feasible and infeasible regions. To solve this problem, this article proposes a new adaptive${\varepsilon }$control method and incorporate it into a basic differential evolution (DE) algorithm: (DE/rand/1/exp). Based on the information of constraint violation in the current population, the adaptive method controls the value of${\varepsilon }$through a simple heuristic rule. Compared with the traditional exponential function-based control methods, the proposed adaptive method can prevent the algorithm from being trapped into local optima while retaining the obtained near-optimal candidate solutions in the infeasible region for generating promising searching paths. Besides, we set the crossover rate (CR) as a more reasonable value for DE/rand/1/exp, which can enhance the efficiency significantly. The well-known 2006 IEEE Congress on Evolutionary Computation (CEC 2006) competition on real-parameter single-objective constrained optimization benchmark is adopted to evaluate the effectiveness of the proposed adaptive${\varepsilon }$-constrained DE. Fifteen constrained engineering optimization problems are collected from the literature to test the proposed algorithm. Moreover, the adaptive${\varepsilon }$control method is extended to an adaptive algorithm to solve the benchmark problems from CEC 2017. The comparison results confirm the superiority of the proposed method.

It is not easy to provide a thermal environment satisfying all occupants in a shared space owing to individual differences in thermal comfort. Existing research efforts focusing on resolving thermal conflicts in offices with multi-occupants rely on consensus-based temperature setpoints optimization, which causes unfairness among occupants. To cope with this drawback, we propose a method to assign occupants to cubicles where every occupant can enjoy their preferred thermal environment by utilizing the uneven temperature distribution in a shared space with air conditioners. The proposed method accounts for personal thermal comfort differences and uneven temperature distribution in an open office space, and maximizes the aggregate thermal comfort of occupants, by proposing a differential evolution algorithm for the cubicle assignment problem. An application of the proposed method under a space with 30 cubicles is analyzed by assignment simulation of 15 occupants to 30 cubicles. Results show that the proposed method can find every occupant a cubicle matching with their thermal preference among the available cubicles and generate an assignment solution that maximizes the sum of the thermal comfort probabilities of all the occupants. Our investigations demonstrate that cubicle assignment considering personal thermal preferences can discover the potential of current thermal environment for providing personalized thermal conditions for multi-occupants.

With rapid population growth and urbanization, municipal solid waste (MSW) generation rates are rising around the world. Incineration is considered as an effective way to deal with the growing demand for MSW disposal. The prediction of concentrations of pollutants generated from MSW incineration plants is a powerful support for waste incineration process control, which aims to reduce pollutant emissions. This paper proposes a two-stage prediction method based on the long short-term memory (LSTM) network to forecast typical flue gas pollutants of an MSW incineration plant in South China. In the first stage, an LSTM-based classification model is utilized to determine whether the pollutants are at a low level, and over-sampling strategy is applied to deal with the class-imbalance problem. At the second stage, an LSTM-based prediction model is built to further estimate the amounts of pollutants. Besides, the sliding average method is used to process the multi-scale raw data. Experiment results proved the effectiveness of the proposed method and indicated how different inputs affect the forecasting of pollutant emissions.

A centralized manufacturing environment is no longer sufficient to meet market demands. A large number of companies extend their production to a distributed environment. Distributed hybrid flowshop scheduling problem (DHFSP) has become a new research topic in recent years. In this paper, a modified genetic algorithm operator is proposed to change the solution structure and a local search method is improved by employing a new lower bound rule. Through experimental comparison, this algorithm has obvious advantages on the effectiveness of searching better solutions, and the new lower bound rule also reduces the running time of local search.

In wireless sensor network, since the sensor signal coverage is directly related to the optimization of sensor resources, it is one of the most basic problems. To find the minimum number of sensors needed for the deployment and optimize the positions of sensors, self-adaptive estimation particle swarm optimization (SEPSO) is adopted. In some scenes, sensors should be avoided being placed in the area where it is inconvenient to make deployment, and therefore in the process of searching for positions of sensors, a re-deployment method called supplementary boundary condition for SEPSO is proposed to deal with those errant sensors. Extensive experiment results showed that, compared with the virtual force approach, the proposed method can achieve better deployment with less computing time. When the number of sensors is small, the virtual force method performs well, but when the number of sensors increases gradually, the calculation time will increase significantly, but the result does not improve compared with PSO. Two application experiments were conducted in an office scene and in a forest park scene, and the results in both experiments validated the feasibility of the proposed method.

Today, manufacturing enterprises must respond quickly to the ever-changing market environment in order to survive. At the same time, dynamic disturbances in production sites such as machine failures, reworking caused by quality problems, and changes of processing time and personnel will also have negative impacts on the effectiveness of the original plan. In order to ensure the feasibility of scheduling algorithms in highly dynamic environment, this paper presents agent-based rescheduling negotiation mechanisms with five dynamic disturbances, and verifies its feasibility through simulations. A multi-agent system structure with quick responses and simple communication networks is designed according to characteristics of hybrid flow shops, with which the processing of jobs can be easily traced back.

In this paper, we develop a joint cooperative localization and synchronization scheme for dense mobile wireless sensor networks (WSNs) using a scalable belief propagation (BP) based method. We consider a distributed time-varying WSN with mobile devices, where message packets are propagated among all devices starting from temporal and spatial anchors. To account for the nonlinear system models and to compute the belief at each device while maintaining low communication and computation complexity, we propose an efficient scalable BP scheme, where a temporary posterior belief is calculated and updated sequentially so that the dimension of measurement covariance matrices is fixed instead of the unlimited dimension augmentation and batch computation in sigma point belief propagation (SPBP). Simulation results demonstrate a significant enhancement on the robustness of the algorithm and reduction of the computational complexity compared to the baseline scheme.

The 15th annual IEEE International Conference on Automation Science and Engineering (CASE 2019) was held on August 22–26, 2019, at The University of British Columbia, Vancouver, BC, Canada. IEEE CASE represents the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners.

Fault detection, diagnostics, and prognostics (FDD&P) ensure the operation efficiency and safety of engineering systems. In the building domain, they can help significantly reduce energy consumption and improve occupant comfort. Specifically, prognostics are becoming increasingly important as a pro-active fault prevention strategy through continuously monitoring the health of energy systems. In this article, we develop a machine learning-based method for building systems. The proposed method can help develop predictive models from historical operation and maintenance data. After the detailed description of the proposed machine learning-based prognostic method, a case study involving prognostics on central heating and cooling plant (CHCP) equipment is provided. To this end, a year's worth of sensor and actuator data from four boilers and five chillers of a CHCP in Ottawa, Canada are collected. The plant operators are interviewed to understand how they handle failure events, and their logbooks are reviewed to extract the date and time of the recorded failure events. The sensor and actuator data up to two weeks prior to each of these failure events are used to develop regression tree models that predict time to failure (TTF). The results indicate that about half of the modeled failure events could be accurately predicted by looking at the data available in the distributed control system. Finally, the future work is outlined.

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.

Surrogate-assisted multi-objective evolutionary algorithms have been commonly used to solve multi-objective expensive problems. In this paper, we investigate whether the surrogate-assisted offspring generation method can improve the optimization efficiency of multi-objective evolutionary algorithms. We first construct a surrogate model for each objective function. After that, some candidate solutions from the surrogate models are used to produce promising offspring for the multi-objective evolutionary algorithm. In addition, a prescreening criterion based on reference vectors and the nondominated rank is used to select the surviving offspring and exactly evaluated individuals. The pre-screening criterion can ensure the diversity and convergence of the offspring, and reduce function evaluations. Benchmark problems with their dimensions varying from 8 to 30 are used to test the effects of the surrogate-assisted offspring generation method under the framework of using the pre-screening criterion. Experimental results show that using the candidate solutions from surrogate models can enhance the performance of its basic algorithm on most of the problems.

Industrial trends and new generation information and communication technologies have become driving forces for advancement in the process control and manufacturing industry. This paper thoroughly investigates the future industrial trends from the perspectives of market, engineering system, product, innovation, etc., then incorporates the concept of software defined networking and proposes a new cloud based manufacturing model, Software Defined Cloud Manufacturing (SDCM). The key characteristics, reference architecture and emerging enabling technologies of SDCM are presented to support the SDCM's advantages in terms of real-time response, reconfiguration and operations of the manufacturing system. Resource virtualization and function programmability lie at the core of SDCM to empower the manufacturing sector. The paper is concluded with remarks and future work.

Metadata in most existing building automation systems (BASs) is inconsistent, incomplete, and nondescriptive. This situation is a major obstacle to the widespread use of data analytics to improve the operation of buildings. In this article, we put forward a method to infer zone-level metadata from features derived from BAS data. The method includes two steps: 1) classification of BAS points into different types (e.g., indoor temperature, indoor temperature set point, airflow, airflow set point, damper position, and radiator valve position) and 2) association of BAS points based on their functional relationships (i.e., grouping the sensors, actuators, and set points of each zone together). The metadata inference method was demonstrated with data from zones served by four different air handling units (AHUs) in two office buildings in Ottawa, ON, Canada. The results from this case study indicate that common zone-level BAS point types can be accurately classified and associated even in the absence of intuitive data labels. Note to Practitioners-This article was motivated by the problem of metadata normalization in existing buildings, in order to scale up the application of smart building solutions in the real world. Existing metadata normalization approaches mainly focused on inferring the point types of the metadata with both semantic (label) and numerical information (time series readings). In this article, we put forward a method to infer zone-level metadata with numerical information only. Methods for both types of classification and relationships' association of the BAS points are investigated. The results from two office buildings indicate that the classification phase can achieve an average of 90% accuracy, while the association phase can obtain an average of 85% accuracy. The method was developed and demonstrated with a limited data set by using data exclusively from zone-level sensors, actuators, and set points. Future work is planned to extend the proposed method to more comprehensive BAS data sets with the system- and plant-level data as well.

Most industrial systems frequently switch their operation modes due to various factors, such as the changing of raw materials, static parameter setpoints, and market demands. To guarantee stable and reliable operation of complex industrial processes under different operation modes, the monitoring strategy has to adapt different operation modes. In addition, different operation modes usually have some common patterns. To address these needs, this article proposes a structure dictionary learning-based method for multimode process monitoring. In order to validate the proposed approach, extensive experiments were conducted on a numerical simulation case, a continuous stirred tank heater (CSTH) process, and an industrial aluminum electrolysis process, in comparison with several stateof-the-art methods. The results show that the proposed method performs better than other conventional methods. Compared with conventional methods, the proposed approach overcomes the assumption that each operation mode of industrial processes should be modeled separately. Therefore, it can effectively detect faulty states. It is worth to mention that the proposed method can not only detect the faulty of the data but also classify the modes of normal data to obtain the operation conditions so as to adopt an appropriate control strategy.

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes' impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.

With the advance of the information and communication technology, smart grid, and smart metering, residential electricity usage data are available for analyzing household usage pattern. However, most such usage pattern analyses have been based on smart meter data. Geographical location and environment factors have not been well considered. In order to have a better understanding of residential electricity usage pattern, this paper studies the usage pattern based on both environment data and smart meter data. A Hidden Markov Model (HMM) is applied to learn the consumption dynamic behavior under the corresponding environments and a clustering method is applied to discover the typical usage patterns. The environmental adaptation which indicates the household reaction to the environment during the electricity consumption is revealed.

