Tucker decomposition approximates the original tensor using a set of factor matrices and a core tensor, thereby reducing the storage and computational cost. Existing incremental Tucker decomposition techniques have limitations in efficiency and are not applicable to diverse dynamic incremental tensors. This paper proposes a dynamic incremental Tucker decomposition method based on heterogeneous computing, which efficiently handles complex dynamic tensors while preserving computational efficiency. Specifically, the target optimization function of Tucker decomposition is reconstructed to decouple the intrinsic correlation between factor matrix rows and fixed tensor dimensions, and the optimal solutions for the factor matrix rows are derived. This enables the algorithm to adapt to arbitrary forms of incoming incremental data, including time-series tensors, without recalculating existing data, thus enhancing data utilization efficiency. Then, a lightweight precision optimization strategy is introduced, which iteratively updates factor matrix rows using the most element-rich dimensions of each tensor order based on recorded observable data volumes. This strategy improves precision while significantly reducing computational costs. Furthermore, a task-characteristic-based heterogeneous computing scheduling strategy is proposed. By extracting highly parallelizable intermediate operators and leveraging the high throughput of GPUs, the algorithm is accelerated, leading to a 10 to 40 times speedup in incremental tensor decomposition compared to state-of-the-art methods, while maintaining accuracy.

Accurate and robust underwater positioning is essential for enabling autonomous operations of underwater vehicles in deep-sea environments, particularly within the framework of the industrial underwater Internet of Things (IoT). Conventional acoustic positioning systems typically rely on two-way ranging, which doubles the signal travel distance, thereby reducing accuracy, increasing energy consumption, and constraining operational range. To address these limitations, this study presents a novel deep-sea navigation and positioning framework based on one-way acoustic communication synchronized via atomic clocks. This approach significantly shortens signal propagation paths and improves real-time responsiveness, enabling high-precision long-range operations. To enhance signal detection under low signal-to-noise ratio (SNR) conditions, a signal processing scheme is developed that integrates spread-spectrum modulation with energy-based normalization, effectively extending the operational range beyond 10 km. Sea trials conducted at depths greater than 4000 m validate the proposed system, achieving a maximum horizontal range of 11.5 km and an average positioning error of only 0.15% relative to the slant range. Furthermore, the system’s inherent scalability supports simultaneous positioning of multiple autonomous underwater vehicles (AUVs), paving the way for future large-scale, cooperative deep-sea exploration missions.

Canonical Polyadic decomposition (CPD) obtains the low-rank approximation for high-order multidimensional tensors through the summation of a sequence of rank-one tensors, greatly reducing storage and computation overhead. It is increasingly being used in the lightweight design of artificial intelligence and big data processing. The existing CPD technology exhibits inherent limitations in simultaneously achieving high accuracy and high efficiency. In this paper, a heterogeneous computing method for CPD is proposed to optimize computing efficiency with guaranteed convergence accuracy. Specifically, a quasi-convex decomposition loss function is constructed and the extreme points of the Kruskal matrix rows have been solved. Further, the massively parallelized operators in the algorithm are extracted, a software-hardware integrated scheduling method is designed, and the deployment of CPD on heterogeneous computing platforms is achieved. Finally, the memory access strategy is optimized to improve memory access efficiency. We tested the algorithm on real-world and synthetic sparse tensor datasets, numerical experimental results show that compared with the state-of-the-art method, the proposed method has a higher convergence accuracy and computing efficiency. Compared to the standard CPD parallel library, the method achieves efficiency improvements of tens to hundreds of times while maintaining the same accuracy.

The breakthroughs of next-generation information technologies have accelerated the advancement of industrial cyber-physical intelligence (ICPI), particularly in system intelligence and applications. However, this progress has also brought challenges in ensuring operational reliability and system intelligence. Anomaly detection, a critical component of fault-tolerant and intelligent ICPI, is usually addressed by treating it as a one-class classification and location problem. While autoencoder frameworks have shown promise in addressing this challenge, most existing methods usual struggle with precise anomaly identification or require resource-intensive region-based training. Furthermore, the dynamic nature of anomalies and the scarcity of labeled training data complicate the development and evaluation of anomaly detection models. In this article, an innovative tensor-based knowledge distillation approach (TKDA) is introduced, which integrates a pretrained teacher network, a tensor-decomposed student network, and a denoising module into a unified framework. Anomalies are identified and localized by analyzing differences in intermediate activation values between teacher and student networks during data processing. Extensive experiments demonstrate that TKDA addresses the limitations of low accuracy in anomaly location and inefficiency in computational processes, achieving significant improvements across diverse datasets, including F-MNIST, MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two medical datasets.

