With the increasing demand for housing renovation and demolition, the amount of building demolition waste is rising year by year. However, due to the low recycling and reusing rate, a large proportion of waste is disposed of by landfill, which has a great impact on the environment. In addition, compared with other industries, the level of digitalization and intelligence in the construction industry is still relatively low. Facing these problems, this paper aims to explore a market-oriented circulation channel for building demolition waste and to achieve it from a technical perspective. Firstly, a digital twin-enabled building demolition waste trading workflow is proposed. The digital twin model could integrate, analyze, and display demolition-related data in real time, which is the basis of building demolition waste trading. Through the two-way information sharing mechanism, contractors and recyclers could quickly get in touch and know each other’s products and needs. Secondly, an innovative building demolition waste trading platform has been developed utilizing cutting-edge technologies, including robotics, big data, IoT, and 3D printing. Furthermore, to cater to the diverse needs of various stakeholders, a dedicated management platform for logistics providers and a comprehensive monitoring platform for the government are developed.

To promote the sharing of medical data assets (MDAs) in a more secure and sustainable manner, this article presents a blockchain-based MDA sharing framework. The contributions of this article are threefold. First, we designed a layered-architecture to decouple the privacy-preserving responsibilities among technologies considering the incentive rewarding and parallelization of execution. Second, we introduce zero-knowledge proofs (ZKPs) in smart contracts with a group signature to construct a supervisory privacy-preserving sharing mechanism, which can be executed in a decentralized environment to protect the privacy of MDAs. Third, we introduce an incentive mechanism that motivates MDA sharing by capturing the decentralized features of the participants to deliver fair rewards. The experiments show that our framework achieves a comprehensive privacy protection on sharing MDAs, comparing with single blockchain sharing schema, with only 2.2% sacrifice on TPS (throughput/second). Moreover, our framework has better potential for large-scale application due to the paralleled execution on ZKP-based smart contracts.

Logistics interfaces with manufacturing throughout the entire production process need synchronous operations. For achieving integrated organization and operations between manufacturing and logistics, this article introduces the concept of shop-floor logistics and manufacturing synchronization with four principles, including: 1) synchronization-oriented manufacturing system; 2) synchronized information sharing; 3) synchronized decision making; and 4) synchronized operations. The marriage of the principles rendered the development of an overall framework of the Industrial Internet of Things (IIoT) and digital twin-enabled graduation intelligent manufacturing system (GiMS). A mixed-integer programming-based synchronization mechanism is proposed under GiMS. To meet the requirement of fast decision making in real-life shop-floor logistics and manufacturing synchronization problems, an equivalent constraint programming model is developed and tested. The observation and analysis of the case company show the advantage of the proposed concept and approach with the best performance regarding key performance indicators. The concept of synchronization provides an insight for understanding the interaction of logistics and manufacturing at the operational level. This article potentially enables manufacturers to reevaluate and develop their manufacturing planning and control strategies in the IIoT and digital twin-based manufacturing environment.

Detection algorithms play an important role in the life-cycle management of smart vision-based defect detection equipment. This article proposes a brain-inspired interpretable network pruning method for smart detection equipment for online defect detection scenarios. A brain-inspired neuronal circuit decomposition model is constructed from the view of the structure physics of artificial neural networks. To meet the real-time requirements, an interpretable network pruning is proposed in three steps: First, a full-size basic convolutional neural network is constructed. Second, the convolutional neural circuit's extraction method based on a genetic algorithm is designed to evaluate the function of different neural units. Third, a pruning method is proposed to eliminate the redundant convolutional neural circuits and retain key units to balance the accuracy and time efficiency. The experimental results demonstrated the proposed pruning method can improve the frame-pre-second by 116% on the premise of maintaining the detection accuracy of 92%.

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.

Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs.Note to Practitioners—MCSA in CMfg is a complicated and challenging task. The current industrial applications rely on optimization methods, such as all-in-one methods (e.g., genetic algorithm) and multidiscipline design optimization methods (e.g., analytical target cascading), to allocate MCSs. While these methods can help cloud operators obtain feasible allocation solutions rapidly, there exist several disadvantages: 1) they can only solve problems in one-sided settings (one customer with many providers); 2) there is an assumption that the customer’s demand can be provided by any providers, which means providers’ capacities are infinite; and 3) the problem is optimized based on the fixed prices provided by providers, without considering supply and demand. This article presents two multiunit double auction mechanisms that allow multiple customers to trade with multiple providers. Allocation rules can break the assumption and providers’ capacities are limited. MCSs are also priced dynamically based on supply and demand. We show how cloud operators apply two mechanisms first and demonstrate their properties and bidding strategies of customers and providers when the mechanism is not incentive compatible. Finally, our experiments and analysis fully validate the applicability and efficiency of two mechanisms and illustrate how cloud operators can maximize their utilities.

