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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

Point Gated Attention

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Point Gated Attention

Chinese automation Congress (CAC)

作者： Haibin Wang Tian Wang Mengyi Zhang Baochang Zhang Peng Shi Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understandingof Scoial Safety Nanjing University of Science and Technology Nanjing China Institute of Artificial Intellgence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

Self-attention networks have achieved great success in traditional language processing, and have also made great progress in image classification and object detection. When analyzing the previous work, it is found that some point cloud information is lost in their feature extraction work, so we design a network to reduce the loss when extracting features. And through the feature extraction network - Gated Attention Unit instead of Transformer, to make the extraction of point cloud features more efficient. Based on Point Transformer, a new network structure named Point Gated Attention is used to classify point clouds. The overall_acc on the dataset ModelNet40 can reach 91.4%, and the average accuracy can reach 93.9%.

关键词： Point cloud compression automation Object detection Logic gates Transformers Feature extraction Data mining

Distributed formation control for manipulator end-effectors

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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu De Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain School of Automation Nanjing University of Science and Technology Nanjing210094 China

We present three classes of distributed formation controllers for achieving and maintaining the 2D/3D formation shape of manipulator end-effectors to cope with different scenarios due to availability of modeling parameters. We firstly present a distributed formation controller for manipulators whose system parameters are perfectly known. The formation control objective is achieved by assigning virtual springs between end-effectors and by adding damping terms at joints, which provides a clear physical interpretation of the proposed solution. Subsequently, we extend it to the case where manipulator kinematic and system parameters are not exactly known. An extra integrator and an adaptive estimator are introduced for gravitational compensation and stabilization, respectively. Simulation results with planar manipulators and with seven degree-of-freedom humanoid manipulator arms are presented to illustrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

Solving non-cyclic dynamic multi-objective optimization problems via GRU prediction and multi-information hybrid exploration

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Journal of Supercomputing 2025年第8期81卷

作者： Sun, Hao Bai, Xiaochuang Wang, Cong Yang, He Hu, Ziyu School of Electrical and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066004 China Electrical automation department Tianjin Research Institute of Electric Science Co. Ltd. Tianjin China

In non-cyclic dynamic multi-objective optimization problems, the non-cyclic nature of environmental changes may cause the Pareto optimal front (PF) to be different from historical times. In addition, changes may also occur on the Pareto optimal solutions set (PS). However, predictions based solely on a single space show lower accuracy due to insufficient information. Therefore, solving non-cyclic dynamic multi-objective optimization problems via gate recurrent unit (GRU) prediction and multi-information hybrid exploration (GPMHE) is designed to overcome the above challenges. In the objective space, the GRU-based prediction strategy (GP) captures patterns of population change, predicts its distribution in new environments, and subsequently maps the results to the decision space;The multi-information hybrid exploration strategy (MHE) takes the feature individual as the representative individual, adaptively guides the population evolution direction in the decision space, thereby enhancing the algorithm’s adaptability. Compared with five advanced algorithms on the non-cyclic dynamic benchmark test set (NCD), the proposed algorithm GPMHE has shown robust adaptation to dynamic environmental changes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

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Science China(Information Sciences) 2019年第9期62卷 144-157页

作者： Guoxing WEN C.L.Philip CHEN Hui DOU Hongli YANG Chunfang LIU College of Science Binzhou University Department of Computer and Information Science Faculty of Science and Technology University of Macau College of Navigation Dalian Maritime University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Mathematics and Systems Science College Shandong University of Science and Technology

This paper addresses the obstacle avoidance problem of formation control for the multi-agent systems modeled by double integrator dynamics under a directed interconnection topology. The control task is finished by a leader-follower formation scheme combined with an artificial potential field(APF) *** leader-follower scheme is carried out by taking the desired trajectory with the desired velocity as virtual leader, while the APF method is carried out by dealing with the obstacles as the high potential points. When the obstacle avoidance tasks are finished, the artificial potential forces degrade the formation performance, so their undesired effects are treated as disturbances, which is analyzed by the robust H∞performance. Based on Lyapunov stability theory, it is proved that the proposed formation approach can realize the control objective. The result is also extended to the switching multi-agent formation. The effectiveness of the proposed formation scheme is further confirmed by simulation studies.

关键词： formation control obstacle avoidance artificial potential field method H_∞ performance second-order multi-agent system directed topology

Distributed formation control of manipulators' end-effector with internal model-based disturbance rejection

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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu de Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain The School of Automation Nanjing University of Science and Technology Nanjing210094 China

This paper addresses the problem of end-effector formation control for manipulators that are subjected to external disturbances: input disturbance torques and disturbance forces at each end-effector. The disturbances are assumed to be non-vanishing and are superposition of finite number of sinusoidal and step signals. The formation control objective is achieved by assigning virtual springs between end-effectors, by adding damping terms at joints, and by incorporating internal model-based dynamic compensators to counteract the effect of the disturbances;all of which presents a clear physical interpretation of the proposed approach. Simulation results are presented to illustrate the effectiveness of the proposed approach. © 2021, CC BY.

关键词： End effectors

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

Robust Iterative Learning control for Spatially Interconnected Systems using 2D control Theory

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Robust Iterative Learning Control for Spatially Interconnect...

European control Conference (ECC)

作者： Bartłomiej Sulikowski Krzysztof Gałkowski Daniel Trzciński Eric Rogers Anton Kummert Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Department of Electronics and Computer Science University of Southampton Southampton UK Faculty of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany

This paper develops a class of iterative learning control (ILC) laws for a subclass of uncertain spatially interconnected systems. Model uncertainties appear both in the state and the output equation of the dynamics modeled in the 2D systems setting to which existing results are not applicable. The first stage is to write the dynamics in 2D model form. Then the stability theory for a distinct class of 2D systems known as repetitive processes is used to develop the ILC law in the case of differential dynamics. This analysis results in a design algorithm that can be applied using linear matrix inequalities.

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Soybeannet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (Uav) Images

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SSRN

SSRN 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute & Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source at https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images and the labeled soybean dataset at https://***/datasets/jiajiali/uav-based-soybean-pod-images, offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, The Authors. All rights reserved.

关键词： Statistical tests

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

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arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification