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第40届中国控制会议

作者： Huanhuan Li Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering New York University

This paper presents a new distributed formation control method based on abstraction for nonlinear uncertain *** on a modified concept of approximate abstraction, each agent is first abstracted as a first-order integrator by designing an interface system. Then, the distributed formation controller is designed for first-order integrators and proved to be *** result illustrates the effectiveness of the proposed design.

关键词： Distributed control formation control hierarchical control approximate abstraction small-gain approach

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An Improved YOLOv5 Detection Method for Strip Surface Defect

An Improved YOLOv5 Detection Method for Strip Surface Defect

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International Conference on automation and Computing (ICAC)

作者： Yefeng Liu Longfei Yu Qichun Zhang dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent CNC Machine Tool Manufacturing Shenyang Institute of Technology dept. School of Automation and Electrical Fushun China dept. School of Automation and Electrica Engineering Shenyang Ligong University dept. Department of Computer Science University of Bradford Bradford U. K.

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm.

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

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New Results in Stabilization of Uncertain Nonholonomic Systems: A Self-Triggered Control Approach

New Results in Stabilization of Uncertain Nonholonomic Syste...

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第40届中国控制会议

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering New York University

The self-triggered event-based control of uncertain nonholonomic systems in the chained form is studied for the first time in this paper. In order to tackle the effects of drift uncertain nonlinearities, nonholonomic constraints and nonsmooth aperiodic sampling in event-based control, a novel systematic design scheme is proposed by integrating set-valued maps with state-separation and state-scaling techniques. The stability analysis of the closed-loop self-triggered control system is based on the cyclic-small-gain theorem that overcomes the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems. More specifically, the closed-loop self-triggered control system is transformed into an interconnection of multiple input-to-state stable systems, to which the cyclic-small-gain theorem is applied for robust stability analysis. Interestingly, the proposed design approach is also applicable to a broader class of nonholonomic systems subject to state and input-dependent uncertainties, and the result is still new even if the plant is reduced to the ideal unperturbed chained form. The main result is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in orientation.

关键词： Nonholonomic systems self-triggered control robust stabilization

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SVM-based Extended Kalman Filter State Estimation of Biped Robot

SVM-based Extended Kalman Filter State Estimation of Biped R...

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IEEE International Conference on robotics and Biomimetics

作者： Chengzhi Gao Ye Xie Shiqiang Zhu Guanyu Huang Lingyu Kong Anhuan Xie Jianjun Gu Dan Zhang Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada York University North York Toronto Canada

ISBN: (纸本)9781665481106

At present, more and more attention has been paid to the exploration of biped robots. As the precondition of biped stable walking, the state estimation of robot has also received the attention of many experts. Biped robot has special motion characteristics, has a hybrid dynamic system and belongs to a floating base, so it must obtain the contact information of two feet before realizing the motion state estimation of the robot. The contribution of this paper is to introduce soft support vector machine to detect contact phase. This method based on support vector machine (SVM) eliminates the need for force sensors on two feet because they are vulnerable to the impact between the foot and the contact surface. In addition, precise dynamic modeling is not required. Furthermore, this paper uses SVM to obtain the touchdown information, and proposes a state estimation method using extended Kalman filter to combine motion estimation with IMU data. Finally, the touchdown detection algorithm and the state estimation algorithm are verified through experiments, and the accuracy and feasibility of the SVM method are proved through the touchdown detection experiments. Based on this, the full state estimation of the robot is realized through the state estimation experiments.

关键词： Support vector machines Legged locomotion Technological innovation Motion estimation Switches Robot sensing systems Kalman filters

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DeceFL:a principled fully decentralized federated learning framework

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National Science Open 2023年第1期2卷 35-51页

作者： Ye Yuan Jun Liu Dou Jin Zuogong Yue Tao Yang Ruijuan Chen Maolin Wang Chuan Sun Lei Xu Feng Hua Yuqi Guo Xiuchuan Tang Xin He Xinlei Yi Dong Li Guanghui Wen Wenwu Yu Hai-Tao Zhang Tianyou Chai Shaochun Sui Han Ding School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Applied Mathematics University of WaterlooWaterloo N2L 3G1Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Mathematical and Physical Sciences Wuhan Textile UniversityWuhan 430200China School of Electrical Engineering and Computer Science and Digital FuturesKTH Royal Institute of TechnologyStockholm 10044Sweden Shenyang Institute of Automation(SIA) Chinese Academy of SciencesShenyang 110169China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu 610091China

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently *** a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy *** there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central *** we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning *** has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly ***,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.

关键词： decentralized federated learning smart manufacturing control systems privacy

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI 11

An Explainable Convolutional Neural Networks for Automatic S...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Liu, Jun Deng, Feng Yuan, Geng Lin, Xue Song, Houbing Wang, Yanzhi Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781643682402

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high;Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes;explainable CNNs remains a challenge;explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient. © 2022 The authors and IOS Press.

关键词： Image segmentation

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Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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Kombucha electronics: electronic circuits on kombucha mats (vol 13, 9367, 2023)

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SCIENTIFIC REPORTS 2023年第1期13卷 1-10页

作者： Adamatzky, Andrew Tarabella, Giuseppe Phillips, Neil Chiolerio, Alessandro D'Angelo, Pasquale Nikolaidou, Anna Sirakoulis, Georgios Ch. Unconventional Computing Laboratory University of the West of England Bristol UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Istituto Italiano di Tecnologia Center for Converging Technologies Soft Bioinspired Robotics Via Morego 30 16165 Genova Italy Institute of Materials for Electronic and Magnetism National Research Council (IMEM-CNR) Parma Italy

A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications.

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