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.

In a paced aircraft final assembly line, some disturbances can be collected timely on the basis of the cyber-physical production system. In order to reduce the execution deviation, some workers need to switch among stations after a fixed period. Thus, a worker allocation problem with the multistage workstation is introduced first. Then, an integer programming formulation is presented to formulate the problem with the objective of shortest workstation cycle and the workload balance of both stations and workers. Moreover, a modified nondominated sorting genetic algorithm (NSGA-IV) is proposed to solve it, which tradeoff the convergence and the population diversity in the decision space. Finally, the NSGA-IV algorithm compares with five multiobjective evolutionary algorithms in a real-world case. Compared with manual allocation, the takt time of an aircraft final assembly line is reduced by 20.86% by using the NSGA-IV algorithm.

Object segmentation for 3-D point clouds plays a critical role in autonomous driving, robotic navigation, and other computer version applications. In object segmentation, all points are considered to be equal of importance in the literature. However, unequal cases exist and a segmentation boundary is mainly determined by neighbor points. To investigate point inequivalence, in this article, an unequal learning approach is proposed to integrate gene expression programming (GEP) and a deep neural network (DNN). GEP is designed to discover the inequivalent function, which measures the importance of different points according to the distances to the segmentation boundary. A cost sensitive learning method is improved to guide the DNN to obtain the loss of different points unequally with the discovered inequivalent function during model training. The experimental results reveal that point inequivalence with respect to boundary distance exists and is helpful to improve the accuracy of object segmentation.

As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data.

Aiming to improve the adaptability of fabric defect detection, this paper proposes an “edge-cloud” collaborative fabric defect detection architecture that contains edge layer, platform layer, and application layer. In the edge layer, the fabric defect detection machine is able to realize the collection and detection of fabric images data. In the platform layer, the cloud platform that integrates memory computing, parallel storage, and a relational library is designed to realize the efficient storage and analysis of fabric data. In the application layer, a deep learning fabric defect detection algorithm is designed to recognize the defect patterns. The interaction between the cloud platform and the detection device is designed to adaptively adjust the detection algorithm. The closed-loop optimization is achieved by implementing “edge-cloud” architecture that the fabric pictures are captured and analyzed for fast detection algorithm in edge devices. The captured data is stored and monitored by the cloud platform. The cloud platform adjusts the edge detection algorithm by transfer learning, which can adapt to the changing environment. A case study illustrates that the proposed edge-cloud collaborative fabric defect detection can achieve better dynamic adaptability.

The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID), as one of the key IoT technologies, has been used to collect real-time production data to support the manufacturing decision-making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID-enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes). Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data.

Automated guided vehicles (AGVs) have been largely used in manufacturing and supply chain management. With the development of Auto-ID technologies like radio frequency identification (RFID), AGVs' positioning could be enhanced. This paper demonstrates using magnetic field lines in the AGVs for precise coverage locating based on the errors' suppression positioning method. Dolph-Chebyshev antenna array is used to enable AGVs with more precise location implementation. It is observed that the far-field active RFID system positioning accuracy is higher, the movement is more stable, and the fluctuating rate is smaller.

Radio Frequency Identification (RFID) technology has been widely used in manufacturing sites for supporting the shopfloor management. Huge amount of RFID-enabled production data has been generated. In order to discover invaluable information and knowledge from the RFID big data, it is necessary to cleanse such dataset since there is large number of noises. This paper uses n-dimensional RFID-Cuboids to establish the data warehouse. A big data cleansing approach is proposed to detect, remove and tidy the RFID-Cuboids so that the reliability and quality of dataset could be ensured before knowledge discovery. Experiments and discussions are carried out for validating the proposed approach. It is observed that the proposed big data cleansing approach outperforms other methods like statistics analysis in terms of finding incomplete and missing cuboids.