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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenyu Yin Shang Liu Guangyuan Xu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software University of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing number of Distributed Denial of Service (DDoS) attacks poses a major threat to the Internet, highlighting the importance of DDoS mitigation. Most existing approaches require complex training methods to learn data features, which increases the complexity and generality of the application. In this paper, we propose DrLLM, which aims to mine anomalous traffic information in zero-shot scenarios through Large Language Models (LLMs). To bridge the gap between DrLLM and existing approaches, we embed the global and local information of the traffic data into the reasoning paradigm and design three modules, namely Knowledge Embedding, Token Embedding, and Progressive Role Reasoning, for data representation and reasoning. In addition we explore the generalization of prompt engineering in the cybersecurity domain to improve the classification capability of DrLLM. Our ablation experiments demonstrate the applicability of DrLLM in zero-shot scenarios and further demonstrate the potential of LLMs in the network domains. The implementation of DrLLM is available at https://***/liuup/DrLLM.

关键词： Training Resistance Large language models Prevention and mitigation Signal processing Cognition Internet Prompt engineering Computer crime Speech processing

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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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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Databases Biological system modeling Precision medicine Electrocardiography Diffusion models Data models Quality assessment Biomedical monitoring Monitoring Synthetic data

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Nonradiative infrared thermography detection based on artificial intelligence analysis replaces traditional CT detection 3

Nonradiative infrared thermography detection based on artifi...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Chen, Jiaqi Su, Xin Gong, Jingyi Hu, Ruihan Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China SinoDaan Co. Ltd Guangzhou510540 China Guangzhou Xiaoaixiong Creative Consulting Co. Ltd Guangzhou510000 China Faculty of Computing National Pilot School of Software Harbin Institute of Technology Harbin150000 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

CT examination utilizes computational functions to achieve tomography of the human body based on the basic characteristics of X-rays, thereby unavoidably producing ionizing radiation that can cause damage to the human body. So, it is not applicable to pregnant women and children;Repeated exposure to CT irradiation in a short period of time may cause leukocytosis, fatigue, dizziness, vomiting and other symptoms. In particular, pregnant women, neonates and patients with extreme weakness are more likely to develop malformation, cancers and other adverse effects after exposure to radiation. However, endoscopic examination will induce physical damage to a certain extent, leading to potential risks of inflammation, and its process will cause fear and discomfort to patients, among which children are more likely to show fear than adults. In addition, there are many practical operation problems for endoscopic examination. So, it is not an ideal method. The medical infrared thermal imaging instrument adopts the high-tech infrared detection technology, which has no radiation and does not touch the human body. When the human body is diseased, the heat balance of the diseased part will also be destroyed. The infrared thermal imaging captures this imbalance based on the infrared rays from the human body to form an infrared thermogram, which reflects the temperature characteristics of the human body and thus will not harm the human body. The instrument has now already passed the clinical verification. Infrared thermography can well reflect the presentation of sinusitis, especially performs well in distinguishing whether the inflammation is acute or chronic. And the expression on infrared thermography is better than CT. Combined with artificial intelligence imaging algorithms, it can achieve feature analysis at the level of a single pixel and provide doctors with more detailed and accurate reference data, so as to implement efficient auxiliary diagnosis. The instrument is suitable

关键词： Infrared radiation

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A Vein Segmentation Method Based on Improved U-Net 12

A Vein Segmentation Method Based on Improved U-Net

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12th International Conference of Information and Communication Technology, ICTech 2023

作者： Wang, Hongliang Zhu, Liang Deng, Changyi Xiu, Pengfei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9798350332919

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making. © 2023 IEEE.

关键词： Image segmentation

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Cooperative Secure Communication in UAV Assisted Networks with Dynamic Role Switching

Cooperative Secure Communication in UAV Assisted Networks wi...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Wang, Qinyu Gao, Ang Hu, Yansu Zhang, Jiankang School of Electronics and Information Northwestern Polytechnical University Xi'an710072 China Yangtze River Delta Research Institute of Northwest Polytechnic University Taicang215400 China School of Electronic and Control Engineering Chang'an University 710064 China Bournemouth University Department of Computing and Informatics United Kingdom

ISBN: (纸本)9798350370218

This paper proposes a novel role switching scheme (RSS) to optimize the secure communication by the cooperation of multiple unmanned aerial vehicles (UAVs). Each UAV is enabled to switch its role during the flight, i.e., could be an aerial relay to serve ground users (GUs) or a friendly jammer to generate artificial noise (AN) to deteriorate the wiretapping of potential eavesdropper users (EUs). It's worthy to be noticed that the joint optimization for UAVs' trajectory and legitimate GUs' transmission power control with RSS is a non-convex mixed integer fractional programming problem. A multi-agent deep reinforcement learning (MADRL) combined successive convex approximate (SCA) algorithm is further designed to maximize the achievable secrecy energy efficiency (ASEE). Numerical results illustrate that compared with the role fixed scheme (RFS) and relaxation based SCA approaches, the proposed DRL-SCA algorithm endows UAVs the capacity to fly close enough to target users (both GUs and EUs) which brings a better achievable secrecy rate (ASR), less energy consumption and higher ASEE. © 2023 IEEE.