Schema information is increasingly crucial to improve service discovery, recommendation, and composition, addressing link sparsity and lack of explainability inherent in methods relying solely on triples. However, existing approaches predominantly utilize schema information as a rigid filtering mechanism, equivalent to fixed conditions that lack the capability to adaptively adjust based on model learning. This paper introduces a novel learnable schema-aware knowledge embedding framework that enhances service link prediction by synergizing entity, relation, and type embeddings through a coupled matrix-tensor factorization model. To our knowledge, this is the first approach that couples entity and relation embeddings to enable adaptive learning ofSchemaEmbeddings (SchemaE). Our framework is both expressive and easy to use, with the capability to generalize to existing bilinear models. Within this framework, we further propose the schema prompt method for embedding isolated nodes, which typically suffer from sparse relations or the absence of neighbors, leading to biased representation often overlooked in existing works. Despite embedding schema information, our model remains lightweight due to the introduction of a parameter-efficient strategy via type assists. We conduct extensive experiments on four public datasets, including comparisons with existing SOTA models, parameter analysis, performance validation on extended models, and visualization. The experimental results confirm the effectiveness and efficiency of the proposed model.

No abstract available.

Internet-of-Things (IoT), is the comprehensive interconnection systems of computational, networking and physical devices with the important goal of providing proactive and personalized services efficiently. The foundation of such services is big data integration and processing among various devices, which brings important challenges including data fusion, transferring and sharing of computational results. On the other hand, decentralized blockchain platforms provide novel technologies for reliable IoT data integration and processing. In addition, to facilitate decentralization and distribution of IoT big data, tensor-train (TT), as a tensor decomposition method, can play a vital role. Therefore, in this paper, a tensor-train-based permissioned-private (P2) blockchain is proposed to realize the organization, integration, sharing and applications of IoT data for intelligent IoT services. To demonstrate the performance of the proposed method, case studies with IoT data are carried out on permissioned-private chain platform to measure its performance.

In this paper, we present the Magnetic Resonance Image (MRI) denoising method via nonlocal multidimensional low rank tensor transformation constraint (NLRT). We first design a nonlocal MRI denoising method by non-local low rank tensor recovery framework. Furthermore, a multidimensional low rank tensor constraint is used to obtain low-rank prior information combined with 3-dimensional structure feature of MRI image cubes. Our NLRT can achieve denoising by retaining more image detail information. The optimization and updating process of the model is solved via the alternating direction method of multipliers (ADMM) algorithm. Several state-of-the-art denoising methods are selected for comparative experiments. In order to reflect the performance of the denoising method, Rician noise with different levels is added to the experiment to analyze the results. The experimental results prove that our NLTR has more outstanding denoising ability and can obtain better MRI images.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

Prediction of industrial time-series is crucial for various Industrial Internet of Things applications. Despite the high accuracy of deep learning methods for time-series prediction, the significant memory requirements of deep learning models pose a challenge for the limited computational resources of industrial edge devices. To address this issue, this work proposes BTFormer, which achieves a high compression rate while maintaining competitive performance. First, a binary adaptive attention module is proposed to mitigate the loss of attention information caused by binarization. Second, a trend information soft-link is proposed to propagate trend information between layers and improve the representation ability of the model. Finally, a distribution-guided distillation strategy is proposed to optimize the training process. The experiments demonstrate that BTFormer effectively reduces model memory usage by 31.0 times and improves computational efficiency by 32.8 times while maintaining competitive performance.