The ubiquity of smartphones creates great opportunities for participatory sensing, where people can implicitly contribute observations about their local environments through sensors such as cameras and accelerometers. The collected data can then be aggregated and used to benefit the crowd in some way. In this paper, we report on the current development of Sherlock, a device capable of automatically detecting the presence of people in localities through evidences left by smartphones called probe requests, without any user intervention. To validate the proposed mechanism implemented in the device, we performed an experiment with ten participants in six rounds where it was possible to automatically detect 41 presence events, of which 66% could be detected within less than 30s. The implicit crowdsourcing mechanism behind this approach may allow real-time monitoring of people flows in public environments, which can enable, among other things, energy systems to be automatically orchestrated according to demand, reducing associated costs.

The seven papers in this special section focus on the research and development of service-oriented collaborative computing technologies and their applications to the design of products, processes, systems and services in an industrial and social viewpoint.

The future Internet of Things (IoT) is expected to enable a new and wide range of decentralized systems (from small-scale smart homes to large-scale smart cities) in which “things” are able to sense/actuate, compute, and communicate, and thus play a central and crucial role. The growing importance of such novel networked cyber-physical context demands suitable and effective computing paradigms to fulfill the various requirements of IoT systems engineering. In this paper, we propose to explore an agent-based computing paradigm to support IoT systems analysis, design, and implementation. The synergic meeting of agents with IoT makes it possible to develop smart and dynamic IoT systems of diverse scales. Our agent-oriented approach is specifically based on the agent-based cooperating smart object (ACOSO) methodology and on the related ACOSO middleware: they provide effective agent design and programming models along with efficient tools for the actual construction of an IoT system in terms of a multiagent system. A case study concerning the development of a complex IoT system, namely a Smart University Campus, is described to show the effectiveness and efficiency of the proposed approach.

Accurate state-of-charge (SOC) estimation is essential to battery management system. The widely adopted estimation methods based on Kalman Filter (KF) fail to take the variable environmental conditions into consideration, which may result in a poor accuracy. This paper proposes a novel estimation model based on KF method to estimate SOC of Lithium-ion battery. In the proposed model, the noise variances are optimized for the system current state at each iteration, a variable forgetting factor is introduced to improve the algorithm's convergence and accuracy of estimation, and the artificial neural network (ANN) is applied for the measurement equation of KF. The experiments, based on Lithium-ion Battery set of NASA, show that the proposed SOC estimation model is valid and can improve the algorithm performance and accuracy and robustness.

Fault Detection, Diagnostics and Prognostics (FDD&P) is attracting a lot of attention from building operators and researchers because it can help greatly improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while improving occupant comfort at the same time. However, FDD&P for building operations remains with many challenges due to special operation environments of HVAC systems. These challenges include `tolerance or ignorance' of failures in long-haul operations, lack of operation regulations, and even lack of documents for HVAC failure mode and effect analysis (FMEA), which is a systematic method of identifying and preventing system, product and process problems. To address some of these challenges, we propose to develop a FMEA for HVAC by exploring work orders generated by building energy management systems (BEMS) using a data mining approach. With the developed HVAC FMEA, it is possible to conduct pre-FDD&P procedures to improve HVAC maintenance and to select the high impact failures in order to acquire the operation data for selected failures and develop machine learning-based predictive models to predict a failure before it occurs and isolate the root component of a given failure. In this paper we report some preliminary results in developing an HVAC FMEA tool from a large number of work orders obtained from a BEMS in routine operations. The developed HVAC FMEA will be used as a guidance tool for data gathering and developing data-driven models for building HVAC FDD&P.

This paper presents a case study on the deployment of smart building monitoring and ongoing commissioning in Canadian federal government office buildings. The case study involved four office buildings with a total rentable space of about 100,000 m2. Based on the measurement and verification results over a reporting period of 12 to 24 months, the four pilot buildings have demonstrated an average energy saving of 15%, which resulted in significant energy cost savings of about $818,000 and greenhouse gas (GHG) emission reductions of about 660 tons. The mechanism by which the savings are currently achieved in the pilot buildings is through the handling of the work orders generated by the deployed building energy management systems. These work orders are based on the detected faults, anomalies or inappropriate operations of the building heating, ventilation, and air conditioning (HVAC) system. Further investigation is underway on the optimization of the HVAC controls according to building occupancy and weather conditions, and on the integration of HVAC and lighting controls.

Link prediction plays an important role in complex network analysis. It is to predict the existence of an unknown link or a future link in a network. Classical methods for link prediction evaluate the similarity of vertices based on common neighbors, and denote that every common neighbor makes equal contribution to the connection likelihood. However, common neighbors may play different roles depending on whether they belong to the same community, where vertices are densely or sparsely connected to other communities. This paper proposes a novel similarity index for link prediction which combines the topology information and community information. The proposed approach is compared with ten classical local similarity indices on ten real-world networks. The experiment results shown that the proposed approach can improve the accuracy of link prediction no matter which community detection algorithm is used.

Due to the sustained and rapid growth of information and communication technology (ICT) and automation techniques, smart factories for high-end equipment manufacturing involve extensive collaborative networks and knowledge sets. Conventional manufacturing modes are undergoing profound reforms in the Internet and Big Data era, and operations management of such factories should lay more emphasis on service values. To this end, the current manufacturing modes and operations management strategies are fully investigated in this paper. A CPSS (cyber-physical-social system)-based manufacturing mode of smart factories for high-end equipment manufacturing is put forward. The connotation of operations management of smart factory is extended based on the introduction of service value and value chain, thus contributing to a win-win situation of an enterprise and its customers. Furthermore, a multi-participation Blockchain-based collaborative manufacturing model for smart factories is presented.

Location based services are the hottest applications on mobile device nowadays. Indoor wireless position is the key technology to enable location based service to work well indoors, where Global Position System normally couldn't work. The main tendency of indoor wireless position is based on Bluetooth and RSSI (radio signal strength indicator). RSSI is the key parameter for wireless position. But values of RSSI are affected by environment factors easily. Because of this reason, results got from the indoor location technology are usually imprecise and unacceptable. In this paper, an adaptive algorithm based on Distance-Loss model in complex environment is introduced to deal with such problems. The algorithm makes the model adapt to the environment by several parameters which are not influenced by environment. The stability and the accuracy of the algorithm is evidenced by a series of strict experiences

The Asymmetric information among different entities can contribute to the imbalance between supply and demand in agricultural supply chain. Negotiation is an effective method to address information symmetry. Many researchers can be found in the literature on agent-based negotiation in industrial supply chain, but very few in agricultural supply chain, primarily because of the interior instability. This paper presents an agent-based negotiation framework for agricultural supply chain to address the information symmetry by introducing the third party logistics. The third party logistics, with more functions than a traditional broker, integrates logistics services and intermediary services to guarantee the relative stability in dynamic agricultural environments. A negotiation interaction process is also designed to facilitate the interaction among these agents. Finally, a case is used to validate the proposed framework.

E-business based maintenance, repair and overhaul (E-MRO) is a new MRO service mode. Although in real world there are a number of E-MRO prototype systems, few comprehensive studies have been conducted on this topic. Motivated by the challenges of making optimal E-MRO service planning, simultaneously considering the capacity constraints of MRO service providers and the maintenance constraints of equipment users, this paper proposes a stochastic programming model involving multi-choice parameters, where uncertain factors in E-MRO are quantified. To solve the model, the properties of expectation of a random variable, and the Lagrange interpolating polynomial approach are used to derive the deterministic model equivalent to the stochastic programming model. The objective of the model is to seek optimal service planning, including determining whether to configure the corresponding service from the corresponding provider to the corresponding user at the corresponding period, and determining the time of the corresponding service. The optimal service planning can be referred by practitioners for a more reasonable decision. A numerical example validated the feasibility of proposed model.

It is a great pleasure to welcome you to the 2016 IEEE 20thInternational Conference on Computer Supported Cooperative Work in Design (CSCWD 2016), which takes place at Qianhu Hotel, Nanchang, China, from May 4thto 6th, 2016.

The key to saving energy in commercial buildings is to deliver building services only when and where they are needed, in the amount that they are needed. Given that building services are usually employed to provide occupants with satisfactory indoor conditions, it is therefore important to accurately detect the occupancy of building spaces in real time. This paper starts with some discussion on building occupancy resolution and accuracy as well as a brief introduction to traditional explicit occupancy detection approaches. The focus of this paper is on the review and classification of emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, sometimes referred to as implicit / ambient / soft sensing approaches. About 40 related projects / systems are reviewed and compared in terms of occupancy sensing type, occupancy resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies. It also briefly discusses technology trends, research challenges, and future directions.

A recent literature review shows that approximately 20-50% of energy/cost savings are possible in office buildings when accurate occupancy information is applied to the control of building energy systems. Implicit occupancy sensing, by extracting occupancy data from systems already in the building rather than from those explicitly designed to collect occupancy information, has the potential to provide high-enough accuracy for building energy management with lower costs compared to traditional explicit sensing approaches. Since more and more office workers today carry smart phones, we conducted a proof-of-concept study to explore the feasibility of using smart phone Bluetooth signals for office occupancy detection. The objective is to use existing IT infrastructure to detect occupancy to enhance building control functions while protecting office worker privacy. This paper presents some preliminary results of our recent investigation in this direction. The experimental results are very promising.

In order to make the smart home system to have the ability of learning user behavior actively and provide services spontaneously, this paper introduced user behavior prediction model which combined back propagation neural network (BPNN) with Hadoop parallel computing to the traditional smart home system, numerous user-generated behavior and environmental parameters data are packaged in particular data frame format and uploaded to the cloud platform through 4G or WLAN by the home gateway. According to the received historical data, repeated parallel training of BPNN which run on cloud platform was utilized to achieve user behavior prediction. Case study on smart home validated that the proposed model is valid for user behavior prediction with accuracy elevated, it can help user to complete equipment operating independently in the corresponding cases. Another comparison, time efficiency experiment on the parallelized neural network algorithm also showed that the suggested method is excellent in convergence speed and accuracy.

The Internet of Things (IoT) envisions the seamless interconnection of the physical world and the cyber space. This provides a promising opportunity to build powerful services and applications for manufacturing. This paper provides an overview of key research issues to be addressed and the latest advances in the area of IoT-enabled manufacturing. We first introduce the core technologies of IoT, such as Radio Frequency Identification, Wireless Sensor Networks, Cloud computing, and Big Data. Then we discuss some key research issues of IoT-enabled manufacturing in term of architecture, deployment and business model, data acquisition and processing, model-based decision-making, dynamic service composition, user-centric pervasive environment and latency reduction with state-of-the-art reviews. Finally, we point out some potential application areas of IoT in manufacturing.

Cloud Manufacturing can provide mass manufacturing resources and capabilities as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, etc. The ability to leverage ample services hosted in MCs has direct relation to the success or failure of a manufacturer. Meanwhile, various uncertainties in today's highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules ineffective or even obsolete. IoT's real-time sensing ability can be used to detect those uncertainties. However, little work has been done to take advantage of abundant services from MCs and to effectively deal with uncertainties. In order to address this issue, we propose a mathematical formulation for IoT-enabled dynamic Service Selection (SS) across multiple MCs. We consider three kinds of uncertainties (fluctuation of completion time, choices of manufacturing services, and runtime changes made by users) that come from both the user and market sides. The formulation can guide the dynamic SS and enable users to continuously adjust SS to be more effective and efficient.