关键词： DRL Dynamic Role Switching SCA Secure Communication UAV Assisted Networks

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YOLO-MFE: towards more accurate object detection using multiscale feature extraction 6

YOLO-MFE: towards more accurate object detection using multi...

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6th International Conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Li, Hui Yin, Zhenyu Fan, Chao Wang, Xiaohui School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware and Software Shenyang110168 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

YOLOv3, as a multi-scale object detection algorithm, has a simple structure and can be detected quickly. However, during the training process, as the bottom convolutional layer contains more object detail, and the information in the underlying layer will be gradually faded out through continuous convolution. To address this, this paper adds a normalized attention mechanism behind the feature layers at different scales to further suppress insignificant features and enhance the discriminability and robustness of the features. Moreover, an improved pyramid structure is leveraged to fuse the detail of the shallow feature maps with the semantic feature information of the higher layers, aiming to improve the model performance. In particular, a YOLOv3 object detection algorithm named YOLO-MFE based on improved multi-scale feature extraction is proposed. Experimental results on the Pascal VOC dataset demonstrate that our algorithm outperforms the original YOLOv3 algorithm in terms of object detection accuracy, with a mAP on the Pascal VOC 2007 test set that is 1.66% higher. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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Science China(Information Sciences) 2020年第11期63卷 195-207页

作者： Yuyan GUO Bin XU Weixin HAN Shuai LI Yueping WANG Yu ZHANG School of Automation Northwestern Polytechnical University Department of Computing The Hong Kong Polytechnic University Flight Automatic Control Research Institute State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

This paper investigates the state-constrained controller design of a hypersonic flight vehicle(HFV) based on an asymmetric barrier Lyapunov function(ABLF). The robust adaptive back-stepping controller with integral terms is applied for the HFV longitudinal dynamics. Considering the asymmetric angle of attack(AOA) constraint caused by the unique structure and scramjet, the controller is modified by constructing an ABLF, where the asymmetric constraint on AOA tracking error is introduced. Combined with the constraint on virtual control, the AOA is restricted to a predefined asymmetric interval. The system stability and the AOA constraint are guaranteed via Lyapunov analysis. Simulation results verify that the AOA can be kept in the given asymmetric interval while the altitude reference signal is tracked.

关键词： hypersonic flight vehicle HFV integral back-stepping control adaptive control asymmetric barrier Lyapunov function ABLF angle of attack (AOA) constraint

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SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis 25

SCC-YOLO: An Improved Object Detector for Assisting in Brain...

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Proceedings of the 2025 International Conference on Health Big Data

作者： Runci Bai Guibao Xu Yanze Shi Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing China School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu China

ISBN: (纸本)9798400713446

Brain tumours may cause neurologic impairment, cognitive and mental disorders, elevated intracranial pressure and convulsions, which may be associated with a serious health hazard. The You Only Look Once (YOLO) system has been demonstrated to be highly accurate when it comes to detecting objects in medical imaging. A new SCC-YOLO structure is proposed, which combines SCConv with YOLOv9. The SCConv module improves the performance of the convolutional neural network, which improves the performance of the system. In this paper, we investigate the impact of YOLOv9 on the Br35H dataset (Brain_Tumor_Dataset) Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection.

关键词： Brain Tumor

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Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network 5th

Towards Efficient and Privacy-Preserving Hierarchical Federa...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： An, Ningyu Liang, Xiao Zhou, Fei Wang, Xiaohui Li, Zihan Feng, Jia Guan, Zhitao State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd Beijing100209 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Zhejiang Electric Power Company.Ltd Hangzhou310007 China

ISBN: (纸本)9789819981007

Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge devices, existing federated learning schemes still suffer from heterogeneity challenge. Hierarchical federated learning divides devices into clusters based on their resources to enhance training efficiency. In addition, there are also security concerns such as the risk of privacy leakage during the interactions intra and cross the layers. In this paper, we propose PHFL, a Privacy-Preserving Hierarchical Federated Learning Scheme, which includes a personalized task assignment protocol and a heterogeneous-friendly aggregation scheme, so as to achieve an efficient and low-cost training for resource-constrained edge devices. To address the heterogeneity challenge, we combine synchronous and asynchronous aggregation, and design different aggregation weights for device differences. On top of that, a privacy-preserving scheme based on threshold homomorphic encryption is designed to accommodate the privacy requirements of the proposed hierarchical federated learning scenario. The security analysis and performance evaluation demonstrate that this approach improves model generalizability and learning efficiency with acceptable overhead, while also providing privacy protection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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