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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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A Scheme for Network Capacity Region Optimization in the Fog Stochastic Network 13

A Scheme for Network Capacity Region Optimization in the Fog...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Ju, Chengen Ma, Yue Yin, Zhenyu Xu, Guangyuan Wang, Xiaohui Shi, Xiaoqiang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software Shenyang110168 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software Shenyang110168 China

ISBN: (纸本)9798350376258

The efficient task offloading and scheduling mechanisms in fog computing networks can significantly enhance the service quality for IoT applications. However, a fundamental problem in this domain lies in determining the optimal number of fog nodes and selecting the most suitable offloading nodes. In this paper, we introduce a novel approach to address this issue by proposing a method to accurately estimate the required number of fog nodes while ensuring network stability. Meanwhile, we also delve into the tradeoffs involved in offloading tasks to nearby fog nodes versus remote fog nodes to delineate the network capacity region. Then, we establish a lower bound on the number of tasks that should be offloaded to remote fog nodes, considering both task execution times and their associated delay-deadlines. Finally, the simulation results show the efficacy of our proposed method in upholding network stability. ©2024 IEEE.

关键词： Stochastic systems

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Research on Intelligent Visual Detection Methods for Human Physiological Parameters 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Chenggang Wan, Zilin Shao, Rui Lin, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

In recent years, the incidence and mortality rates of cardiovascular diseases have been increasing year by year, prompting greater attention to the monitoring of physiological parameters in daily life. This paper proposes a deep physiological parameter detection method based on Image Photoplethysmography (IPPG) technology, overcoming the discomfort of contact-based measurements and the limitation of measuring single physiological indices through intelligent visual means. A lightweight attention mechanism combined with the BHCNN model is designed to automatically extract waveform features of IPPG signals for estimating blood pressure and heart rate in the human body. The mean absolute errors (MAE) of the obtained systolic pressure, diastolic pressure, and heart rate are 6.60, 6.22, and 7.15 respectively, with standard deviations (STD) of 3.22, 2.61, and 3.98. The data consistency of both indices exceeds 95%, demonstrating high accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood pressure

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Research on Geospatial Object Recognition Algorithm in Power Transmission and Transformation Engineering Based on FIseg-YOLO 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ouyang, Peng Pi, Yufei Zhang, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

The preliminary work of power transmission and transformation engineering design tasks relies on the precise survey of terrain to ensure the high-quality completion of design tasks. Therefore, developing a technology that can accurately and efficiently survey the construction area has become a hot topic in the field of electrical design research. However, identifying the location and precise boundaries of geospatial targets of different scales and densities from high-resolution natural environment images captured by drones remains a challenge. To address this issue, we improved upon the YOLOv8 algorithm by incorporating the concept of the YOLACT algorithm, proposing a novel instance segmentation algorithm—FIseg-YOLO, specifically designed for recognizing geospatial targets in power transmission and transformation line projects. This algorithm integrates various attention mechanism modules in the backbone network, effectively improving the accuracy of target recognition against complex backgrounds. Simultaneously, we optimized the neck network structure by enhancing the multi-path fusion of the network, achieving multi-level feature fusion between different layers. Additionally, the algorithm adds a multi-scale detection head to the basic network structure to extract shallow information from the input image and combine it with the feature fusion network to detect objects across a broader scale. Experimental validation on the geospatial orthophoto dataset shows that the mAP@.5 of the FIseg-YOLO model reached 67.3%, a 6.6% increase compared to the YOLOv8 base model, indicating a significant improvement in model performance. This improvement is of great significance for enhancing the efficiency of preliminary survey tasks in power transmission and transformation engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Design of Missile Roll Autopilot based on Linear Extended State Observer 34

Design of Missile Roll Autopilot based on Linear Extended St...

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第34届中国控制与决策会议

作者： Xu Xieyu Cui Xiaoxi Yu Wenbo Luo Penghao Gao Mi Norinco Group Institute of Navigation and Control Technology Beijing Aeronautic Technology Research Center Beijing JingHang Research Institute of Computing and Communication

ISBN: (纸本)9781665478977

With the expansion of missile envelope and the improvement of missile combat effect in recent years,the control of missile roll channel is faced with many kinds of unknown disturbances,such as oblique-blowing moment,additional torque at high angle-of-attack periodic interference of spin tail,severe noise and so on,which are very difficult to be modeled or analyzed *** traditional PID roll autopilot cannot keep the command tracking effectively under these *** on the ADRC idea,this paper designs a kind of LESO observer for missile roll channel without changing the original control parameters or structure,which can effectively estimate the disturbance and realize stable tracking of the roll angle command under all kinds of disturbance conditions.

关键词： LESO roll autopilot disturbance estimation and compensation ADRC

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HEALTH MANAGEMENT OF BRUSHLESS DIRECT CURRENT MOTOR controlLER

HEALTH MANAGEMENT OF BRUSHLESS DIRECT CURRENT MOTOR CONTROLL...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Gu, Yufeng Zhang, Qianqian Wang, Qingzhe Wang, Bo AVIC Xi'an Flight Automatic Control Research Institute No.129 Jinye Road Xi'an China AVIC Computing Technique Research Institute No. 15 Jinye 2nd Road Xi'an China

Brushless direct current motor (BLDCM) is a typical mechatronics product, and its health management is an important basis to ensure the normal operation of BLDCM. This paper studied the system architecture, operation principle and health management scheme of BLDCM controller, through the design of DC bus over-voltage and under-voltage protection circuit, bus over-current protection circuit, over-temperature protection circuit, Hall signal disorder protection circuit, control software and human-machine interface. The state monitoring and fault protection of 270V/28V power supply, communication function, motor speed, controller temperature, bus current and motor rotor position are realized, which provides a certain technical support for the health management of BLDCM. © The Institution of Engineering & Technology 2024.

关键词： Brushless DC motors

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Aerodynamic/control integrated optimization method for unpowered high-speed vehicle configuration design

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Chinese Journal of Aeronautics 2024年第7期37卷 153-167页

作者： Xin PAN Linlin WANG Li LI Lulu JIANG Gang CHEN State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China Shannxi Key Laboratory for Environment and Control of Flight Vehicle School of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China Beijing Institute of Space Long March Vehicle Beijing 100076China AVIC Xi’an Aeronautics Computing Technique Research Institute Xi’an 710068China

The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large *** conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their *** a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design *** enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated *** integrated optimization strategies,with differences in coupling granularity,have been ***,these strategies are put into action on a biconical vehicle that operates at Mach *** results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies ***,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control *** a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%*** method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design ***,it improves the controllability of high-sp

关键词： Aerodynamic/control Integrated Optimization MDO High-speed vehicle Shape Optimization controllability

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Synergistically Learning Class-specific Tokens for Multi-class Whole Slide Image Classification

Synergistically Learning Class-specific Tokens for Multi-cla...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sun, Pengzhong Wang, Wei Li, Xiangyu Dong, Suyu Li, Shuo Wang, Kuanquan Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology Faculty of Computing Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Case Western Reserve University Case School of Engineering Cleveland United States

ISBN: (纸本)9798350337488

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks. © 2023 IEEE.

关键词： Multi-class whole slide image analysis Multiple instance learning Transformer

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods 9

Classification of Attention Performance Post-Longitudinal tD...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Laboratory Mandi India Indian Institute of Technology Mandi School of Computing And Electrical Engineering Mandi India Institute of Nuclear Medicine And Allied Sciences Cognitive Control And Machine Learning Group Delhi India

ISBN: (纸本)9798350394474

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. © 2024 IEEE.

关键词： Electroencephalography

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