This paper presents our recent work on human activity detection based on smart phone sensors and incremental clustering algorithms. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used in order to generate pre-clustered results for the next stage. In the second stage, pre-clustered results will be refined to form the final clusters, which means the clusters are built incrementally adding one cluster at a time. Experiments on phone sensors data of five basic human activities show that the proposed scheme could get comparable results with traditional clustering algorithms but working in a streaming and incremental manner, which is promising for automatic annotated data collection.

Complex structural parts are pervasive and playing an important role in the aircraft manufacturing area. In order to improve the machining efficiency, the cutting parameter optimization of complex structural parts during the machining has always been a problem in manufacturing industry. At present, the cutting parameters are usually optimized based on the final state of complex structural parts and remain unchanged during the machining process, which may not consider the cutting parameter optimization of workpiece in the intermediate machining process. Thus, a cutting parameter optimization method based on dynamic machining features for complex structural parts is proposed to improve the machining efficiency and guarantee the product quality during the machining process. The interim geometric state of each machining occasion is constructed in order to analyze the chatter stability. Then, the cutting parameters are optimized using a genetic algorithm within the limits of chatter stability.

Motivated by the challenges of seeking the optimal E-business based maintenance, repair and overhaul (E-MRO) service policy, simultaneously considering bilateral requirements of quality of service (QoS), this paper presents a mathematical model based on variable fuzzy recognition and multi-objective programming. Cloud model is utilized to quantify the information of bilateral requirements as the numerical values. Then, the comprehensive satisfaction of multiple attribute is calculated by using variable fuzzy recognition method. Based on bilateral satisfactions, a multi-objective programming model is formulated, where bilateral QoS satisfactions are modeled as objective functions. By using global criteria method, the multi-objective optimization is transformed to an equivalent single objective optimization, which can be solved by LINGO. Finally, the optimal E-MRO service policy satisfying bilateral requirements is obtained. A case study illustrated the feasibility and efficiency of the proposed model.

In order to improve fault forecasting model accuracy of back propagation neural network (BPNN), an improved prediction method of optimized BPNN based on Multilevel Genetic Algorithm (MGA) was proposed. We design new chromosome with multilevel structure, improve the encoding mode, fitness function and genetic operator. Which can optimizes the initial values of weights, thresholds and the structure of BPNN synchronously. Enhancing the ability of nonlinear learning and generalization of BPNN. Case study of continuous casting equipment verified that the proposed model with higher prediction accuracy is better than classical BPNN and GA-BPNN prediction method for fault prediction.

Customized/personalized products are gaining more shares in today's product market. Such products need collective efforts from consumers, manufacturers and third parties. On the other side, the Internet of Things (IoT) with pervasive sensing/actuating/networking ability greatly facilitates remote operation of manufacturing activities and efficient collaboration among stakeholders. This provides great opportunities to the above demand. Thus we propose a full-connection model of product lifecycle in the IoT-enabled cloud manufacturing environment. The model uses social networks to connect multiple parties and facilitate open innovations, IoT to glue physical space to cyber space and cloud manufacturing to provide various elastic services, so that the on-demand workspace, interaction, information sharing or collective problem solving are enabled. We also propose a supporting infrastructure for this model using the latest information and communication technologies. Finally, we present a RFID (Radio-frequency identification) enabled production system for customized/personalized products with the ability to enable a new paradigm of “dynamic processes and close collaborations among different roles” and secure robust production.

This paper presents our recent work on the analyses of smart phone sensor data collected for the human activity recognition (HAR), with the objective to develop more accurate activity recognition systems independent of smart phone models. We identify the multi-device scenario and present the impairments of different smartphone embedded sensor models on HAR applications. Outlier removal, interpolation, and filters in the preprocessing stage are proposed as mitigating techniques. Based on datasets collected from four distinct smartphones, the proposed mitigating methods show positive effects on 10-fold cross validation, device-to-device validation, and leave-one-out validation. Improved performance for smartphone based human activity recognition is observed.

Monitoring solutions using the Internet of Things (IoT) techniques, can continuously gather sensory data, such as temperature and pressure, and provide abundant information for a monitoring center. Nevertheless, the heterogeneous and massive data bring significant challenges to real-time monitoring and decision making, particularly in time-sensitive industrial environments. This paper presents an online monitoring system based on an IoT system architecture which is composed of four layers: 1) sensing; 2) network; 3) service resource; and 4) application layers. It integrates various data processing techniques including protocol conversion, data filtering, and data conversion. The proposed system has been implemented and demonstrated through a real continuous steel casting production line, and integrated with the TeamCenter platform. Results indicate that the proposed solution well addresses the challenge of heterogeneous data and multiple communication protocols in real-world industrial environments.

In 1999, Kevin Ashton envisioned a novel paradigm named Internet of Things (IoT), in which all things could see, hear, and smell the world for themselves, and interact with each other and cooperate with their neighbors to reach some common desired goals. In the following years, the IoT ideas started to spread rapidly due to the technology advancements in the fields of microelectromechanical systems and most recently, nanoelectromechanical systems, computers, and wireless communications, resulting in autonomous everyday thing augmented with sensing/actuation, storage, processing, and network capabilities. Their new applications emerged daily from smart homes to smart cities, from automobiles to high-speed trains, from new-born care devices to patient operating rooms and entire hospitals, and from manufacturing factories to agricultural food plants. IoT is one of the fastest growing technical areas across almost all engineering disciplines and touches almost all verticals of the World Economy. It represents major investments in commercial and government initiatives. We expect to have over 40% Compound Annual Growth Rate year over year in the commercial marketplace and to dominate “traffic” on the Internet within the next decade.

Frequent itemset mining is an important step of association rules mining. Traditional frequent itemset mining algorithms have certain limitations. For example Apriori algorithm has to scan the input data repeatedly, which leads to high I/O load and low performance, and the FP-Growth algorithm is limited by the capacity of computer's inner stores because it needs to build a FP-tree and mine frequent itemset on the basis of the FP-tree in memory. With the coming of the Big Data era, these limitations are becoming more prominent when confronted with mining large-scale data. In this paper, DPBM, a distributed matrix-based pruning algorithm based on Spark, is proposed to deal with frequent itemset mining. DPBM can greatly reduce the amount of candidate itemset by introducing a novel pruning technique for matrix-based frequent itemset mining algorithm, an improved Apriori algorithm which only needs to scan the input data once. In addition, each computer node reduces greatly the memory usage by implementing DPBM under a latest distributed environment-Spark, which is a lightning-fast distributed computing. The experimental results show that DPBM have better performance than MapReduce-based algorithms for frequent itemset mining in terms of speed and scalability.

This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent as needed. Embedded sensors including a tri-axial accelerometer, tri-axial linear accelerometer, gyroscope sensor, and orientation sensors are used for motion data collection. A two-stage data analysis approach is used for prediction model generation: short period statistical analysis (max, min, mean, and standard deviation) and long period data analysis using machine learning. The system is implemented in an Android smart phone platform.

Communication technologies, involving fieldbus network, Wireless Sensor Network (WSN) and industrial Ethernet, are mainly applied to complex industrial applications like continuous casting field, in order to transmit information. However, there're still some shortcomings exposed and certain higher requirements such as compatibility, expansibility, and transmission distance and speed have been put forward. Accordingly, this paper proposed a design scheme of embedded multi-protocol gateway with wire and wireless communication methods integrated. A communication model of embedded multi-protocol gateway is established, taking example by the principle of protocol conversion and the architecture of heterogeneous network integration based on Open System Interconnection Reference Model (OSI/RM). In addition, the intercommunication of such a network combining WSN, CAN bus, 3G network, WLAN, and Ethernet is realized, taking data heterogeneous and command message conflicting into consideration. Moreover, the use of modular and hierarchical design method made it possible for subnet communication interface to expand to different monitoring equipment and data acquisition equipment. The proposed model provides solution of heterogeneous network integration and real-time data service of high speed and wide coverage for the MRO online monitoring system.

In the research area of community detection which aims at detecting some highly cohesive vertex subsets in social network, there mainly exist some problems, such as the algorithms with comparatively excellent quality of the final partitioning usually have high time complexity and some other fast algorithms often result in low quality of partitioning or other disadvantages. Nowadays, the increasing demands for community detection in large-scale social networks necessitate the use of distributed and scalable methods to detect communities in an effective and efficient manner. Label propagation algorithm (LPA), whose time complexity is O (m) on a network with m edges, is a near linear time algorithm to detect community effectively. Besides, owing to having good scalability, the parallel version of LPA (DLPA) is suitable for community detection in large-scale social networks. However, DLPA synchronously updates the vertices labels, which usually brings about label oscillations and results in low quality of partitioning. In this paper, we analyze the drawbacks of DLPA and propose a novel method C-DLPA, which combines DLPA with the notion of maximal cliques and at the same time utilizes a new updating mechanism that updating each node' label by probability of its adjacent nodes, to make final partitioning become more accurate and to avoid oscillations effectively. The experimental results show that C-DLPA has better performance is not only low time cost by as much to avoid oscillations but its community detection accuracy compared with DLPA.

Existing scheduling and decision solutions to container terminal logistics systems (CTLS) are falling short of peoples' wishes both in theory and in practice. This paper proposes a container terminal gang scheduling computational paradigm (GSCP) for planning and control at container terminals based on computational thinking. GSCP is built on the computing perspective and architecture of multi-processor system-on-chip. GSCP applies principles of pervasive computing to evaluate load conditions of core resources, and then integrates the fundamental principles of gang scheduling, processor affinity and load balancing to define the flexible and robust decision framework and scheduling algorithm set. Those are intended to improve the performance of CTLS, and strive for balance among through capacity for vessels and containers, task latency and load leveling as well. GSCP is demonstrated and validated by a typical container hub logistics service case with intensive computational experiments.

The production of aircraft structure parts is featured by multiple varieties and small batches, which imposes significant challenges for the representation of process knowledge. Feature based method is an effective way as the process knowledge carrier. Although the parts are different from each other, they are composed of similar geometric features with similar machining processes. This paper introduces the concept of "dynamic machining feature" which is formed in the machining process and influenced by various real operations. In this paper, the process knowledge and interim geometric information are associated based on dynamic machining features. An ontology-based method has been adopted to represent relevant information of dynamic machining features. The proposed approach can speed up process decision and facilitate process optimization.

In recent years? the study of social network is raising more and more attentions of researchers, locating the key nodes in social network is a hot research point. Lots of papers about how to discover the key nodes in social network such as mail network, micro log network was published. However, few people study on key nodes discovery in logistics network. In addition, most of methods of key nodes discovery only take relationship strength between nodes into account, few take the weight of node into account. In this paper, a node activity degree[1] based on users behavior features was defined, As a result, the logistics networks can be considered as a double-weighted networks by taking relationship strength as edge's weight and node activity as node weight. Based on Page Rank algorithm, an improved algorithms was proposed in this paper. The nodes weights were used as damping coefficient, and weight of the edges was used to compute importance of nodes during iterative process. At last? we implemented the improved Page Rank algorithm[2] using MapReduce. One dataset from a logistics company were selected and comprehensive experiments were conducted. The experimental results show that proposed algorithms can effectively and efficiently discover key nodes in real logistics network.

