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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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Secure-by-Construction Synthesis for control systems

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IEEE Transactions on Automatic control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Robust Car-Following control of Connected and Autonomous Vehicles: A Stochastic Model Predictive control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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computer systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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