Industrial Cyber Physical Systems can use data and information gained from across a variety of different environments to enable robots that are reconfigurable. Custom grasping is a basic operation a robot must be able to carry out for a given task, i.e., finding the best grasping point for emergent behaviors. However, environmental disturbance and limited data degrade the precision and speed of many tailored machine learning models on robot grasping detection. This paper proposes a region-based method to enable fast custom grasping through fewer RGB-D data. The grasping detection problem is simplified as a two-stage prediction problem. At the first stage, a robust grasp candidate generation strategy is proposed based on the Sobel operator. At the second stage, a region-based predictor is designed to locate the best grasping point-pair for an emergent task. The predictor is trained by a modified consistency based self-training method to realize semi-supervised learning. Experimental results show that the success rate of custom grasping of new emergent object can be increased by 3.4% on average using the proposed method. By introducing data augmentation strategies in training, the success rate is further increased by 9.2% on average. A robot is able to grasp new object with 91.5% success rate using less than 100 training samples. The number of training samples required for the proposed method is less than to 1% of which for the previous works. Note to Practitioners—This research was motivated by the problem of robot reconfigurability for various industrial automation processes and focuses mainly on the recognition of grasping point-pair of emergent object for different task. Existing approaches on robotic grasping detection are tailored to a given object and require expensive training with large amount of labeled data. This paper presents a region-based few shot learning approach that enables the robot to detect the best grasping point-pair autonomously and quickly. We show how to generate candidate point-pairs with image distortion and background disturbance. We then demonstrate how the best grasping point-pair can be located with much less training cost. Experiments suggest that this approach is feasible in robot automation for handling a class of objects. In future research, we will construct behavior learning module to enable evolving cyber-physical robotic system for more purposes.

Cyber-physical-social systems (CPSS), an emerging cross-disciplinary research area, combines cyber-physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.

Exploring the characterization laws of image data and improving the efficiency of image data characterization knowledge is essential to promote the development of the Internet of Things technology. Considering that images in the real world usually contain multiple objects, and the objects are closely dependent. For these reasons, it brings great challenges to the robust representation learning of multilabel images. In general, researchers model the relationship between objects based on a class activation map and use graph convolution to mine the dependencies between objects. However, graph structure data often contain noise, which means that the edges between nodes are sometimes not so reliable, and the relative importance of neighbors is also different. Based on this, our goal is to reduce noisy connections and false connections between objects, eliminate multilabel image representation bias, and learn robust representations. Therefore, we propose a robust representation learning method for multilabel images driven by graph attention network (RRL-GAT). Specifically, to reduce the accidental false connection of objects in the image, we propose the class attention graph convolution module (C-GAT) to mine the strong association structure between categories. Besides, for the dynamic correlation between objects in the image, we propose an adaptive graph attention convolution module (A-GAT) to capture the subtle dynamic dependencies in the image. The results on two authoritative data sets show that our method is significantly better than all current state-of-the-art methods. Besides, the visualization results show that RRL-GAT can capture the semantic relationship of a specific input image and has sufficient recognizability.

As one of the essential paradigms of Industrial 4.0, the Industrial Internet of Things (IIoT) challenges existing data management and information services by supporting computational-intensive applications, in which devices share and receive information through interactions under resource constraints. When there exist diverse service requirements of IIoT applications, information dissemination will be more likely driven by service-oriented incentives. In this article, a novel information dissemination process with the service-oriented incentive mechanism is analyzed and modeled in IIoT, which depicts the dynamical evolution of IIoT devices’ interactions. In particular, the characteristics of service-oriented activating and dissemination degenerating are considered due to the unique capability of IIoT devices. Extensive theoretical and simulation results verify the dynamical behaviors of information dissemination, including the propagation threshold, equilibrium, and stability. In addition, comparative simulations have demonstrated the service-oriented incentive mechanism further expands information diffusion by driving the participation of IIoT devices.

A key step in remanufacturing is disassembly of the “core” or the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the cores. Rust, corrosion, deformation, and missing parts may require disassembly plans to be changed and adapted frequently. Conventional industrial automation that usually serves in repetitive and structured activities may fail when it is applied to disassembly. This research investigates the flexible sequencing of robotic disassembly in the presence of failed automation operations and develops online recovery by incorporating backup actions. It starts with modeling the time and success rate of a backup action. The expected disassembly time and completion rate of a disassembly plan are deduced according to the failure probability of both the operations and their backup actions. A biobjective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm. Two solution selection criteria are combined to produce potential offspring candidates in each evolutionary generation. Experimental results show that the backup actions allow efficient recovery from automation and can potentially improve the robustness of robotic disassembly.Note to Practitioners—This research was motivated by the development of automated disassembly techniques. Industrial automation techniques usually use predetermined operation motions. Robotic disassembly using such an approach may fail due to uncertainties in the condition of the products (e.g., positioning and geometry). This article introduces backup actions for disassembly sequence planning and describes the logic and reasoning of their implementation. Our proposed method can theoretically increase the completion rate of automated robotic disassembly. Experimental studies suggested that backup actions are efficient in providing a reliable disassembly sequence and, thus, can improve the robustness of robotic disassembly. In future research, we will implement typical backup actions and establish an automated disassembly process with a replanning module.