Cloud Manufacturing (CMfg) adopts and extends the concept of cloud computing to make mass Manufacturing Resources and Capabilities (MR/Cs) more widely integrated and accessible to users through the Internet. However, a single manufacturing cloud (MC) only has relatively limited scalability and elasticity. Using the aggregated MR/Cs from multiple MCs is a natural evolution. To address this requirement, we propose an integration framework for multiple MCs, so that MCs can collaboratively cope with peak user demands for MR/Cs. The key functional modules and the business model of the proposed framework are presented to guide future integration of multiple MCs. The enabling technologies, such as semantic web and ontologies, intelligent agents, service oriented architecture, and material handling and logistics technologies are also discussed. An application example is given, showing the feasibility and rationality of the proposed approach.

Link prediction in network attempts to predict the exist-yet-unknown links or future links in accordance with the node properties and the network typology. It has been used in many domains such as social network, biology experiment, and criminal investigations. Classical methods are based on graph topology structure and path features but few consider clustering information. Actually, clustering information plays an important role in link prediction, it connects the sparse nodes and other communities to form intensive communities. Besides the application of clustering, the MapReduce-based method is used to improve the efficiency. The validity of algorithm is verified by real-world datasets. The experimental results show that the proposed algorithm has a higher prediction accuracy and lower time complexity, and is more scalable than traditional methods executed by a single machine.

Small batch and multiple variety production mode and changing machining conditions call for dynamic inspection to guarantee machining quality. Dynamic inspection by considering real time monitoring information is a promising approach, but there is no available technology for integrating real time monitoring and inspection. To address this issue, this paper proposes a method of integrating monitoring and inspection based on intelligent software agents and manufacturing features. An agent-based approach is applied to develop an integrated framework, while manufacturing features are used as the information carrier to represent and connect monitoring and inspection information. Dynamic inspection is triggered according to the analysis results of real time monitoring. A prototype system has been developed to implement and validate the proposed method.

With the arrival of the big data era, information privacy and security issues become even more crucial. The Mining Associations with Secrecy Konstraints (MASK) algorithm and its improved versions were proposed as data mining approaches for privacy preserving association rules. The MASK algorithm only adopts a data perturbation strategy, which leads to a low privacy-preserving degree. Moreover, it is difficult to apply the MASK algorithm into practices because of its long execution time. This paper proposes a new algorithm based on data perturbation and query restriction (DPQR) to improve the privacy-preserving degree by multi-parameters perturbation. In order to improve the time-efficiency, the calculation to obtain an inverse matrix is simplified by dividing the matrix into blocks; meanwhile, a further optimization is provided to reduce the number of scanning database by set theory. Both theoretical analyses and experiment results prove that the proposed DPQR algorithm has better performance.

With the coming of the Big Data era, data mining has been confronted with new opportunities and challenges. Some limitations are exposed when traditional association rule mining algorithms are used to deal with large-scale data. In the Apriori algorithm, scanning the external storage repeatedly leads to high I/O load and brings about low performance. As for FP-Growth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. What's more, although remarkable achievements have been scored, there are still problems in dynamic scenarios. The paper presents a parallelized incremental FP-Growth mining strategy based on MapReduce, which aims to process large-scale data. The proposed incremental algorithm realizes effective data mining when threshold value and original database change at the same time. This novel algorithm is implemented on Hadoop and shows great advantages according to the experimental results.

With more and more applications of Internet of things (IoT) technologies, the quality of life of residents is one of the most important aspects in smart cities. Specially, home healthcare monitoring for the disabled and / or the elderly has become a focus of recent researches and developments. Existing home healthcare systems have drawbacks such as simple and few functionalities, weak interaction and poor mobility. This paper presents a home mobile healthcare (mHealth) system for wheelchair users, based on the emerging IoT technologies. The paper focuses on the proposed system architecture and the design of wireless body sensor networks (WBSNs). The nodes of WBSNs include wireless heart rate and ECG sensors, wireless pressure detecting cushion, home environment sensing nodes and control actuators. A prototype system implementation shows that the proposed people-centric sensing system is efficient in monitoring human activities and in interacting with the living environment.

Intents is an emerging framework which is employed for service discovery and integration. Currently the main strategy applied in Intents for resolving an intent message is exactly matching which may miss some valuable service candidates for the user. In order to address this issue, techniques in Information Retrieval (IR) are potential alternatives to find the missing services. This paper makes an empirical study of some classic IR techniques on a practical Intents dataset and demonstrates the findings of interest from the experiments which can be employed in designing matching schemes based on similarity.

Social network is a social structure of nodes that are linked by various kinds of relationships, such as friends, web links, etc. To extract social relation based on logistics data will contribute significantly to detect some underlying crimes. One of the main difficulties in social relation extraction from massive data is the low time efficiency. Fortunately, large scale parallel computation has been proved that it has an excellent capacity to cope with big data. In this paper, a MapReduce-based method was applied for extraction of social relation from logistics network using Hadoop platform. Experimental results showed that the proposed method improves the time efficiency well, and has more excellent scalability than traditional methods executed by a single machine.

A continuous monitoring system with high reliability is significantly important for complex equipment which is usually expensive, large-scale and sophisticated. Once a failure happens, it brings about not only serious economic losses, but also potential security hazards. In order to overcome outage damage caused by temporary failure and ensure excellent operation of the equipment, this paper presented an effective prediction model which combined the back propagation neural network (BPNN) with multi-agent cooperation grouping algorithm. The values of weights and thresholds of BPNN were obtained through optimization results of the multi-agent cooperation grouping algorithm. Based on above initialization parameters which met corresponding demands, repeated BPNN training was utilized to forecast fault. Case study on continuous casting equipment validated that the proposed model is valid for failure prognosis with forecasting accuracy elevated, compared with classical BPNN prediction method. Another comparison, function approximation experiment on the basis of a benchmark function, also showed that the suggested method is superior to BPNN in convergence speed.

During the past decade, marine environment monitoring has attracted more and more researchers around the world and various marine environment monitoring systems have been developed. Traditionally, an oceanographic research vessel is used to monitor marine environments, which is very expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as a promising solution for this purpose since they have a number of advantages such as easy deployment, unmanned operation, real-time monitoring, and relatively low cost. This paper first describes a common architecture of WSN-based oceanographic monitoring systems and a general architecture of an oceanographic sensor node. Then, it presents a detailed review of some related projects, systems, and technologies. It also highlights major challenges and research opportunities on the development and deployment of wireless sensor networks for marine environment monitoring.

In the Grid computing environment, computation, services, and storage belong to different organizations or individuals with different objectives. Entities in this domain are autonomous and self-interested; however, they are willingly to share their resources to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements that need to achieve. This work analysis the environment structure for the Grid, proposes a bidding language that is expressive and a winner determination algorithm that is adequate for the Grid computing environment.

The emerging dynamics of the digital e-markets are creating many opportunities with the vast growth and potential of online services and mobile technology. However, the sustainability of e-markets is uncertain due to industry and operational risks. While industry threats extend to the rapidly shifting powers, fuzzy dynamics, and fierce rivalry, this work examines an overlooked operational risk that relates to the fact present e-markets often constrain e-traders from strategic conduct. Such denial incites adverse reactions that cause e-market failures. In contrast, Smith invisible hand realizes the efficiency of the flexible strategic choice in free markets. Conveying strategies as rules may, also, accelerate the bidding lifecycles due to the automatic preference deduction of rules by the smart exchange. This work presents the RBBL rule based bidding language that enables free expressions of strategic rules in the bid structure, while proposing the GSPM generalized second price truthful matching double auction that computes stable, efficient and tractable outcomes with market profitability. The introduced smart exchange deliberates on the RBBL rules for automatic preference deduction while using the GSPM for winner determination, hence, improves sustainability with the rapid and stable e-trades, social efficiency, and self-prosperity of free choice.

Welcome to the 2013 IEEE 17th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD 2013).

The evacuation path optimization in the disaster area plays an important role in reducing the human and social harm and saving aid time. In this paper, a novel algorithm for emergency evacuation path choice based on quantum ant colony algorithm (QACA) is proposed, and it avoids premature convergence and speeds up the convergence to the global optimal solution. In the proposed algorithm, Q-bit is used to represent the pheromone, and the rotation gate is used to update the pheromone. Simulation results show that the proposed algorithm is feasible and effective.

Service discovery and integration has been a very active research field attracting many researchers around the world. Current technologies in service discovery like WSDL and UDDI have deficiencies which prevent their wide applications, especially outside of enterprise areas. Intents is an emerging technology aimed at interconnecting various services. Unlike its counterparts, services in Intents are single topic and given more informative descriptions. This paper examines the essentials of service discovery and integration applied in Intents, presents an architecture of Intents-based systems, and discusses the advantages of Intents over other existing technologies.

Intents is an emerging technology aimed at interconnecting services. In Intents a user agent is the most critical component which is responsible for intent resolution, service registration, candidate list generation and task completion. However, currently there is very little support or implementation for Intents user agent, especially on mobile devices. This paper presents the design and implementation of a prototype for Intents user agent. Third-party use cases are used for the validation of the proposed approach.

Online monitoring system for continuous casting equipment is established based on IOT (Internet of things) sensing technology and communication technology. As the system contains a variety of sensor types and data transmission protocols, it will lead to a large amount of heterogeneous data and the data is difficult to integrate with applications in upper layer. A data processing framework is introduced into the system to deal with such problems. The framework focuses on protocol conversion, data processing methods and integration with applications in upper layer. Finally, application in online monitoring system proved the validity of the framework.

In this paper, we investigate security issues in a collaborative wireless network in the presence of eavesdropping attacks, where multiple amplify-and-forward (AF) relays are exploited to secure the message transmission between legitimate users. We first consider the multiple AF relays all participating in assisting the transmission from source to destination, which is called all-relay based collaborative transmission scheme as denoted by all-relay scheme for notational convenience. We also propose the best-relay transmission scheme in which only the single “best” relay is selected to help the source transmit messages to destination. We then analyze the intercept behavior in wireless networks and evaluate intercept probabilities of the proposed all-relay and best-relay schemes as well as the conventional direct transmission without relay in a Rayleigh fading environment. Numerical results show that the best-relay transmission scheme always outperforms the all-relay and direct transmission schemes in terms of intercept probability. It is also shown that as the number of eavesdroppers increases, the intercept probabilities of both all-relay and best-relay schemes increase. Moreover, the intercept probability performance of all-relay and best-relay schemes significantly improves with an increasing number of relays, implying the advantage of exploiting multiple relays against eavesdropping attacks.

Due to the broadcast nature of wireless medium, wireless communication is extremely vulnerable to eavesdropping attack. Physical-layer security is emerging as a new paradigm to prevent the eavesdropper from interception by exploiting the physical characteristics of wireless channels, which has recently attracted a lot of research attentions. In this paper, we consider the physical-layer security in cooperative wireless networks with multiple decode-and-forward (DF) relays and investigate the best relay selection in the presence of eavesdropping attack. For the comparison purpose, we also examine the conventional direct transmission without relay and traditional max-min relay selection. We derive closed-form intercept probability expressions of the direct transmission, traditional max-min relay selection, and proposed best relay selection schemes in Rayleigh fading channels. Numerical results show that the proposed best relay selection scheme strictly outperforms the traditional direct transmission and max-min relay selection schemes in terms of intercept probability. In addition, as the number of relays increases, the intercept probabilities of both traditional max-min relay selection and proposed best relay selection schemes decrease significantly, showing the advantage of exploiting multiple relays against eavesdropping attack.

