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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Artificial Intelligence Thrust of the Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics.

关键词： Adaptation models Quantization (signal) Heuristic algorithms Bandwidth Distributed algorithms Optimization Convergence

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Exploring the Economic Feasibility of Advanced Air Mobility in the Early Stages

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

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Fractional controlling System of an Autonomous Locomotive Multifuel Engine 3

Fractional Controlling System of an Autonomous Locomotive Mu...

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3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020

作者： Ivaschenko, Anton Avsievich, Vladimir Avsievich, Alexandr Samara State Technical University Computer Engineering Department Samara Russia Samara State Transport University Transport Robotics Automation and Control Department Samara Russia

ISBN: (纸本)9781728163895

The paper presents the results of electronic control system development and study for the gas and diesel fuel supply for an autonomous locomotive multi-fuel engine. In modern engine control systems physical and software components are deeply connected, which makes it possible to implement digital control algorithms as a part of a cyber-physical system. This paper proposes an original fractional proportional-integral-differential control algorithm to increase the stability and quality of transients of a multi-fuel engine. The results of simulation and experimental probation prove the benefits of implementing this algorithm for the transition process time and the integral quality assessment in comparison with the classical algorithms, which allows reducing fuel consumption. © 2020 IEEE.

关键词： Two term control systems

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Learning from Human Uncertainty by Choquet Integral for Optic Disc Segmentation 21

Learning from Human Uncertainty by Choquet Integral for Opti...

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4th International Conference on control and computer Vision, ICCCV 2021

作者： Qiu, Hao Su, Pan Jiang, Shanshan Yue, Xingyu Zhao, Yitian Liu, Jiang Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China School of Control and Computer Engineering North China Electric Power University China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781450390477

Modern deep neural networks are able to beat human annotators in several medical image processing tasks. In practical manual annotation for medical image segmentation tasks, the labels of annotators often show inter-observer variability (IOV) which is mainly caused by annotators' different understandings of expertise. In order to build a trustworthy segmentation system, robust models should consider how to capture uncertainty in samples and labels. Different from the conventional way of handling IOV with label fusion such as majority voting, a fuzzy integral based ensemble framework of multiple deep learning models for optic disc segmentation is proposed. Each component segmentation model is trained with respect to an annotator. Then, a powerful nonlinear aggregation function, the Choquet integral, is employed in form of a neural network to integrate the segmentation results of multiple annotators. The proposed method is validated on the public RIM-ONE dataset consisting of 169 fundus images and each image is annotated by 5 experts. Compared with conventional segmentation ensemble methods, the proposed methods achieves a higher Dice score (98.69%). © 2021 ACM.

关键词： Deep neural networks

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Adversarial attacks and defenses in physiological computing:a systematic review

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National Science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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OA-ECBVC: A Cooperative Collision-Free Encirclement and Capture Approach in Cluttered Environments

OA-ECBVC: A Cooperative Collision-Free Encirclement and Capt...

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IEEE Conference on Decision and control

作者： Xinyi Wang Yulong Ding Yizhou Chen Ruihua Han Lele Xi Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Department of Control Science and Engineering Tongji University Shanghai China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Department of Computer Science The University of Hong Kong Hong Kong China Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms.

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Innovative Competency-Based Electrical Engineering Curriculum 54

Innovative Competency-Based Electrical Engineering Curriculu...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Rehder, Gustavo P. Serrano, Ariana L. C. Grimoni, José B. Brandão, Anarosa A. F. Silva, Magno M. T. Moriya, Henrique T. Panazio, Cristiano M. Barbero, Edson Alayo, Marco I. Gianesi, Irineu G. N. Seabra, Antonio C. Universidade de São Paulo Electronic Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Electric Automation and Energy Dept. The Escola Politécnica São Paulo Brazil Universidade de São Paulo Computer Engineering and Digital Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Telecommunications and Control Dept. The Escola Politécnica São Paulo Brazil Á lvares Penteado School of Commerce Foundation São Paulo Brazil Instituto Mauá de Tecnologia São Paulo Brazil

ISBN: (纸本)9798350351507

The Electrical Engineering undergraduate program of the Escola Politécnica of the Universidade de São Paulo has created an alternative innovative curriculum path for its freshmen students in 2024. The Competency-based Path emphasizes the development of competencies and skills for the unprecedented technological advancements that necessitate a holistic education for future electrical engineers, preparing them with practical skills for real-world challenges. The program was designed using an innovative methodology and centers around semester-long or year-long integrative projects. These projects bridge the gap between theory and practice, as students deal with real-world challenges while fostering social responsibility. © 2024 IEEE.

