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computer science and Management technology (ICCSMT), International Conference on

作者： Yunliang Li Yanfang Fu Zhiqiang Du Zhipeng Cai School of Armament Science and Technology Xi’an Technological University Xi’an China School of Computer Science and Engineering Xi’an Technological University Xi’an China North Automatic Control Technology Institute Taiyuan China

With the rapid development of information technology, the Internet of Things (IoT) has become more complex, and more and more devices and users are connected to the IoT system. While improving data interactivity and expanding the scope of data sharing, secure access to IoT resources is also facing great risks. Therefore, it is necessary to design an appropriate access control scheme. Firstly, this paper introduces Decentralized Identifiers (DID) and Verifiable Credentials. It is used for authentication and rights management of users and devices, respectively, which makes identity and authority unforgeable. Secondly, this paper uses blockchain technology to support DID and Verifiable Credentials, which can ensure the decentralization, invariance and traceability of data. Finally, using smart contracts enables automatic processing of requester authorization and access requests. The test results show that the scheme has good performance.

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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Syndrome Classification Based on Multi-Graph Attention Network 12

Syndrome Classification Based on Multi-Graph Attention Netwo...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Ma, Jishun Lu, Weikai Zhou, Changen Teng, Shenghua Li, Zuoyong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Fujian University of Traditional Chinese Medicine Research Base of Traditional Chinese Medicine Syndrome Fuzhou350122 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value. © 2022 IEEE.

关键词： computer aided diagnosis

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Tan, Qingshan Li, Changhe Zeng, Sanyou Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9798350314588

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front. © 2023 IEEE.

关键词： Multiobjective optimization

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Optimal Action Strategy approach for Desert Crossing Based on Dynamic Programming

Optimal Action Strategy approach for Desert Crossing Based o...

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CQVIP Conference on Data Driven Intelligence and Innovation, CCDDII 2021

作者： Fan, Xingrong Meng, Dian Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing400067 China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China

It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing based on dynamic programming. According to the game rules, a multi-stage decision optimization model was first established to maximize the remaining funds of players within given game time. Then, a simulation-based dynamic programming algorithm was designed to solve the optimization model, and the optimal action strategy was obtained by backtracking crossing routes. Finally, two experimental case studies with different initial conditions were used to validate the model. In comparison with the benchmark exhaustive search algorithm, the proposed approach has higher performance and efficiency. The case studies confirm that the method can obtain the optimal action strategy under different game maps and initial resources. © 2021 Elsevier B.V.. All rights reserved.

关键词： Dynamic programming

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On the conceptualization of total disturbance and its profound implications

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science China(Information sciences) 2020年第2期63卷 221-223页

作者： Sen CHEN Wenyan BAI Yu HU Yi HUANG Zhiqiang GAO Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing Aerospace Automatic Control Institute Center for Advanced Control Technologies Department of Electrical Engineering and Computer ScienceCleveland State University

Dear editor,How to deal with uncertainties and/or disturbances is a central issue pushing the development of both control science and control technology. Among various approaches, the active disturbance rejection control (ADRC) has been successfully implemented in various industrial practices because of its uniqueness in concepts, simplicity

关键词： ESO On the conceptualization of total disturbance and its profound implications

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Data-Driven Source Term Estimation of Hazardous Gas Leakages Under Variable Meteorological Conditions

SSRN

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SSRN 2024年

作者： Ni, Chuantao Lang, Ziqiang Wang, Bing Li, Ang Cao, Chenxi Du, Wenli Qian, Feng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies. © 2024, The Authors. All rights reserved.

关键词： Least squares approximations

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan 430074 China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China China University of Geosciences Faculty of Engineering Wuhan 430074 China Macau University of Science and Technology School of Computer Science and Engineering Macau Macao Wuhan University Electronic Information School Wuhan 430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： adversarial generative network image fusion Multi-modal fusion semantic awareness

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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