This work identifies and examines the status quo of intolerance of e-markets to the free conduct of individuals, which often provokes adverse strategies and lead to market failures. The work advocates that free market dynamics bring stable efficiency by equalizing the conflicting forces of the self interest and essential need of individuals. That motivates a collaborative reactions that diffuse monopolies. The constant learning at repetitive e-trades motivates traders to reason about e-market disruptions and adjust strategies. The free expressions of strategic conduct, hence, inspire the truthful reactions that result in an efficient ecosystem friendly exchange of wealth and resources. Hence, the work introduces the rule based bidding language that enables the free, flexible, concise, and symmetric expression of preferences and strategic conduct. The bidding language enables individuals to freely express their strategic actions as logical rule formulae on multiple feature-value preferences that jointly form the traded items. The free e-market deliberates on the logical rules for automatic deduction, elicitation and formulation of bids and asks. The deduction of rules enables also a faster e-market clearing and rapid e-trades. This work is an attempt to liberalizing the e-marketplaces by freely expressing the strategic choice that drive the resilience of stable social efficiency.

In this paper, we explore the physical-layer security in cooperative wireless networks with multiple relays where both amplify-and-forward (AF) and decode-and-forward (DF) protocols are considered. We propose the AF and DF based optimal relay selection (i.e., AFbORS and DFbORS) schemes to improve the wireless security against eavesdropping attack. For the purpose of comparison, we examine the traditional AFbORS and DFbORS schemes, denoted by T-AFbORS and T-DFbORS, respectively. We also investigate a so-called multiple relay combining (MRC) framework and present the traditional AF and DF based MRC schemes, called T-AFbMRC and T-DFbMRC, where multiple relays participate in forwarding the source signal to destination which then combines its received signals from the multiple relays. We derive closed-form intercept probability expressions of the proposed AFbORS and DFbORS (i.e., P-AFbORS and P-DFbORS) as well as the T-AFbORS, T-DFbORS, T-AFbMRC and T-DFbMRC schemes in the presence of eavesdropping attack. We further conduct an asymptotic intercept probability analysis to evaluate the diversity order performance of relay selection schemes and show that no matter which relaying protocol is considered (i.e., AF and DF), the traditional and proposed optimal relay selection approaches both achieve the diversity order M where M represents the number of relays. In addition, numerical results show that for both AF and DF protocols, the intercept probability performance of proposed optimal relay selection is strictly better than that of the traditional relay selection and multiple relay combining methods.

In this paper, we consider a cognitive radio network that consists of one cognitive base station (CBS) and multiple cognitive users (CUs) in the presence of multiple eavesdroppers, where CUs transmit their data packets to CBS under a primary user's quality of service (QoS) constraint while the eavesdroppers attempt to intercept the cognitive transmissions from CUs to CBS. We investigate the physical-layer security against eavesdropping attacks in the cognitive radio network and propose the user scheduling scheme to achieve multiuser diversity for improving the security level of cognitive transmissions with a primary QoS constraint. Specifically, a cognitive user (CU) that satisfies the primary QoS requirement and maximizes the achievable secrecy rate of cognitive transmissions is scheduled to transmit its data packet. For the comparison purpose, we also examine the traditional multiuser scheduling and the artificial noise schemes. We analyze the achievable secrecy rate and intercept probability of the traditional and proposed multiuser scheduling schemes as well as the artificial noise scheme in Rayleigh fading environments. Numerical results show that given a primary QoS constraint, the proposed multiuser scheduling scheme generally outperforms the traditional multiuser scheduling and the artificial noise schemes in terms of the achievable secrecy rate and intercept probability. In addition, we derive the diversity order of the proposed multiuser scheduling scheme through an asymptotic intercept probability analysis and prove that the full diversity is obtained by using the proposed multiuser scheduling.

In high-level-architecture (HLA)-based distributed heterogeneous collaborative engineering environments (CEEs), the construction of federation object model files is time consuming. This paper presents an ontology fusion approach aiming at establishing a common understanding in such collaborative environments. The proposed approach has three steps: ontology mapping, ontology alignment, and ontology merging. Ontology mapping employs a top-down approach to explore all bridge relations between two terms from different ontologies based on bridge axioms and deduction rules. Ontology alignment adopts a bottom-up approach to discover implicit bridge relations between two terms from different domain ontologies based on equivalent inference. Ontology merging generates a new collaboration ontology from discovered equivalent bridge relations. It adopts an axiom-based ontology fusion strategy and takes heavy-weighted ontologies into consideration. It can find all the explicit and derived interontology relations. In a typical CEE, the proposed approach has a great potential to improve the efficiency of preparation for HLA-based collaborative engineering processes, reduce the work load for adaptive adjustment of existing platforms, and enhance the reusability and flexibility of CEEs. A case study has been conducted to validate the feasibility of the proposed approach.

In decentralized computational environments applications or services belong to different organizations with different objectives. Grid computing model is appropriate for such environments, in particular for large scale computations. In this paper, decentralization is modeled in terms self-interested, economically inspired agents that attempt to achieve their goals. The objective of this work is to model the Grid scheduling problem based on the nature of the Grid environment and the characteristics of the Grid entities focusing on the resource utilization and completion time objectives. We create a mapping between the Grid scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model and a bidding language for the Grid scheduling problem. The proposed approach is being validated through a prototype implementation using the Coordinated Intelligent Rational Agent (CIR-Agent) model.

Wireless Sensor Network (WSN) has emerged as a dependable technology to improve the quality of life in smart homes through offering various automated, interactive and comfortable services. Sensors integrated at different places in homes, offices, and even in clothes, equipment, and utilities are used to sense and monitor occupants' positions, movements, vital signs, utility usage, temperature and humidity levels of rooms, etc. Along with sensing and monitoring capabilities, sensors cooperate and communicate with themselves to deliver, share and process sensed information and assist real-time decision-making procedures through triggering appropriate alerts and actions. However, ensuring privacy and providing adequate security in these crucial services provided by WSNs is a major issue in smart home environments. In this paper, we examine the privacy and security challenges of WSNs and survey its practicality for smart home environments. We discuss the unique characteristics that distinguish a smart environment from the rest, elaborate on security and privacy issues and their respective solution measures. A number of challenges and interesting research issues emerging from this study have been reported for further investigation.

Continual advances in modern technologies make it feasible to build intelligent independent living spaces for the elderly and disabled. In the past few years, various sensors and medical devices have been commercially available for home healthcare applications, but still there is a lack of effort on integrating heterogeneous commercial off-the-shelf (COTS) sensors from various vendors to collect data and provide more flexibility in product selection for customers. In order to address this issue, this paper presents a heterogeneous sensors integration platform for independent living spaces. The proposed platform, which is based on Apache Thrift, can accomplish the tasks of data collection, organization, and storage through cross-language services. A prototype system has been implemented and integrated into an independent living space monitoring system to validate the proposed approach.

Distributed, collaborative, and integrated product development with dynamic and reconfigurable manufacturing environments require more holistic information model to support its use throughout the product lifecycle. In this paper, a multiple dimensional in-process workpiece (MDIPW) is created based on dynamic feature information model (DFIM). The MDIPW is able to adjust its forms of expression through creating associations between feature-based models and manufacturing resources. These associations as representation of knowledge are embedded into the MDIPW, and make it more intelligent. The generated inprocess workpiece is a solid model which can be used for other purposes, such as inspection points generation.

Industries can benefit a lot from integrating sensors in industrial plants, structures, machinery, shop floors, and other critical places and utilizing their sensing and monitoring power, communicating and processing abilities to deliver sensed information. Proper use of wireless sensor networks (WSNs) can lower the rate of catastrophic failures, and improve the efficiency and productivity of factory operations. Ensuring reliability and providing adequate security in these crucial services provided by WSNs will reinforce their acceptability as a viable and dependable technology in the factory and industrial domain. In this paper, we examine the reliability and security challenges of WSNs and survey their practicality for industrial adoption. We discuss the unique characteristics that distinguish the factory environment from the rest, elaborate on security and reliability issues with their respective solution measures, and analyze the existing WSN architectures and standards. A number of challenges and interesting research issues have emerged from this study and have been reported for further investigation.

This paper presents a review of the collaborative smart home technologies for senior independent living. It focuses on the major features that enable safe independent living for the seniors. Based on the investigation on about 30 projects or systems in the area of Smart Home for independent and assisted living, key research issues as well as underlying opportunities have been identified.

As recent rescission impact is still evident in the slow recovery of industry, restructuring for total visibility and agility is inevitable to sustaining competitive edge and steady growth. Endowed with total visibility, smart automation is quite essential for responsive manufacturing and efficient supply-chains. This work proposes a new model for building smart automation for manufacturing systems that blends flexible manufacturing with total visibility, distributed intelligence, rationality, collaboration and flow control. In this vein, the work exploits the coordinated, intelligent and rational aspects of smart tag and resource agents with RFID enabling technology. While smart tag agents manage visibility for agile process flow and supply-chain management, smart resource agents improve responsiveness in shop floors and across supply chains. A hybrid control model drives the smart manufacturing system that realizes a reactive-reflex control at operations level and an agent-oriented deliberative control at planning level. At technology level, the system realizes JADE development environment that hosts the smart controller layers.

NC machining time of a part depends on its geometry and process plan, NC program, and machine characteristics. Since the integration of geometry and process plan, NC program, and machine characteristics is difficult during NC machining time forecasting, existing commercial software tools and research systems do not fully consider these factors, and therefore the machining time forecasting accuracy is low. In order to address this challenging issue, this paper proposes a feature-based model for NC machining time estimation. Experiment results shown that the proposed approach is feasible and practical.

This paper presents a novel approach aiming to dynamically maintain the collaboration ontology in the execution of an ontology-based federated collaborative product development system when a federate joins or has resigned from a given federation. The proposed approach includes two algorithms: ontology maintenance (+) and ontology maintenance (−), corresponding to joining and resigning situations. It adopts an axiom-based deduction ontology fusion strategy, and takes heavy-weighted ontologies into consideration. It can find all the explicit and derived inter-ontology relations, and furthermore it reaches the active upper bounds of implicit equivalent inter-ontology relations searching. This paper also discusses some implementation issues on the basis of TH_RTI, a RTI (Run Time Infrastructure) version developed by National CIMS ERC, Tsinghua University. The proposed approach has great potential to improve the efficiency of ontology-based federated collaboration executions, reduce the work load for adaptive adjustment of ever-existing platforms, and enhance the applicability and flexibility of collaborative product development systems.

This paper proposes an architecture for integration of indoor localization systems and decision support systems and/or manufacturing executive systems. In this paper, an indoor localization integration architecture is introduced under the scenario of an intelligent building localization system to uncover the relations among the necessary components and functions. Implementation details are also thoroughly explored, including class diagrams, work flows and the interface of the key component. The proposed architecture has the advantages of scalability and reconfigurability. In this architecture, different types of wireless sensors technologies including WiFi, ZigBee and RFID can be integrated simultaneously to support “smart environments”. Information from different data sources is transparent to information consumers (application systems), which means they can track “real” time position information of the interested assets in spite of the data sources. An application has been designed and implemented with the proposed architecture.