This is the editorial of the SS entitled ‘`Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation’'.

Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a$T^{2}$-Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the$T^{2}$-tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method.

Industrial Internet of Things (IIoT), as an important industrial branch of the Internet of Things (IoT), has an essential purpose to improve intelligent industrial production. For this purpose, IIoT big data should be efficiently processed to mine valuable information. In handing the IIoT big data, cloud-edge computing is getting more attention to reduce the interaction latency to meet the real-time requirement, especially in the field of prognostic and health management (PHM). It is expected that artificial intelligence (AI) technologies will significantly change the manner of processing IIoT big data. Therefore, new methods about PHM, combining cloud-edge computing with AI technologies, are required to process the IIoT big data for intelligent industrial manufacturing. As an essential element of PHM, predicting the remaining useful life (RUL) of industrial equipment plays an increasingly crucial role, especially for industrial intelligence. However, traditional methods pay much attention on prediction accuracy and neglect the influence of computing time. In this article, by combining cloud-edge computing with AI technology, a new data-driven method, namely, cloud-edge-based lightweight temporal convolutional networks (LTCNs), for RUL prediction is proposed. First, to meet the real-time requirement, a cloud-edge computing and AI-based framework for RUL prediction is presented. Second, a new model structure named LTCN is proposed and applied in the framework. Real-time prediction results will be obtained in the edge plane and higher accuracy prediction results will be obtained through historical information in the cloud plane. Third, an incremental learning approach based on updating partial parameters of LTCN is discussed to improve the accuracy of prediction models with newly collected data. Experiments show that our method can improve the prediction accuracy and reduce the computational time of RUL.

Social Internet of Things (SIoT), integrating the social networks and Internet of Things (IoT), leads to heterogeneous interactions of thing to thing, human to human, and human to thing, which in turn generates exploded information. Hence, as the soul of SIoT, information with its interaction and diffusion, records the track of humans and things and contains the hidden value for social administration and people's lives. Therefore, how to characterize the interplay between behavior spreading and information diffusion in SIoT is essential to predict and manage the information. Motivated by this, a more comprehensive understanding of the coupled modeling of social interaction and information diffusion processes in SIoT is conceived first. With the widespread adoption of cloud-edge computing, different nodes have different consciousness on information. Hence, a cloud-edge-aided information diffusion model is proposed for efficient interactions, which incorporates the role of edge in timely processing and feedback. On this basis, a blockchain-based cloud-edge SIoT architecture is proposed for traceability and security of information diffusion. Furthermore, the dynamical analysis of the coupled model in SIoT is provided, which illustrates the outbreak threshold, stability, and scale of information propagation. An interesting finding is that interactive behavior spreading only influences the final size of information propagation, not the spreading threshold. Extensive simulation results and detailed performance analysis verify the theoretical results, which are beneficial to provide traceable dissemination so as to find the most influential node and control the scale of information diffusion.

Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method.

In the modern industry, the information has been sufficiently shared among the production equipment, intelligent subsystems, and mobile devices via advanced network technology. For this purpose, many challenges on plant-wide performance evaluation such as product quality prediction have been received considerable attention in complex industrial Internet of Things systems. In this article, an efficient and effective soft sensor based on the semisupervised parallel deepFM model is proposed for the product quality prediction. First, a label broadcasting method is presented to augment labeled samples from unlabeled samples. Then, a data binning method is introduced to discretize process variables for an unbiased estimation. Based on the modified deepFM model, quality information can be separately extracted from different components of the model while high- and low-dimensional features can be obtained. Manifold regularization is embedded into the back propagation algorithm, in which unlabeled samples issue can be further resolved. Experiments on a real-world dataset demonstrate the effectiveness and performance of the proposed methods.

Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial manufacturing through industrial intelligence by incorporating production equipment, mobile terminals, and smart devices with wireless or wired networks. However, industrial visual information, such as images, videos, graphs, and texts, generated and collected from the industrial processes, contains various kinds of hidden value for industrial intelligence. Therefore, for the trend of providing ubiquitous industrial intelligence, new paradigms of perception and processing technologies of visual information such as recognition methods are required. However, industrial visual information is heterogeneous and complex with multiattributes, which presents significant challenges on visual information perception and processing technologies such as multiattributes recognition method. In this article, to provide industrial intelligence, a tensor-based visual feature recognition method is used to recognize the object from the perspective of multiattributes with the combination of attributes. To demonstrate its practical implementation, a case study about the industrial intelligence on the faulty location and diameter of bearings in the IIoT is described. Also, experiments on object recognition are carried out on the public image set COIL-100 to demonstrate the performance of the proposed method.

The industrial Internet of Things (IIoT) is growing quickly due to increasing deployment and integration of smart sensors, instruments, and devices, and software using wired or wireless networks. Through this integrated hardware-software approach, industrial practices will improve significantly, resulting in industrial intelligence for more efficient manufacturing. To realize such industrial intelligence, significant developments in IIoT big data processing and analysis are required to uncover and use hidden essential and valuable information of the production process. But large-scale, streaming, multiattribute IIoT data from production processes are noisy and have redundancies. Therefore, a suitable data processing technique such as tensor-train that can handle these IIoT data is needed. However, existing tensor-train decomposition methods are inefficient and cannot meet the processing demands of the large-scale IIoT big data. In this article, we propose an advanced (improved and highly efficient) distributed tensor-train (ADTT) decomposition method with its incremental computational method for processing IIoT big data. Finally, experiments are carried out on a typical and publicly available IIoT dataset - the bearing test data to verify and measure the performances of the proposed ADTT method.

Smart city, a focus of many researchers from academia and industry, is a successful example of Cyber-Physical-Social Systems (CPSS). Based on the rapid and efficient processing of large-scale data, Smart city, an example of CPSS, has revolutionized the service provision model by providing proactive services for humans. However, to operationalize the services provided in smart cities, a comprehensive analysis of heterogeneous and large-scale big data is required. Further, to speed up data processing and improve the adaptability and extensibility of big data, CPSS big data processing should be realized in the form of blocks and avoid redundant computing on historical data. In this paper, as an extension of multi-order distributed and incremental High-Order Singular Value Decomposition (HOSVD) computing, Ring-based Tree algorithm and Tree-based Tree algorithm are proposed for the problems of increasing scale of processable data and computational efficiency. The experimental and simulation results demonstrate that the proposed algorithms have high performance in terms of error, improvement factor, and improvement factor ratio. At last, to demonstrate the performance of our improved algorithms, a case study about CPSS big data processing is provided.

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big services, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation and experimental results from real-life, the high-efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social system data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.

Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. This article aims to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition. First, a tensor-based recommendation and service framework for the streaming educational big data is developed. Then three local tensors concerning learners, resources, and learning records are constructed and further fused to an integrated global learner-resource tensor. Afterward, we present an incremental tensor-based correlative analysis and personalized recommendation (ITCA-PR) algorithm to recommend appropriate resources under various contexts. Besides, we also propose an incremental tensor-based adaptive clustering and community recommendation (ITAC-CR) algorithm to recommend suitable learning partners under various contexts and accordingly construct adaptive learning communities. Extensive experimental results demonstrate that the ITCA-PR algorithm outperforms state-of-the-art improved collaborative filtering algorithms by F-score measurement and the ITAC-CR algorithm has a better clustering performance by DVI measurement. These precise educational services will promote the development of sustainable smart education.

