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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Chen, Jinghuang Bao, Xiaoyu Ye, Yuntao Huang, Qiyun School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (纸本)9798331541750

Sawtooth Waves (STW) that occur during Rapid Eye Movement (REM) sleep are closely associated with memory consolidation and the regulation of brain functions. However, due to challenges in data acquisition and the high cost of manual annotation, there is currently an absence of a publicly massive STW annotated dataset. This study focuses on constructing a high-quality STW dataset, comprising 4912 samples from 200 participants, and developing classification algorithms for STW detection. We utilized three prevalent algorithms-Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-to assess the dataset's quality. The dataset achieved a maximum classification accuracy of 95.62% with the CNN, confirming the gold standard of STW dataset. Finally, we studied age and gender difference in physiological characteristics of STW, including average frequency of STW, peak-to-peak amplitude(PTP), root mean square(RMS) and spectrum power. This study created a public STW dataset and a STW detection model, laying the groundwork for analyzing the mechanisms of STW events. © 2024 IEEE.

关键词： Convolutional neural networks

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TEPG:a traffic engineering based power-aware greedy routing algorithm in backbone networks with bundled links

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Frontiers of computer Science 2024年第1期18卷 243-245页

作者： Jinhong ZHANG Xingwei WANG Ruixia LI Bo YI Min HUANG Dongxing SHUI School of Electronic and Information Engineering West Anhui UniversityLu’an 237012China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110169China School of Information Science and Engineering Northeastern UniversityShenyang 110819China

1 Introduction and main contributions Various power-aware solutions have been proposed to address the alarming energy waste and consequent serious environmental issues since Gupta and Singh initiated the seminal study on green networking problem in *** of the researches concentrated their efforts on power-aware networking under the non-bundled link scenarios by leveraging the dynamic power management based Low Power Idle(LPI)policy which was defined and standardized in the IEEE 802.3az ***,in modern backbone networks,pairs of routers are typically connected,for each traffic direction,by multiple physical links that form one logical bundled link,which is the link aggregation technique defined and standardized in IEEE 802.1AX.

关键词： power bundle backbone

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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An Infrared and Visible Image Fusion Approach of Self-calibrated Residual Networks and Feature Embedding

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Recent Advances in computer Science and Communications 2023年第2期16卷 2-13页

作者： Dai, Jinpeng Luo, Zhongqiang Li, Chengjie School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin644000 China Artificial Intelligence Key Laboratory of Sichuan University of Science and Engineering Yibin644000 China School of Computer Science and Technology Southwest Minzu University Chengdu610041 China

Background: The fusion of infrared images and visible images has been a hot topic in the field of image fusion. In the process of image fusion, different methods of feature extraction and processing will directly affect the fusion performance. Objectives: Low resolution (small size) of high-level features will lead to the loss of spatial information. On the other side, the low-level features are not significant due to their insufficient filtering of background and noise. Methods: In order to solve the problem of insufficient feature utilization in existing methods, a new fusion approach (SC-Fuse) based on self-calibrated residual networks (SCNet) and feature embedding has been proposed. The method improves the quality of image fusion from two aspects: feature extraction and feature processing. Results: First, self-calibrated modules are applied to the field of image fusion for the first time, which enlarged the receptive field to make feature maps contain more information. Second, we use ZCA (Zero-phase Component Analysis) and l1-norm to process features, and propose a feature embedding operation to realize the complementarity of feature information at different levels. Conclusion: Finally, a suitable strategy is given to reconstruct the fused image. After ablation experiments and comparison with other representative algorithms, the results show the effectiveness and superiority of SC-Fuse. © 2023 Bentham Science Publishers.

关键词： Image reconstruction

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Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Qian Liu, Lijun Yang, Xiaobing Liu, Li Peng, Wei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

ISBN: (纸本)9798350386226

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model's reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering. © 2024 IEEE.

关键词： Multi-task learning

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GAN-Based Deblurring Algorithm for Drug Package Detection 42

GAN-Based Deblurring Algorithm for Drug Package Detection

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Hao Fei, Zixiang Zhou, Wenju Fei, Minrui School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9789887581543

With the development of Industry 4.0, many manufacturers are moving in the direction of automation and intelligence. Drug package inspection quality testing methods have also changed from traditional manual inspection to machine vision inspection. However, due to the long exposure time or vibration of the camera, the images are blurred, which will seriously affect the accuracy of the defect detection. We propose a deblur model with higher level of image restoration and more lightweight. Using reserving and merging blocks to replace the original reserving and merging blocks, which can be used in the inference phase to turn ResBlocks into equivalent straight structures using reserving and merging operations, making the network lighter. The pyramid squeeze attention module is also incorporated to enable the model to efficiently extract finer-grained multi-specimen scale spatial information, thus making more efficient use of feature space information. Finally, a drug package dataset was produced and the model was trained to obtain good deblurring results. The comparative experiments confirmed that the model has better image reduction and excellent reduction speed, and can be further cited in production inspection pipelines to improve detection accuracy. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Merging

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Study on frictional behavior of SiC_(f)/SiC composite clad tube clamping condition under nuclear irradiation

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Friction 2024年第5期12卷 919-938页

作者： Chenglong XIAO Liangliang SHEN Tianqi ZHU Jianbo TANG Ximing XIE Xinyu FAN Jian XU Zhiying REN School of Mechanical Engineering Zhejiang University of TechnologyHangzhou310023China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Material Technology and EngineeringChinese Academy of SciencesNingbo315201China State Key Laboratory of Fine Chemicals Liaoning High-Performance Polymer Engineering Research CenterSchool of Chemical EngineeringDalian University of TechnologyDalian116024China School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou350116China School of Computer Science and Technology Shandong UniversityQingdao266237China Aviation Industry Corporation of China Ltd.Beijing100028China

Silicon carbide fiber reinforced silicon carbide matrix(SiC_(f)/SiC)composite is the key cladding material of nuclear fuel,which determines the safety and reliability of nuclear fuel storage and *** replacement of its storage and transportation scenario needs to be completed by the manipulator,but the application of SiC_(f)/SiC wear,fracture,and nuclear leakage in the snatching process of brittle-flexible-rigid contact in the irradiation environment has been seriously restricted due to unclear understanding of the damage ***,the effects of irradiation dose and clamping load on the friction characteristics of the contact interface between SiC_(f)/SiC clad tube are studied in this paper,and the effects of irradiation parameters and clamping force on the static friction coefficient of the contact interface between the clad tube and flexible nitrile are *** on the Greenwood-Williamson tribological model,a numerical model of the shape and structure of the contact micro-convex at the micro-scale of the clamping interface is constructed by introducing the multi-surface integral,and finally verified by *** research results show that there is a unique“Irradiation suppression zone”under the clamping condition of SiC_(f)/SiC cladding tube under the nuclear irradiation environment,and the growth of static friction coefficient slows down until stagnates after irradiation reaches a certain extent(600 kGy),and there will be a decline when the irradiation dose continues to increase,among which the clamping force of 15.2 N within the irradiation interval of 1,000 kGy can meet the safety of nuclear environment *** results of this paper can provide an important theoretical basis and application guidance for the safe operation of SiC_(f)/SiC cladding tubes in the storage and transportation clamping process.

关键词： SiC_(f)/SiC Clad tube nuclear irradiation conditions Greenwood-Williamson tribological model multi-surface integral

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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