This paper presents a review of the recent developments of collaborative wireless sensor networks (CWSN). CWSN focuses on collaboration in wireless sensor networks (WSN). It has applied on almost every research field of wireless sensor networks, such as localization, topology, protocol, environment sensing and coverage, energy aware, security, and so on. As the resource on each node is limited and there are lots of nodes in WSN, now it is becoming a significant and basic method of WSN, especially when there are bottlenecks that affect the performance of a node or the networks. This paper presents and discusses the concept and features of CWSN, its research trends and its applications, along with our recent researches. The relationship between CWSN and IOT (Internet of things)/CPS (Cyber physical systems) is also briefly discussed.

Budget constraints, technological advances, and a growing elderly population are calling for major reforms in healthcare systems. The publicly managed and community-based independent living service provided for the elderly outside hospitals or long-term care institutions is a trend all over the world so that the healthcare systems would be sustainable in the future. We have started a long term initiative to integrate and develop innovative ICT solutions and construction technologies to provide quality and yet affordable supports for seniors to live safely and independently at their own homes. This paper presents some preliminary results on the development of a service-oriented system integration framework for community-based independent living spaces.

Collecting and analyzing real-time data of a distributed wireless sensor network system is very challenging since it combines a series of processes to continuously monitor and respond to a wide variety of events. This paper presents an event-driven, real-time data collection system architecture for an independent living spaces monitoring system. A service-replacement-based timestamp matching structure is also proposed to improve the system's robustness and maintainability. A prototype system has been developed to validate the proposed approach.

The six papers included in this special issue focus on systems integration and collaboration in design, manufacturing, and services.

This paper proposes an iterative bidding framework for integrated due-date management (DDM) decision making. We focus on a type of make-to-order environment in which a firm needs to quote due dates and prices and to schedule the production of a variety of job orders required by a large group of customers. In most cases, customers prefer shorter due dates. However, given limited production capacity and various cost constraints, the firm has to balance the attractiveness of its due-date quotations and the reliability of delivering accepted job orders. The key issue is how to integrate DDM decisions such that high-quality solutions, which benefit both the firm and the customers, can be obtained. We study the integrated DDM in an economic setting where customers are modeled as self-interested agents and the objective of the firm is to maximize social welfare. We present an iterative bidding framework as a decentralized decision support tool which enables the integration of key DDM decisions. Effective solutions are achieved through the automated negotiation between the firm and its customers. We provide analytical results on the application of the proposed framework to two special cases of the integrated DDM. We also evaluate the performance of the framework on general DDM problems through a computational study.

Ontology is considered as one of the most important roles in knowledge sharing and reusing. However, how to effectively construct the enterprise process domain ontology is a difficult problem. This paper proposes a new business process ontology construction approach using Process Specification Language (PSL) based on the EPMS1and Computer Integrated Manufacturing Open System Architecture (CIMOSA) in process simulation. In this paper, we propose a unified collaborative process of business process ontology model based on Region Connection Calculus (RCC) theory to resolve automatic semantic analysis on system integration process and the uncertainty of business process. An architecture of the cooperative work system for manufacturing enterprise is proposed and the prototype system of the enterprise process simulation has been expanded. Some experimental results are concluded and show the proposed method could effectively support business process cooperation.

Semantic integration is crucial for successful collaboration between heterogeneous information systems in an open environment. Traditional ontology-driven approaches rely on the availability of explicit ontologies. However, in many domains, this prerequisite cannot be met. In order to address this issue, this paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to an ontological view concept. To explicitly and formally specify the ontological views, a Frame-based Ontological view Specification Language (FOSL) is proposed. This language is based on the Frame knowledge representation paradigm and uses XML as the encoding. A prototype environment that supports semantic integration based on ontological views specified with FOSL is introduced.

Business process simulation plays an important role for enterprise business process re-engineering. This paper proposes a new algorithm to dynamically select a critical path with multiple constraints for enterprise process cooperative scheduling within cooperative business simulation systems. The proposed algorithm is based on Dynamic Programming and Analytic Hierarchy Process (AHP), and has been validated through a prototype implementation with case studies.

This paper describes a 3-D location tracking and visualization system using wireless sensor nodes in indoor environment. The system is based on modified radio-location fingerprinting algorithm and it uses inexpensive, programmable wireless embedded platforms operating in ZigBee wireless network protocol. The main emphasis of this study is on the ability to estimate the location of mobile wireless nodes in 3-D indoor spaces, without any costly infrastructure. The wireless nodes have been deployed in an experimental test bed, for location tracking and motion estimation. K-nearest neighbor algorithm has been implemented for location estimation over a set of radio location fingerprints collected in a 3-D space. A 3-D visualization interface has been generated in VRML in order to display the real-time sensor positions and the data in the 3-D world. Preliminary experimental results show that the proposed scheme can achieve accurate and stable location tracking in indoor environments.

With the objective of providing the best decision support to facility management and maintenance, this paper presents an agent-based, serviced-oriented approach for integrating data, information, and knowledge captured and accumulated during the entire facility lifecycle from its project planning, design, construction, material / component / equipment procurement, to operations and maintenance. All data / information / knowledge sources and hardware / software applications are loosely integrated through agent-based web services, either proactive or reactive, to provide decision supports over all the stages of the facility lifecycle, and particularly to optimize facility operations and maintenance. Proof-of-concept prototypes have been implemented to validate the proposed approach.

This paper presents a novel ontology fusion approach which aims to establish a mutual understanding among HLA (High Level Architecture) -based distributed heterogeneous collaborative product development systems. The approach includes three steps: ontology mapping, ontology alignment and ontology merging. It adopts an axiom-based deduction ontology fusion strategy, and takes heavy weighted ontologies into consideration. It can find all the explicit and derived inter-ontology relations, and furthermore it reaches the active upper bounds of implicit equivalent inter-ontology relations searching. The proposed approach has great potential to improve the efficiency of preparation for HLA-based collaborative product development, reduce the work load for adaptive adjustment of ever-existing platforms, and enhance the applicability and flexibility of collaborative development systems.

Service enterprise workflow simulation is an important approach to analyze service enterprise business processes dynamically. Traditionally, service workflow simulation is based on the discrete event queuing theory, which lacks flexibility and scalability. To address this problem, this paper proposes a service workflow simulation framework based on multi-agent cooperation. Social rationality of software agents is introduced into the proposed framework. Adopting rationality as a decision making strategy facilitates the implementation of flexible scheduling for activity instances. A simulation system prototype is developed and a business case study is conducted to validate the proposed framework.

Localization in wireless sensor networks has become a significant research challenge, attracting many researchers in the past decade. This paper provides a review of basic techniques and the state-of-the-art approaches for wireless sensors localization. The challenges and future research opportunities are discussed in relation to the design of the collaborative workspaces based on cooperative wireless sensor networks.

Virtual enterprise (VE) is the prime organization pattern of manufacturing in the 21st century, which generally is regarded as a management method for entity enterprise (EE), namely traditional enterprise, to realize agile manufacture using exterior resources. VE's performance management may be considered as the extension of the entity enterprise performance management. Based on the balanced score card, a VE's performance measurement system has been implemented by taking the degrees of satisfaction with member selection in learning and growth perspective, process collaboration between members in internal business perspective, the product-delivered time to customer in customer perspective, and the production cost in financial perspective, as the top layer performance indictors of the VE's performance management system,. The evaluation methods for the member selection, the process collaboration satisfaction degree, the product-delivered time satisfaction degree, and the production cost satisfaction degree for VE are discussed to validate the proposed approach.

This paper presents a Service-Oriented Coordinated Intelligent Rational Agent (SO-CIR-Agent) model to address three design issues of open Cooperative Distributed Systems (CDS): autonomy, distribution, and heterogeneity. This work incorporates the service-oriented design paradigm, agent-oriented design paradigm, and Web service technology as supporting pillars by extending the CIR-Agent model so that agents can survive not only in agent-oriented environments but also in service-oriented environments. The implementation issues of the proposed agent model are discussed at the end.

Today in many domains there are very limited explicit ontologies established for building information systems. The information systems have only schemas for their information repositories which to some extent imply the semantics of the information. Traditional ontology-driven semantic integration approaches cannot be directly applied in integrating these information systems. In our work we use the schemas and data instances of the information repositories to discover semantic correspondences between the schema elements and build a domain ontological view. We apply the hierarchical clustering technique on the data instances and use the clusters in the further analysis to reduce the cost of processing a large amount of data. The matching of schema elements is based on the probability distribution of the data instances. The preliminary results have demonstrated the effectiveness of this approach.

Wireless sensor networks (WSN) have become a significant research challenge, attracting many researchers. This paper provides an overview of collaborative WSN, reviewing the algorithms, techniques and state-of-the-art developed so far. We discuss the research challenges and opportunities in this area. The major focus is given to cooperative signal and information processing in collaborative WSN in order to expose important constraints in wireless applications which require distributed computing, such as node localization, target detection and tracking. In this paper, we also present and discuss the applications of multi-agent systems into WSN as a core technology of cooperative information processing.

This paper discusses the significance of the use of wireless sensor networks and the wireless node localization for applications in factory automation and facilities management. We present a testbed that has been developed for implementing and testing wireless localization algorithms within harsh and dynamic automation environments.

The resource-constrained project scheduling problem (RCPSP) is an extensively explored area. The existing RCPSP approaches tend to focus on single project scheduling problems without practical support to address the multiple project schedule coordination which involves constraints defined across projects. This paper extends RCPSP by involving time and resource constraints and proposes a practical dynamic task network scheduling algorithm. This algorithm takes time constraints, resource constraints, and particularly the dynamic task execution status into consideration. Dynamic scheduling through a partial task network is considered a unique feature of this algorithm. The proposed algorithm is fully implemented and tested in a web-based aircraft inspection maintenance management system.

Online customs declaration is becoming popular for international logistics through airports and seaports. This paper proposes a dedicated data interchange protocol and presents the supporting systems being developed for express post. Compared with the traditional Electronic Data Interchange, the proposed protocol and systems is based on XML, and supports RFID, Harmonized System codes, and image processing. The protocol and systems are efficient and advanced, and have been used by more than 30 logistics companies and airports in China. Among them are top logistics services companies like FedEx, DHL, UPS and TNT. The protocol is being considered as a national standard for customs declaration in China and has a potential to be accepted internationally based on China's best practices.

This paper presents a novel ontological view-based semantic transformation method to address the semantic heterogeneity design issue of open distributed information systems. After carefully reviewing the traditional ontology definitions, this work extends the ontological view concept to represent the partial knowledge about the same business domain in an open environment where common ontology does not exist or is not explicitly represented. Solutions in mathematical formulation and corresponding application algorithms, based on the definition of an ontological view, are proposed. By dealing with structural constant and predicate heterogeneity, respectively, the solution enables the ontological views to be transformed, one to another, automatically.

The kind of bidding languages used in combinatorial auctions contributes to various aspects of computational complexities. General bidding languages use bundles of distinct items as atomic propositions associated with logical connectives. When applying these languages to auction-based scheduling, the scheduling timeline needs to be discretized into fixed time units. We show that this discretization approach is computationally expensive in terms of valuation, communication, and winner determination. We present a requirement-based bidding language designed for auction-based scheduling. In the language, bids are specified as the requirements of scheduling a set of jobs, and prices are attached to the job completion times. Without timeline discretization, this language allows the expression of scheduling valuation functions in a natural and concise way, such that valuation and communication complexities are reduced. In addition, it results in efficient winner determination problem models. We have compared the winner determination models formulated using the two types of languages in terms of solving speed and scalability. Experimental results show that the requirement-based language model exhibits superior performance.

