IEEE/CAA Journal of Automatica Sinica

Editorial: Evolution from AI, IoT and Big Data Analytics to Metaverse

MengChu Zhou

2022, 9(12): 2041-2042. doi: 10.1109/JAS.2022.106100

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The Metaverse of Mind: Perspectives on DeSci for DeEco and DeSoc

Fei-Yue Wang

2022, 9(12): 2043-2046. doi: 10.1109/JAS.2022.106106

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Parallel Sensing in Metaverses: Virtual-Real Interactive Smart Systems for "6S" Sensing

Yu Shen, Yuhang Liu, Yonglin Tian, Xiaoxiang Na

2022, 9(12): 2047-2054. doi: 10.1109/JAS.2022.106115

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In the construction of Metaverses, sensors that are referred to as the "bridge of information transmission", play a key role. The functionality and efficiency of today's sensors, which operate in a manner similar to physical sensing, are frequently constrained by their hardware and software. In this research, we proposed the Parallel Sensing framework, which includes background, concept, basic methods and typical application of parallel sensing. In our formulation, sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence, in order to boost the performance of the sensors. Each sensor will have a parallel counterpart in the virtual world within the framework of parallel sensing. Digital sensors serve as the brain of sensors and maintain the same properties as physical sensors. Parallel sensing allows physical sensors to operate in discrete time periods to conserve energy, while cloud-based descriptive, predictive, and prescriptive sensors operate continuously to offer compensation data and serve as guardians. To better illustrate parallel sensing concept, we show some example applications of parallel sensing such as parallel vision, parallel point cloud and parallel light fields, both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception models. Experimental results demonstrate the effective of parallel sensing framework. The interaction between the real and virtual worlds enables sensors to operate actively, allowing them to intelligently adapt to various scenarios and ultimately attain the goal of "Cognitive, Parallel, Crypto, Federated, Social and Ecologic" 6S sensing.

DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

Xiujuan Wang, Mengzhen Kang, Hequan Sun, Philippe de Reffye, Fei-Yue Wang

2022, 9(12): 2055-2062. doi: 10.1109/JAS.2022.106103

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The demand for food is tremendously increasing with the growth of the world population, which necessitates the development of sustainable agriculture under the impact of various factors, such as climate change. To fulfill this challenge, we are developing Metaverses for agriculture, referred to as AgriVerse, under our Decentralized Complex Adaptive Systems in Agriculture (DeCASA) project, which is a digital world of smart villages created alongside the development of Decentralized Sciences (DeSci) and Decentralized Autonomous Organizations (DAO) for Cyber-Physical-Social Systems (CPSSs). Additionally, we provide the architectures, operating modes and major applications of DeCASA in AgriVerse. For achieving sustainable agriculture, a foundation model based on ACP theory and federated intelligence is envisaged. Finally, we discuss the challenges and opportunities.

Parallel Manufacturing for Industrial Metaverses: A New Paradigm in Smart Manufacturing

Jing Yang, Xiaoxing Wang, Yandong Zhao

2022, 9(12): 2063-2070. doi: 10.1109/JAS.2022.106097

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To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems (CPSSs) and the Internet of Minds (IoM) are regarded as its infrastructures and the "Artificial systems", "Computational experiments" and "Parallel execution" (ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R & D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation (KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations (DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case, especially the unmanned production line named FlexVega.

Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

Yutong Wang, Yonglin Tian, Jiangong Wang, Yansong Cao, Shixing Li, Bin Tian

2022, 9(12): 2071-2078. doi: 10.1109/JAS.2022.106091

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With the rapid development of information technologies such as digital twin, extended reality, and blockchain, the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection (AutoQI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing (QoM), Quality of Products, Quality of Processes (QoP), Quality of Systems, and Quality of Services (QoS). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottom-up inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection, we propose Quality Transformers to complete detection task, while federated learning is integrated to regulate data sharing. In summary, we point out the development directions of quality inspection under metaverse tide.

Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

Jingwei Lu, Xingxia Wang, Xiang Cheng, Jing Yang, Oliver Kwan, Xiao Wang

2022, 9(12): 2079-2086. doi: 10.1109/JAS.2022.106094

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The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence, internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems, QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called EuArtisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems — An Overview

Teng Li, Xiaodong Sun, Gang Lei, Zebin Yang, Youguang Guo, Jianguo Zhu

2022, 9(12): 2087-2105. doi: 10.1109/JAS.2022.105851

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Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

An Overview of Finite/Fixed-Time Control and Its Application in Engineering Systems

Yang Liu, Hongyi Li, Zongyu Zuo, Xiaodi Li, Renquan Lu

2022, 9(12): 2106-2120. doi: 10.1109/JAS.2022.105413

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The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Linfeng Tang, Yuxin Deng, Yong Ma, Jun Huang, Jiayi Ma

2022, 9(12): 2121-2137. doi: 10.1109/JAS.2022.106082

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Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition, the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks. This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named SuperFusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion, and semantic segmentation tasks demonstrate the superiority of our SuperFusion compared to the state-of-the-art alternatives. The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/SuperFusion.

A Bio-Inspired Flapping-Wing Robot With Cambered Wings and Its Application in Autonomous Airdrop

Haifeng Huang, Wei He, Qiang Fu, Xiuyu He, Changyin Sun

2022, 9(12): 2138-2150. doi: 10.1109/JAS.2022.106040

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Flapping-wing flight, as the distinctive flight method retained by natural flying creatures, contains profound aerodynamic principles and brings great inspirations and encouragements to drone developers. Though some ingenious flapping-wing robots have been designed during the past two decades, development and application of autonomous flapping-wing robots are less successful and still require further research. Here, we report the development of a servo-driven bird-like flapping-wing robot named USTBird-I and its application in autonomous airdrop. Inspired by birds, a camber structure and a dihedral angle adjustment mechanism are introduced into the airfoil design and motion control of the wings, respectively. Computational fluid dynamics simulations and actual flight tests show that this bionic design can significantly improve the gliding performance of the robot, which is beneficial to the execution of the airdrop mission. Finally, a vision-based airdrop experiment has been successfully implemented on USTBird-I, which is the first demonstration of a bird-like flapping-wing robot conducting an outdoor airdrop mission.

Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

Cong Liu

2022, 9(12): 2151-2160. doi: 10.1109/JAS.2022.106109

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Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

Wen Hu, Zejian Deng, Dongpu Cao, Bangji Zhang, Amir Khajepour, Lei Zeng, Yang Wu

2022, 9(12): 2161-2173. doi: 10.1109/JAS.2022.106049

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To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index (AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Yuanxun Zheng, Qinghua Li, Changhong Wang, Xiaoguang Wang, Lifeng Hu

2022, 9(12): 2174-2185. doi: 10.1109/JAS.2021.1004144

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Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.