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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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Modelling the dynamics of information warfare: an attacker-defender scenario using Lotka-Volterra equations

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1407-1418页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Faculty of Radio and Computer Technology Moscow Institute of Physics & Technology Moscow 141701 Russian Federation Federal Research Center “Computer Science and Control” Russian Academy of Science Moscow 119333 Russian Federation

The dynamics of information warfare in an attacker-defender scenario pose significant challenges in today’s digital age. To address these challenges, this research models the dynamics of information warfare using modified Lotka-Volterra equations, adapted from predator-prey interactions, to simulate the interactions between attackers and defenders. The model captures the growth, interaction, and feedback loops of both populations, offering insights into their dynamics over time. By incorporating the attacker’s growth rate and the defender’s natural decline rate, and discretizing time for real-time simulation, the study reveals how attacker populations initially grow but decline as defenders enhance their countermeasures. The defender population stabilizes as their defensive capabilities improve, demonstrating the model’s effectiveness in representing the complex dynamics of information warfare. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Attacker-defender Cyber security Information warfare Lotka-Volterra equations Population dynamics

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MedFILIP: Medical Fine-grained Language-Image Pre-training

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3587-3597页

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo The School of Computer Science and Technology Harbin Institute of Technology Harbin China The Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The College of computer and control engineering Northeast Forestry University Harbin China The Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia The Department of Biomedical Engineering Department of Computer Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestXray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. © 2013 IEEE.

关键词： Semantics

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Vibration-induced bias error reduction using loop gain compensation for high-precision fiber optic gyroscopes

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Journal of Systems Engineering and Electronics 2025年第1期36卷 224-232页

作者： CHEN Heyu QIN Xuexin XIE Huan KONG Linghai ZHENG Yue School of Instrumentation Science and Opto-Electronics Engineering Beihang UniversityBeijing 100191China Beijing Institute of Computer Technology and Application Beijing 100854China Hunan Shuaiwei Control Technology Co. LTDChangsha 421000China Beijing Institute of Control and Electronic Technology Beijing 100038China

Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

关键词： vibration-induced error fiber optic gyroscope(FOG) loop gain

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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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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Convolution Algorithm for Evaluation of Probabilistic Characteristics of Resource Loss Systems with Signals 27th

Convolution Algorithm for Evaluation of Probabilistic Char...

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27th International Conference on Distributed computer and Communication Networks, DCCN 2024

作者： Maslov, A.R. Sopin, E.S. Shorgin, S.Ya. Moscow Russia Institute of Informatics Problems Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031808524

Resource loss systems (ReLS) with signals, in which arrival of a signal triggers resource reallocation of a customer, are often used in the performance analysis of the contemporary mobile networks, especially those that utilize high-frequency bands. An analytical solution for the stationary distribution of such systems is not obtained yet. Approximate calculation methods utilize the stationary distribution of ReLS without signals at the departure instants. In the paper, we develop an efficient convolution algorithm for evaluation of the probability metrics at the departure instants. In the case study, we demonstrate the effectiveness of the proposed algorithm in terms of execution time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov chains

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Adaptive Fixed-Time Command Filtered Tracking control for Parametric Nonlinear Systems With Unknown Nonlinear control Coefficient and Unknown Disturbances

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IEEE Access 2025年 13卷 37839-37849页

作者： Zhang, Xiyu Zhou, Youjun Feng, Chun Deng, Xiongfeng Guangxi Science and Technology Normal University School of Mathematics and Computer Science Laibin546199 China Guang’an Institute of Technology School of Mechanical and Electrical Engineering Guang’an638550 China Anhui Polytechnic University Key Laboratory of Electric Drive and Control Anhui Higher Education Institute Wuhu241000 China

This paper addresses the issue of adaptive fixed-time tracking control (FTTC) for a category of parametric nonlinear systems characterized by unknown nonlinear control coefficient (UNCC) and unknown external disturbances (UEDs). To effectively handle unknown parameters and unknown disturbances, the adaptive parameter estimation method is introduced into the control law design. Meanwhile, some parameter adaptive laws are presented to estimate these unknown parameters. To eliminate repeated differentiation of virtual control laws, a class of nonlinear command filters is introduced. Furthermore, the filtering errors are compensated through the designed adaptive compensation control laws. Subsequently, the Nussbaum gain function technique is further used to deal with the UNCC in the system. Leveraging command-filtering backstepping control (BC) and fixed-time control method, an adaptive command filtered FTTC law is final presented to achieve the fixed time tracking. The theoretical analysis results indicate that the designed control law guarantees the convergence of tracking error to a small neighborhood of zero within a fixed time, while ensuring that all signals within the closed-loop system remain bounded. The proposed control law’s effectiveness is demonstrated through three simulation examples. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： Backstepping

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An Ecological and Economic Model of Carbon Neutrality 15th

An Ecological and Economic Model of Carbon Neutrality

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Moiseev, Nikita Olenev, Nicholas Zasukhina, Elena Teymurazov, Kirill Medennikov, Anton Moscow Institute of Physics and Technology Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031791185

The paper considers a mathematical model that describes the interactions between the economic system and the ecological system through waste emissions, resource consumption, and the restoration of restorable resources. It is assumed that the state levies a tax on waste emissions, thereby controlling emissions and the consumption of resources by the economy. The model also describes the impact of economic and environmental factors on demographics. The state should strive for economic growth and avoid environmental disasters, and the model can be useful for achieving these goals. Besides, a software implementation of the model was constructed. Some technical details of the software implementation were also considered. Numerical experiment with one of the possible sets of parameters’ values was carried out, which displayed one of the possible situations of stable development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Carbon capture and utilization

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Adjacency-Aware Fuzzy Label Learning for Skin Disease Diagnosis

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1429-1440页

作者： Zhou, Murong Zuo, Baifu Wang, Guohua Luo, Gongning Li, Fanding Dong, Suyu Wang, Wei Wang, Kuanquan Li, Xiangyu Xu, Lifeng Quzhou People's Hospital Quzhou Affiliated Hospital of Wenzhou Medical University Quzhou324000 China Northeast Forestry University College of Computer and Control Engineering Harbin150040 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Harbin Institute of Technology Shenzhen518000 China

Automatic acne severity grading is crucial for the accurate diagnosis and effective treatment of skin diseases. However, the acne severity grading process is often ambiguous due to the similar appearance of acne with close severity, making it challenging to achieve reliable acne severity grading. Following the idea of fuzzy logic for handling uncertainty in decision-making, we transforms the acne severity grading task into a fuzzy label learning (FLL) problem, and propose a novel adjacency-aware fuzzy label learning (AFLL) framework to handle uncertainties in this task. The AFLL framework makes four significant contributions, each demonstrated to be highly effective in extensive experiments. First, we introduce a novel adjacency-aware decision sequence generation method that enhances sequence tree construction by reducing bias and improving discriminative power. Second, we present a consistency-guided decision sequence prediction method that mitigates error propagation in hierarchical decision-making through a novel selective masking decision strategy. Third, our proposed sequential conjoint distribution loss innovatively captures the differences for both high and low fuzzy memberships across the entire fuzzy label set while modeling the internal temporal order among different acne severity labels with a cumulative distribution, leading to substantial improvements in FLL. Fourth, to the best of our knowledge, AFLL is the first approach to explicitly address the challenge of distinguishing adjacent categories in acne severity grading tasks. Experimental results on the public ACNE04 dataset demonstrate that AFLL significantly outperforms existing methods, establishing a new state-of-the-art in acne severity grading. © 1993-2012 IEEE.

关键词： Fuzzy set theory

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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