This paper proposes an auction-based multilateral negotiation mechanism for collaborative integrated due date management decision making. We assume a specific supply chain setting, in which a supplier supplies a group of customers. Due to limited production capacity within a scheduling time window (e.g. a season of the year), not all customers' due date requirements can be accommodated. This competition for the use of production resources among self-interested customers poses a strategic challenge in the design of due date management systems. To make the resource allocation efficient, we present an iterative auction framework to coordinate the allocation process and to align customers' self-interests with the overall system performance. Our purpose is to provide a decentralized decision support tool which enables the integration of key due date management decisions.

This paper presents a formulation and an algorithm for the winner determination problem in auction-based scheduling. Without imposing a time line discretization, the proposed approach allows bidders to bid for the processing of a set of jobs using a requirement-based bidding language, which naturally represents scheduling constraints. The proposed winner determination algorithm uses a depth first branch and bound search. The search branches on bids, and a constraint-directed scheduling procedure is used at each node to verify the feasibility of the temporary schedule. Experiments show that the proposed algorithm is on average more than an order of magnitude faster than a commercial optimization package, CPLEX 10.0.

Based on a previously developed multi-model driven collaborative platform for service-oriented software engineering, this paper reports an improvement to the platform by adding an additional view called portal view. A semantic global information model is used to implement multi-model consistency among the views through a prototype justice system. The improved platform provides four modeling views to design and implement service-oriented e-Business systems, especially for justice systems for the time being. Business people and technical engineers can collaborate for visual business, process, service modeling, and system demonstration remotely.

Semantic Web services, augmenting Web service descriptions using semantic Web technology, were introduced to facilitate the publication, discovery, and execution of Web services at the semantic level. Semantic matchmakers enhance the capability of UDDI service registries in the semantic Web services architecture by applying some matching algorithms between advertisements and requests described in OWL-S to recognize various degrees of matching for Web services. This paper proposes a novel semantics-enhanced Web service framework and a multi-level matching model for Web services. The matching process is achieved at five levels: syntactic, static semantic, dynamic semantic, qualitative service, and dependable service. A case study on collaborative design is used to demonstrate the proposed approach.

Rapid advancement of information and communication technologies has brought both challenges and opportunities to the construction industry. There have been significant research and development efforts on the application of systems integration and collaboration technologies in construction. This paper presents a research literature review on systems integration and collaboration in architecture, engineering, construction, and facility management (AEC/FM), and discusses challenging research issues and future research opportunities.

This paper extends the classical tree similarity measuring method and proposes a definition for cost of tree transformation operations based on the importance of each concept in the entire concept structure and similarity between individual concepts in a knowledge context. We apply the proposed method to ontology comparison where different ontologies for the same domain are represented as trees and their similarity is required to be measured. We show that the proposed method can facilitate the initiation of ontology integration and ontology trust evaluation.

In the last few years, several Web-based conference management systems have been developed and used by many international conferences. However, almost all of them were built on stand-alone Web servers. Their fault-tolerance, scalability and ability of responding to dispersed users are limited. Aimed at addressing these problems, this paper presents a collaborative conference management system, whose fault-tolerance, scalability and ability of responding to dispersed users are greatly enhanced by collaboration technologies. A prototype system has been implemented and used to facilitate the management of submissions and paper reviews of the CSCWD2008 conference.

Business process management systems traditionally focused on supporting the modeling and automation of business processes, with the objective of enabling fast and cost-effective process execution. As more and more processes become automated, customers become increasingly interested in managing process execution. This paper presents a set of concepts and a methodology toward business process intelligence (BPI) using dynamic process performance evaluation, including measurement models based on activity-based management (ABM) and a dynamic enterprise process performance evaluation methodology. The proposed measurement models support the analysis of six process flows within a manufacturing enterprise includingactivityflow,informationflow,resourceflow,costflow,cashflow, andprofitflow, which are crucial for enterprise managers to control the process execution quality and detect problems and areas for improvements. The proposed process performance evaluation methodology usestime,quality,service,cost,speed,efficiency, andimportanceas seven evaluation criteria. A prototype system supporting dynamic enterprise process modeling, analysis of six process flows, and process performance prediction has been implemented to validate the proposed methodology.

This correspondence presents a distributed scheduling algorithm for reactive maintenance of complex systems. The algorithm uses an iterative bidding procedure to assign operations of maintenance jobs to engineers with partially overlapped skill sets. In each round, an unassigned operation is selected based on the overlapping degree of engineers’ skill sets on the operation and the operation's average processing time among capable engineers. Engineers’ availability and cost information are used to determine the winner of an assignment. The effectiveness of this approach is demonstrated through a computational study. Prototype implementation and applications to real-world domains are discussed.

The rapidly changing market requires integrating information and collaborating among organizations to schedule jobs to machines that satisfy the objectives of service providers, and requesters. Moreover, delivering dynamic scheduling remains a major challenge especially when a diverse number of service providers and service requesters are geographically distributed. In this dynamic environment, scheduling usually involves complex and non-deterministic interactions between different participants. In this work, we propose a distributed multi-agent approach to model dynamic scheduling solution in manufacturing. We believe that agent-based model is appropriate due to its characteristics to support both dynamic behavior and distributed structure. The proposed approach is validated through a prototype implementation using the Coordinated Intelligent Rational Agent (CIR-Agent) model.

Workflow simulation is the prevalent approach to analyze workflow model dynamically. Generic method for workflow simulation is a kind of discrete event simulation, which is lack of flexibility and scalability. In this paper, we proposed a workflow simulation framework based on multi-agent cooperation to solve this problem. Social rationality of agent is introduced into the proposed framework. Adopting rationality as decision making strategies, flexible scheduling of activity instances is achieved. A simulation prototype is developed to validate the proposed framework.

Negotiation automation is a common problem in agent-based e-business applications. Traditional predefined negotiation approaches cannot fully solve the negotiation automation problem due to their inadequate compatibility and limited adaptability, especially in open dynamic environments. This paper proposes an adaptive negotiation framework by enhancing the adaptability of the negotiation platform and participating entities. This framework includes a set of meta-components, by which the negotiation participates can dynamically compose a suitable negotiation mechanism according to its negotiation context so as to increase the negotiation efficiency and system performance. The proposed framework has been designed, implemented and validated through a prototype system.

This paper presents an auction-based framework for scheduling problems in decentralized environments. The framework consists of an iterative bidding protocol, requirement-based bidding languages, and a constraint-based winner determination approach. Without imposing a time window discretization on resources the requirement-based bidding languages allow bidders to bid for the processing of a set of jobs with constraints. Prices can be attached to quality attributes of schedules. The winner determination algorithm uses a depth first branch and bound search. A constraint directed scheduling procedure is used at each node to verify the feasibility of the allocation. The bidding procedure is implemented by an ascending auction protocol. We show that this framework has improved computational properties compared with existing combinatorial auctions for decentralized scheduling. A case study of applying the framework to decentralized media content scheduling in narrowcasting is also presented.

Wide-area protection system (WPS) that monitors the contingency of a power system from the system-wide view and takes an instant action to restore the power system to its stable point is a trend for the future power system control. Because WPS scheme is strongly dependent on network topology, equipment parameters, and system configuration of the protected system, it is difficult to be simulated and tested under a conventional development environment. This paper proposes a Web services-based architecture for WPS design and simulation. This novel architecture wraps the legacy energy management system (EMS) functions into standard Web service interfaces, and provides standard remote computing and security evaluation services from EMS to WPS. Under this architecture, WPS scheme is simulated and tested efficiently and the gap between the simulation environment and the running environment is filled

Service-oriented architecture can improve e-business applications to be integrated and flexible. This paper proposes a multi-model driven collaborative development platform for service-oriented e-business systems. The platform provides three views, i.e., business view, process view, and service view to support service-oriented software engineering, top-down business planning and bottom-up service-oriented application development. Business and technical consultants can collaborate from distributed sites of, e.g., clients and IT vendors to provide their clients' with rapid development and demonstration. The proposed platform is driven by three models, i.e., service meta model, process model and business model. All of the three models support semantic description and rational operations, and facilitate intelligent service discovery, process execution and business-business integration. Concepts and implementation issues about the proposed platform has been presented in this paper. The platform has been developed and deployed in an innovation centre to be evaluated by visiting customers

An architectural description to capture dynamic aspects of software architecture provides the fundamental information to the external adaptation mechanism for self-adaptive systems. This paper presents a dAcme framework for distributed self-adaptive systems and introduces an architecture description language to address the dynamism of software architecture. In dAcme, the traditional model layer is separated into model and instance layers. Multi-domain constraints and self-adaptive tactics are extended based on basic Acme syntax. These extensions turn the analyzing and planning phase of the adaptation life circle into searching a solution for distributed constraint satisfactory problems. A media distribution case study is presented to demonstrate the expressiveness of the proposed approach

Agent-based e-business opens up a computational direction, in which software agents behave on behalf of their owners. However, enabling agents to make decisions and exploiting information to other unknown agents have introduced some security challenges among them is trust relationship while agents are dealing with unknown environments and their residences. Despite existing of some sophisticated proposed approaches addressing the above problem, most of them are suffering from complexity and therefore drawback from development. We propose an efficient trust model for distributed systems mainly focusing on multi-agent systems. We provide a feasible mechanism using some well-known cryptographic techniques such that not only it addresses the above issue but also guarantees the security and resistibility of the model against some attacks

An outstanding enterprise system should provide the analysis functions for various business process flows to assist enterprise decision-makers to understand their enterprise and make reasonable decisions. Based on activity-based management technology (ABM), this paper presents an evolutionary approach such as intelligent business process analysis concepts and metric measurement models for six kinds of process flows within manufacturing enterprises: activity flow, product flow, resource flow, cost flow, cash flow, and profit flow. The proposed process flow analysis technology has been developed as a dynamic enterprise process analysis tool within a process simulation and optimization environment to validate the proposed evolutionary approach

This paper presents an evolutionary approach supporting enterprise process modeling using intelligent agent technology. In this paper, an evolutionary enterprise process modeling methodology was presented from the concepts of enterprise process evolution, zero-time enterprise modeling technology to complex enterprise modeling. Based on an autonomous agent development environment, an agent-based enterprise collaborative modeling environment was implemented in which integrated some of software resource agents wrapped from the main function modules of EPMS (enterprise process modeling system)

In this paper, we draw attention to ontology as a mechanism for application integration and knowledge sharing in collaborative design. The paper provides a comprehensive literature review on this topic. It starts with its concepts, classification, and difference from other related technologies. Then the usage of ontology is discussed. Following the ontology life cycle, this paper compares several ontology authoring languages, ontology building methodologies and tools. It reviews some recent work on applying ontology to collaborative design. It concludes with a discussion on future R&D directions

Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified.

In the competitive world of the 21 century, companies must improve the way they develop products. Although concurrent engineering / integrated product development (CE/IPD) concepts are not new and revolutionary, achieving CE in specific situations is still a difficult task In fact the full concurrency may not be feasible, therefore the strategy on product development has moved to collaborative engineering and collaborative product development (CPD). This paper provides a comprehensive review of the recent R&D on CPD. The literature is reviewed from a number of aspects: system architectures, product modeling and visualization, process modeling and coordination, communication and collaboration, and implementation methodologies. Some R&D challenges and opportunities are identified and discussed. A particular focus is on CPD solutions for Small and middle sized enterprises (SMEs).