C yber- P hysical- S ocial S ystems (CPSS) integrating the cyber, physical, and social worlds is a key technology to provide proactive and personalized services for humans. In this paper, we studied CPSS by taking h uman- i nteraction-aware b ig d ata (HIBD) as the starting point. However, the HIBD collected from all aspects of our daily lives are of high-order and large-scale, which bring ever-increasing challenges for their cleaning, integration, processing, and interpretation. Therefore, new strategies for representing and processing of HIBD become increasingly important in the provision of CPSS services. As an emerging technique, tensor is proving to be a suitable and promising representation and processing tool of HIBD. In particular, tensor networks, as a significant tensor decomposition technique, bring advantages of computing, storage, and applications of HIBD. Furthermore, T ensor- T rain (TT), a type of tensor network, is particularly well suited for representing and processing high-order data by decomposing a high-order tensor into a series of low-order tensors. However, at present, there is still need for an efficient Tensor-Train decomposition method for massive data. Therefore, for larger-scale HIBD, a highly-efficient computational method of Tensor-Train is required. In this paper, a d istributed T ensor- T rain (DTT) decomposition method is proposed to process the high-order and large-scale HIBD. The high performance of the proposed DTT such as the execution time is demonstrated with a case study on a typical form of CPSS data, C omputed T omography (CT) image data.

Radio frequency identification (RFID) systems, as one of the key components in the Internet of Things (IoT), have attracted much attention in the domains of industry and academia. In practice, the performance of RFID systems rather relies on the effectiveness and efficiency of anti-collision algorithms. A large body of studies have recently focused on the anti-collision algorithms, such as the Q-algorithm ( QA ), which has been successfully utilized in EPCglobal Class-1 Generation-2 protocol. However, the performance of those anti-collision algorithms needs to be further improved. Observe that fully exploiting the pre-processing time can improve the efficiency of the QA algorithm. With an objective of improving the performance for anti-collision, we propose a Nested Q-algorithm ( NQA ), which makes full use of such pre-processing time and incorporates the advantages of both Binary Tree ( BT ) algorithm and QA algorithm. Specifically, based on the expected number of collision tags, the NQA algorithm can adaptively select either BT or QA to identify collision tags. Extensive simulation results validate the efficiency and effectiveness of our proposed NQA (i.e., less running time for processing the same number of active tags) when compared to the existing algorithms.

With an objective to provide the proactive and personalized services for human beings, Cyber-Physical-Social Systems (CPSS), which combine the cyber space, physical space, and social space together, need to process the large scale heterogenous data first. Tensor, as an appropriate data representation tool, has been widely used for representation of heterogeneous Cyber-Physical-Social big data. When computationally processing such tensor, many necessary constraints have to be taken into account, e.g., the execution time, energy consumption, economic cost, security as well as reliability. However, the systematic integration of these constraints and then the modelling of general optimization for tensor processing become more challenging. In this paper, with such constraints being considered together, a general model for tensor computation that optimizes the execution time, energy consumption, and economic cost with acceptable security and reliability is proposed. From diverse perspectives of user requirements, a case study for the tree-based distributed High-Order Singular Value Decomposition (HOSVD) is measured. With the focus on multi-objective combination, the experimental results validate the applicability and generality of the proposed model.

Due to the rapid advances of information technologies, Big Data, recognized with 4Vs characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as many challenges. A major benefit of Big Data is to provide timely information and proactive services for humans. The primary purpose of this paper is to review the current state-of-the-art of Big Data from the aspects of organization and representation, cleaning and reduction, integration and processing, security and privacy, analytics and applications, then present a novel framework to provide high-quality so called Big Data-as-a-Service. The framework consists of three planes, namely sensing plane, cloud plane and application plane, to systemically address all challenges of the above aspects. Also, to clearly demonstrate the working process of the proposed framework, a tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system. Finally, some challenges about the proposed framework are discussed.

Cyber-physical-social systems (CPSS), integrating cyber, physical, and social spaces together, bring both conveniences and challenges to humans. For practical applications and user convenience, it is essential that the Big Data produced in CPSS be processed in real time. Therefore, Big Data computation should avoid redundant computations on historical data when dealing with periodic incoming data. In this paper, we propose a columnwise high-order singular value decomposition (HOSVD) algorithm to realize dimensionality reduction, extraction, and noise reduction for tensor-represented Big Data. First, the distributed HOSVD (DHOSVD) is proposed using the columnwise Jacobi-based approach to realize the distributed computation of HOSVD. Second, big streaming data are continuously produced and the intermediate results could be recorded for the next computational step. Third, we propose a similar columnwise incremental HOSVD (IHOSVD) scheme to support online computation on temporally incremental data streaming. The performance of the two HOSVD-based schemes will illustrate the scalability of our efficient real-time Big Data processing methods.