关键词： Students

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Asynchronous multi-rate systems of a transfer learning filtering algorithm using t-distribution

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2025年第5期42卷 947-954页

作者： Wang, Wei Zhao, Shun-Yi Zhang, Cheng-Xi Luan, Xiao-Li Liu, Fei Wu, Jin Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology 999077 Hong Kong

This paper investigates a transfer learning filtering algorithm based on the t-distribution to address the problem of state estimation in asynchronous multi-rate sensor systems affected by outliers. By integrating full probability design and multi-scale system theory, we propose a novel transfer learning framework for asynchronous multi-rate systems. A multi-scale model is established to convert the asynchronous multi-rate system into a synchronous one. This design minimizes the Kullback-Leibler divergence between the predicted distribution in the source domain and the ideal distribution in the target domain, while allowing the sensor sampling rate ratio to be any positive integer. To account for outlier effects on state estimation, the source and target domains leverage the heavy-tailed properties of the t-distribution to model state and observation processes, with approximate estimation achieved through expectation-maximization and variational Bayesian methods. Simulation results on a planar position-velocity system demonstrate the superior performance of the proposed method. © 2025 South China University of Technology. All rights reserved.

关键词： Maximum likelihood estimation

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Use Knowledge to Augment Data from Scratch: An Explainable Deep Immunohistochemical Diagnosis Model

SSRN

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SSRN 2024年

作者： Zhao, Jing Liu, Yuhan Zhang, Xinzhu Sun, Shiliang Yin, Minzhi School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Pathology Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai200127 China

Pathological diagnosis has an important role as the last step in cancer diagnosis. When the naked eye is not enough to identify cancer, we usually use further immunohistochemical diagnosis, which is the detection of specific proteins to obtain protein sequences and their negative/positive expression, and make the final diagnosis based on the sequences. This process is a classification task for protein detection sequences. However, when deep learning goes into this field, it often has the problem of lack of data and poor explainability. Based on the above description, to solve the problem of small sample classification, we propose to extract knowledge from scratch, build protein knowledge graphs, and reuse protein embeddings from the knowledge graph to improve classification performance. In addition, we give explanation for the diagnostic model by visualizing importance and coupling degree of proteins. In our experiments, we find that our proposed classification model has good classification performance and explainability. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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CrossCas: A Novel Cross-Platform Approach for Predicting Cascades in Online Social Networks with Hidden Markov Model

CrossCas: A Novel Cross-Platform Approach for Predicting Cas...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Jinwei Liu Xiaonan Zhang Richard A. Alo Xiuzhen Huang Long Cheng Feng Deng Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA Department of Computer Science Florida State University Tallahassee FL USA Department of Computer Science Arkansas State University Jonesboro AR USA School of Control and Computer Engineering North China Electric Power University Beijing China School of Automation Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781665435413

Information sharing through online social networks (OSNs) facilitates quick discovery and consumption of information online. Many OSNs such as Facebook, Twitter provide resharing or reposting features, which allows users to share others' content with their own friends or followers. As content is shared from person to person, cascades of information-sharing can occur. There are many existing works focusing on analyzing and characterizing the cascades in OSNs. However, previous works focus on the analysis and characterization of cascades without providing a solution to accurately predict cascades. Although some methods for cascade prediction have been proposed recently, their methods work in social networks such as Facebook (or Twitter), and do not work well simultaneously in multiple OSNs such as Software Social Network (SSN) GitHub, Twitter and Reddit because GitHub, Twitter and Reddit have different social activity patterns. In this paper, we first perform a thorough analysis of cascades in multiple OSNs: GitHub, Twitter and Reddit, and identify the cascades of information-sharing. We then propose CrossCas, a novel cross-platform approach for predicting cascades in multiple OSNs with Hidden Markov Model (HMM). The experimental results show that our proposed method achieves high performance.

关键词： Training Social networking (online) Blogs Hidden Markov models Information sharing Prediction algorithms Software

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