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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Optimization of Cube Storage Warehouse Scheduling Using Genetic Algorithms

Optimization of Cube Storage Warehouse Scheduling Using Gene...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Won Yong Ha Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA

In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time.

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jawhar Ghommam Raouf Fareh Ibrahim Al Naimi Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Laboratory of Control Energy Management ENIS Tunisa Electrical Engineering Dept. University of Sharjah UAE

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach.

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Differentially Private Online Federated Learning with Correlated Noise

Differentially Private Online Federated Learning with Correl...

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IEEE Conference on Decision and control

作者： Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.

关键词： Privacy Federated learning Heuristic algorithms Noise Signal processing algorithms Servers

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Actuator Fault Reconstruction for a Class of OSL Nonlinear Systems 21

Actuator Fault Reconstruction for a Class of OSL Nonlinear S...

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21st IEEE International Conference on Sciences and Techniques of Automatic control and computer engineering, STA 2022

作者： Naifar, Omar Gassara, Hamdi Elloumi, Mourad Jmal, Assaad Electrical Engineering Department BP 1173 Sfax3038 Tunisia National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Technology of Automatic Control and Computer Engineering P. O. Box 1173 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying problematic physical characteristics of the system, which is a significant shortcoming. In this work, we offer a way for estimating the incorrect physical parameters of actuators using an adaptive observer strategy. To demonstrate the utility of the suggested method, a numerical example and simulation research are provided. © 2022 IEEE.

关键词： Actuators

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Data-Driven Model Predictive control for Virtually Coupled Train Set Using Behavioral Systems Theory 27

Data-Driven Model Predictive Control for Virtually Coupled T...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Luo, Xiaolin Wang, Dongming Tang, Tao Zhang, Yong Liu, Hongjie Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China University of California Department of Electrical and Computer Engineering RiversideCA92521 United States School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Automation and Intelligence Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China

ISBN: (纸本)9798331505929

Virtual coupling is effective to improve the flexibility and efficiency of railway services. It forms multiple train units as a virtually coupled train set (VCTS). To separate units safely by a minimal distance, relative-braking distance (RBD) is employed. However, it is numerically calculated without explicit models in practice, which makes the design of VCTS control approaches challenging. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach that deploys behavioral systems theory and does not need an explicit model of RBD. Specifically, Hankel matrices are constructed to formulate the unknown dynamics of VCTS, based on the prior-measured trajectories of VCTS operation. Then, using the past data and information received from the preceding unit, the controller of each unit is yielded by solving a computationally-efficient optimal control problem. Finally, experiments are conducted, and results show that our approach can stably operate VCTS, and saves by over 99% computation time than the nonlinear model predictive control approach. © 2024 IEEE.

关键词： Predictive control systems

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State of the Art of Repetitive control in Power Electronics and Drive Applications

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2022年 3卷 13-29页

作者： Tang, Mi Di Benedetto, Marco Bifaretti, Stefano Lidozzi, Alessandro Zanchetta, Pericle Power Electronics Machine and Control Group University of Nottingham NottinghamNG7 2GT United Kingdom Roma Tre University C-PED Center for Power Electronics and Drives Rome00154 Italy Department of Industrial Engineering University of Rome 'Tor Vergata' Rome00133 Italy Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy

Power electronic systems present a non-linear behavior mainly due to their switching nature. This is often combined with their interaction with non-linear systems, such as other switching converters, diode rectifiers, motor drives, etc. and with possible non linearities of the power grid in the case of grid connected systems. The major effect of these non-linear interactions is the generation of harmonic distortion on voltages and currents (both in DC and AC), which needs to be compensated to achieve high power quality systems. The use of passive filters is often the simplest and most immediate solution;however, this decreases converter efficiency and increases its weight and volume. Thus, the use of a control strategy capable of tracking periodic signals, rejecting periodic disturbance and largely improving steady state behavior and harmonic distortion with a limited bandwidth is a very desirable feature. Repetitive control (RC) represents an extremely practical and efficient solution for the aforementioned issues, and it is widely employed in many different applications. This paper focuses on state of the art of RC used in power electronics and drives. RC basic concepts, different control structures, design methods, fixed and variable frequency operating conditions, etc. are investigated. Furthermore, many example applications and existing control approaches developed in recent years for power electronics and drive systems based on RC, have also been discussed in detail. © 2020 IEEE.

关键词： Electric drives

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HapticFormers: Utilizing Transformers for Avocado Maturity Grading through Vision-based Tactile Assessment

HapticFormers: Utilizing Transformers for Avocado Maturity G...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Israa Fahmy Taimur Hassan Irfan Hussain Naoufel Werghi Lakmal Seneviratne Khalifa University Center for Robotics and Autonomous Systems (KUCARS) Khalifa University UAE Department of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE

Detecting maturity of fruits and vegetables, especially avocados, is a critical task in modern agriculture and supply chain management. Moreover, the accurate assessment of maturity can improve the harvesting time and ensure consistent quality for consumers through the supply chain process. A key approach to achieving this is the non-destructive estimation of produce quality. Vision-Based Tactile Sensing (VBTS) technologies, which mimic human tactile perception, offer a novel approach to address this challenge. This paper focuses on the use of two notable VBTS technologies, GelSight and Facebook’s DIGIT sensor. Using these technologies, we developed two novel datasets that assess the avocado maturity using the transformer models, marking a novel contribution in this area. We adapted several transformer architectures to the task, conducting experiments on both image classification and regression to estimate avocado firmness. Among the variants tested, the PoolFormer displayed notable results with accuracy of 92% in detecting avocado maturity level when used with tactile data. The datasets and code used in this study will be shared at this URL.

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Privacy-Preservation for Distributed Quantized k-means Clustering

Privacy-Preservation for Distributed Quantized k-means Clust...

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Annual Allerton Conference on Communication, control, and Computing

作者： Apostolos I. Rikos Nicolas E. Manitara Gabriele Oliva Christoforos N. Hadjicostis Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node’s state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph.

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Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks

SSRN

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SSRN 2024年

作者： Wang, Tonghao Gao, Shijiao Huo, Yukang Cattani, Piercarlo Mei, Shuli College of Information and Electrical Engineering China Agricultural University Beijing100083 China Department of Computer Control and Management Engineering Sapienza - Università di Roma Roma Italy

Reinterpreting wavelet multi-resolution analysis as CNN methods to endow them with the capacity for high-level semantic feature extraction has emerged as a research topic in deep sparse representations. We explore the fundamental operations of wavelet transform and convolution from a projective standpoint, then design a highly interpretable, exceptionally lightweight, and fully learnable deep neural network architecture known as Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks (DSAWCN). This network acquires wavelets filters customized for specific image tasks, thus providing adaptive and efficient multi-scale feature extraction strategies. We examined the behavior and the impact of the parameters in the proposed method using three general texture image datasets and four bark texture image datasets. The findings indicate that this exceptionally lightweight network surpasses current wavelet convolutional networks in terms of classification accuracy and achieves performance comparable to some eminent CNN models. © 2024, The Authors. All rights reserved.

关键词： Convolution

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