Small and medium sized enterprises (SMEs) are facing a more difficult situation in a globally competitive market than large organizations because of SMEs' limited financial and technical capability. In order to survive, they must change the way they do businesses and improve the way they develop products. This paper discusses requirements of collaborative product development (CPD) in SMEs and presents a proposed solution to facilitate CPD in SMEs, In the proposed approach, an Internet-based collaborative platform provides all participants with fundamental functionalities for collaborative product development. In the middle layer of the system architecture, a process management module, a product structure management module, a VR module, and a multimedia conferencing module work together to support collaboration among product development team members. Implementation issues are addressed and future work is discussed.

A successful result of choosing, invoking, integrating and composing services in a collaborative environment depends directly on locating reliable services with respect to their performances, histories, feedbacks from their customers, and generally their reputations. Nevertheless, lacking a secure mechanism to create countable reputation information may lead service providers to exaggerate their performances. We propose to apply threshold schemes under the context of service reputation towards providing an unconditionally secure "reputation" credits and systems for services and their providers. We argue that applying such a secure mechanism for reputation of services is not forgeable. As a result, any service requester was able to rely on valuable reputation information and choose the most reliable services with confidence.

Multi-agent systems have been recognized as a promising solution for developing collaborative design and manufacturing systems. Resource sharing is one of the key issues that need to be addressed effectively in these systems. Effective resources utilization usually requires solving the synchronization interdependency problem among the agents. This paper addresses the synchronization interdependency problem and provides an effective solution for resource sharing at runtime. Also, it describes a proof-of-concept prototype implementation of the synchronization device using coordinated, intelligent rational agent model and the JADE platform.

OWLS (Web Ontology Language for Services) is being widely concerned of composing a Web service based workflow. However, a complete workflow should include both internal and external processes for their strong correlation that can be expressed by the logic power of the Web Ontology Language. Moreover, agent based workflow, from another point of view, provides a flexible mechanism for service discovery and dynamic workflow coordination at run time. This paper presents an agent based workflow ontology model for the purpose of semantic workflow building, reasoning, and process reconfiguration.

The research objective is to develop methodologies and framework for collaborative process planning and scheduling, supported by a real-time monitoring system in distributed environments. A function block enabled collaborative process planning approach is proposed to handle various dynamic changes during process plan generation and execution. This paper focuses on collaborative process planning, particularly on the development of a function block designer As function blocks can sense environmental changes, it is expected that a so generated process plan can adapt itself to the changes with dynamically optimized solutions for plan execution and process monitoring.

This paper presents some results of our ongoing research on enhancing intelligent user assistance in collaborative design environments. The proposed intelligent assistant agent is composed of a user model, an inference component, a knowledge update component, and a collaboration component. The user model is divided into a user interest model and a user behavior model in order to capture the user's interests and user's actions related to the goal separately in the application domain. The detailed design of the user model has been reported previously. This paper focuses on the design and implementation of the collaboration component. It addresses issues on how the intelligent assistant agent reasons on the user interest model and the user behavior model by utilizing the collaboration component to find out the user's potential collaborators, and what collaboration strategy is used to choose a collaborator with the highest utility.

In today's globalized business world, outsourcing, joint ventures, mobile and cross-border collaborations have led to work environments distributed across multiple organizational and geographical boundaries. The new requirements of portability, configurability and interoperability of distributed device networks put forward new challenges and security risks to the system's design and implementation. There are critical demands on highly secured collaborative control environments and security enhancing mechanisms for distributed device control, configuration, monitoring, and interoperation. This paper addresses the collaborative control issues of distributed device networks under open and dynamic environments. The security challenges of authenticity, integrity, confidentiality, and execution safety are considered as primary design constraints. By adopting policy-based network security technologies and XML processing technologies, two new modules of Secure Device Control Gateway and Security Agent are introduced into regular distributed device control networks to provide security and safety enhancing mechanisms. The core architectures, applied mechanisms, and implementation considerations are presented in detail in this paper.

Global competition is driving manufacturing companies to change the way they do business. New kinds of shop floor control systems need to be implemented for these companies to respond quickly to changing shop floor environments and customer demands. This paper presents a new concept called iShopFloor-an intelligent shop floor based on the Internet, web, and agent technologies. It focuses on the implementation of distributed intelligence in the manufacturing shop floor. The proposed approach provides the framework for components of a complex control system to work together as a whole rather than as a disjoint set. It encompasses both information architecture and integration methodologies. The paper introduces the basic concept of iShopFloor, a generic system architecture, and system components. It also describes the implementation of eXtensible Markup Language message services in iShopFloor and the application of intelligent agents to distributed manufacturing scheduling. A prototype environment is presented, and some implementation issues are discussed.

The current Web services technologies have not exploited sufficient semantics and approaches to dynamic service‐oriented operations in open environments. This paper argues that such operations can be realized through agent‐oriented interaction approaches. The key challenge is to develop an integration framework for the two paradigms, agent‐ and service‐oriented, in a way that capitalizes on their individual strengths. This paper proposes the notion of agent‐based Web services (AWS). We address several critical issues, including the appropriate architectural framework and the structure of its main elements (agent‐based Web services), their meta‐model, supporting technologies, integration method, and implementation approach. An integrated development environment for this framework called SOAStudio has been developed and tested by implementing a case study of a reverse auction e‐marketplace.

This paper reports on an on-going research to make coordination and cooperation ubiquitous to the control and tasking, of multiple heterogeneous physical robot agents (PRA). The primary objective of the work presented here is to develop a distributed architectural framework that enables multiple PRAs to coordinate high-level tasks in a collaborative manner. The proposed architecture is based on distributing the elements of the system at two levels, namely abstract and physical. At the abstract level, we identify two layers, cognitive and action layers, based on the type of the PRA's responsibilities. At the physical level, our architecture recognizes the coexistence of agent-oriented framework, such as JADE, and non-agent frameworks, like CORBA, to accommodate the cognitive and the action layers respectively. This paper will also present the implementation challenges and our experience with the integration of heterogeneous agent and non-agent frameworks.

Scheduling problems concern the allocation of limited resources over time among both parallel and sequential activities. The majority of these problems belong to the class of NP-hard. Agent-based approaches have been applied to solve difficult scheduling problems. Load balancing is usually adopted as an optimization criterion for some scheduling problems, such as resource allocation in grid computing environments. However, in many practical situations, a load balanced solution may not be feasible or attainable. To deal with this limitation, this paper presents a generic mathematical model of load distribution for resource allocation, called desired load distribution. The objective is to develop a model that can be utilized for classical resource management settings as well as a model for a many-to-many optimized market setting.

Manufacturing scheduling problems, especially in a dynamic, uncertain environment, belong to the NP-hard class. Agent based approach has been considered as a promising solution to these types of problems. However, it brings in another challenge which is how to make those costly negotiation processes among agents effective. This paper addresses this problem by proposing an Adaptive Negotiation Framework which models the dynamic nature of agent based manufacturing scheduling at negotiation level explicitly. The main contributions of this paper include: (1) an Agent-Based Adaptive Negotiation Framework for manufacturing scheduling, (2) selection heuristics of multiple economically inspired negotiation models, (3) the integration of adaptive negotiation heuristics, economic models and the Coordinated Intelligent Rational agent architecture.

This paper provides an analysis of combining data management and agent technologies, proposes a data agent paradigm aiming at distributed multi-agent collaborative computation environments - collaboration oriented data agent (CODA), and discusses the selected design solution and implementation technologies. CODA is designated with three main features - XML-based structured data infrastructure, central service and distributed data management, and event-condition-action rules-based reactive transaction mode, all of them are arguably bringing out the best in each other, which has been shown in a prototype and a multidisciplinary design optimization-oriented case study.

This paper presents a Web and agent based distributed multidisciplinary design optimization (MDO) environment, called WebBlow, being implemented using a number of emerging technologies such as the Internet, Web, XML, Java, and intelligent agents. The Web provides the infrastructure for implementing the distributed MDO environment over the Internet. It provides an anytime/anywhere solution for project managers and designers working on multiple design and optimization projects. It can also ensure the system security and mitigate the difficulties with firewalls encountered by conventional software agents. Intelligent agents are used to encapsulate process simulation and performance simulation as well as optimization software packages in order to facilitate the integration of various legacy systems, and more importantly realize the dynamic computing load balancing among a cluster of computers behind the Web server. XML is used for data management at the server side as well as for message exchange among simulation and optimization software agents. A prototype environment is presented for blow molded automotive parts design.

Targeting the remote monitoring and control of shop floors, this paper proposes a new framework called Wise-ShopFloor Web-based integrated sensor-driven e-Shop Floor that can be applied to distributed manufacturing environments. It utilizes the latest Java technologies (Java 3D and Java Servlet) as enabling technologies for system implementation. This web-based framework allows users to monitor and control a distant shop floor device using Java 3D models instead of cameras. The behavior of a 3D model is driven by sensor signals of its physical counterpart. The goal of this research work is to eliminate network traffic, while still providing end users with intuitive environments.

The paper presents a framework for building Web-based collaborative workspaces using the latest Java technologies-Java 3D, JavaServer Page (JSP), and Java Servlet. This Web-based approach allows designers, engineers and production managers to share a common workspace that can be used for design review, production monitoring, remote control, and troubleshooting, based on a set of interactive Java 3D models that represent the physical world with common interests. Following a brief overview of the related research work, the paper discusses the Java 3D concept from its scene graph structure to behavior control, and explains our approach to building Web-based collaborative workspaces using Java 3D. The proposed framework uses the popular client-server architecture and view-control-model design pattern with a secured session control. Control logic and the interfaces, which interact with the real world, are handled by an application server through servlets. The benefits enabled by the framework include reduced network traffic, increased flexibility of remote monitoring, interactive control, Web-based synchronous collaboration and quick response. It also shows significant potential for various Web-based real-time and distributed applications.

Both workflow and agent technology have recently been applied to business process management. The integration of these two technologies definitely provides solutions to problems that cannot be solved by either of them individually. This paper summarises the capabilities of these two technologies and discusses the forms and benefits of integrating them for business process management. Generally, agent-enhanced workflow management and agent-based workflow management are the main forms of application of intelligent agents to workflow systems. Some research issues in each form are discussed. A conclusion, with a discussion about future research directions, is also given.

Multi-user interfaces are generally held to be difficult to write. However, several groups have shown that a good multi-user infrastructure or a toolkit can make the programmer burden more tractable. This paper expands on the theme and describes an infrastructure that supports not only multi-user interfaces but also their interaction with other (non-interface) agents. The idea is to support agent collaboration and to consider an interface to be just another type of agent.

Real world engineering design projects require the cooperation of multidisciplinary design teams using sophisticated and powerful engineering tools. The individuals or the individual groups of the multidisciplinary design teams work in parallel and independently often for quite a long time with different tools located on various sites. In order to ensure the coordination of design activities in the different groups or the cooperation among the different tools, it is necessary to develop an efficient design environment. This paper discusses a distributed architecture for integrating such engineering tools in an open design environment, organized as a population of asynchronous cognitive agents. Before introducing the general architecture and the communication protocol, issues about an agent architecture and inter-agent communications are discussed. A prototype of such an environment with seven independent agents located in several workstations and microcomputers is then presented and demonstrated on an example of a small mechanical design.