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.

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.

Language models have been proven to handle time series data after tokenization and show generalization performance on unseen forecasting tasks. However, existing techniques for tokenizing time series data struggle to eliminate redundant information and noise, which can lead to signal aliasing and cumulative quantization errors, making it difficult to further improve prediction performance. In this paper, we propose an Adaptive Component Extraction and Tokenization (ACET) framework, which includes two key novelties to address these challenges: the Dynamic Component Extraction Module (DCEM) and the Time Series Tokenization Module (TSTM). The DCEM dynamically isolates the principal components from the interference in the original signal, eliminating the need for manual parameter tuning. This not only enhances the accuracy of signal tokenization but also mitigates the adverse effects of high-frequency noise. Then, the TSTM tokenizes continuous time series data while preserving long-term trend features, ensuring that critical information is retained for subsequent forecasting. Extensive cross-domain experiments on various real-world datasets demonstrate that, in zero-shot forecasting scenarios, ACET achieves improvements of 19.27% in WQL and 6.63% in MASE, compared to baseline methods.

Industrial time series prediction (ITSP) is critical to the predictive maintenance system of modern industry. However, time-varying conditions and complex industrial processes cause the distribution drift of industrial time series, raising the difficulty of prediction. This article proposes an ITSP model considering distribution information, namely MDEformer. First, the multivariate dynamic embedding (MDE) is designed to provide the property of the channel-binding dynamic distribution awareness. Specifically, a dynamic mode transition and selection module is adopted to exploit dynamic distribution features of time series, and the bidirectional dynamic residual connection integrates dynamic distribution information into embedding vectors to filter distribution change interference. Then, the vanilla Transformer encoder is used to achieve multivariate prediction. Finally, a generative pretraining and fine-tuning strategy is used to enhance the generalization ability in real production scenarios. Extensive results on a real-world zinc smelting dataset illustrate the superiority of MDEformer.

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.

Human detection is an important problem which has many applications in collaborative manufacturing, such as employee counting and human fatigue detection. Recently, some methods based on convolutional neural network (CNN) have been proposed to solve this problem, but they often suffer from poor generalization due to the lack of real-world datasets from industrial scenarios. In this paper, we propose a graph pooling based human detection method to improve the generalization of existing CNN models. First, we segment an image and abstract it as a graph where nodes represent the segmented regions and the features of nodes are pixel values of the segmentation. Second, a graph pooling model is used to extract coarse-grained but informative features from the graph. Next, the pooled graph is reconstructed to image and input to a popular CNN model (i.e., YOLOv5) for human detection. We perform experiments on data containing a great number of images from industrial scenarios. Experimental results show that the proposed method outperforms the popular CNN model and has better generalization performance.

At present, data-driven fault diagnosis methods have made excellent achievements. In industrial scenarios, it is difficult to obtain sufficient amount of fault data, which means intelligent fault diagnosis is often faced with imbalanced data problem. Moreover, the label noise is usually brought due to manual recording errors so as to seriously affect the diagnosis performance. To address these problems, this paper proposed a safe-domain generative adversarial network with Transformer (SDGAN). A safe domain selecting method is used to remove the noisy samples and construct a pure dataset which poses no risk to the training process of GAN. Therefore, GAN is able to generate high-quality minority samples to balance the original dataset. In addition, the Vision Transformer (ViT) is also applied as a classifier to recognize the global information for each fault sample and achieve high diagnostic accuracy. The experimental results show that SDGAN achieves great diagnosis performance on various imbalanced ratios and noise ratios cases. Furthermore, SDGAN outperforms other baseline methods on imbalanced fault diagnosis with label noise, which indicates that the SDGAN can effectively solve real-world industrial problems.

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.

Product backorder is a common problem in supply chain management systems. It is essential for entrepreneurs to predict the likelihood of backorder accurately to minimize a company’s losses. However, existing methods are hard to achieve satisfactory results since the number of backorders and non-backorders are extremely imbalanced. Besides, the backorder data’s attributes are complex to oversample them effectively. To address these problems, a novel hybrid sampling method is proposed to help predict extremely imbalanced backorder. The Randomized Undersampling (RUS) and a Conditional Wasserstein Generative Adversarial Network (CWGAN) are innovatively introduced into backorder prediction. First, RUS is used to reduce the majority non-backorder samples. Second, CWGAN is served as an oversampling technique to generate high-quality backorder samples. It utilizes unique structures in the generator and the discriminator to effectively model both numerical and categorical variables. Finally, the training dataset is balanced, and the Random Forest Classifier (RFC) is adopted to make backordering prediction. In the experiments of Kaggle’s dataset ‘Can you predict product backorder?’, our proposed method is superior to all benchmark methods in terms of standard evaluation metrics. The results show that our proposed product backorder prediction model is effective.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

Image segmentation in the big data context is a hot topic in the field of image understanding. Contrary to traditional computing paradigm with precise description of problems, Granular Computing (GrC) is studied by utilizing the toleration of imprecise, incomplete, uncertain and mass information to make systems manageable, robust, low-cost and harmonious. Thus it is an efficient measure to simplify calculation. In this paper, a multi-feature fusion approach based on quadtree and Grc was presented in accordance with the mechanism of human vision. In this technique, firstly original images are reduced into gray images, binary images and quadtree-segmented images, then features are extracted with different granularities from the reduced images respectively, and finally original images are partitioned precisely by the fusion of features according to quotient space theory (QST). Based on the technique of granularity hierarchical and synthesis, this paper gives the example and validation of color image segmentation. Experimental results demonstrate that the algorithm is valid for image segmentation with both speed and accuracy obviously approved compared with common segmentation methods.

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.

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.

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.

Wireless sensor networks are distinguished from traditional networks. Existing routing algorithms are not effective in supporting such networks. In this paper, a novel quantum-inspired ant-based routing (QABR) algorithm for WSNs is proposed, and it has rapid convergence and good global search ability. Experiment results show that the proposed algorithm can find rapidly the optimal path from source node to destination node, and prolong the networks lifetime.

In order to solve the transformation problem of BOM from engineering BOM to maintenance BOM for MRO system (MRO, Maintenance, Repair and Overhaul), a formal transformation model of BOM view is established. In this model, the intermediate component, inherit component, virtual component are defined on the specific maintenance management domain, and the transformation process from engineering BOM to maintenance BOM is discussed through feature recognition methods and rules. Last, the proposed model is applied to MRO system for a steel manufacturing enterprise, which indicates that this methodology is effectual to the problem.

In order to ensure the enterprises can acquire the changes of the market at any time, then adjust the strategies quickly, the state information of business process execution can reflect the situation best. If the status in a process model can be defined clearly, you can monitor the execution information of the process which is based on the processes run-time implementation quickly, andchange the process operation in accordance with real-time requirements. Then put forward the claim of building state-module for executable business process, this method, based on the Web Service state concept, the basis of the technical accomplishment and the connotations of the executable business processes, is used to model for the state of executable business process by FSM and combine Web Service status information with the status of running business processes, to handle the state management problems of Web Service in the processes management.

Users focus on their kernel business domain, and have no the ability to use directly the service flow and composite service components. Base on the web application pattern, the consultation-model-based services application Model (CMSAM) is presented, and the business-model driven services flow framework is proposed in CMSAM. The CMSAM describes the different responsibility amongst the services requester, the consultation services provider, the domain services choreographer, the domain services provider, the content provider and content integrator. The CMSAM maps the service components onto the service executable environment through the business process model, the requirement model, the business process flow model, the abstract service flow model, the executable service flow model and the service instances. In the charging Algorithm, it is the objective function to minimize the service fee under the condition of searching successfully the suitable service for user's business function. At last, an implementation process of part modeling service is given in the collaborative product design and analysis domain for the CMSAM as an example.

The field of financial cost analysis and decision is a conceptually rich domain where information is complex, huge in volume and a highly valuable business product by itself. The eXtensible Business Reporting Language is an XML vocabulary designed to simplify the automation of exchanging financial information, and to enable the automatic extraction of financial and cost information by software applications. However, there exist some important limitations in the current version of the XBRL specification, more insightful semantics and a sharper level of representation are required to describe and exploit complex information. In this paper, we design a semantics-supported XBRL model and develop a Service Oriented Architecture (SOA) based system architecture for enterprise total cost analysis and decision in collaborative manufacturing environment by adopting Semantic Web technologies and standards. The extended model can provide more insightful semantics and a sharper level of representation for financial cost management and analysis.

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.

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.