检索结果-内蒙古大学图书馆

Multi-Person Respiration Monitoring Leveraging Commodity Wi-Fi Devices

计算机科学技术学报（英文版） 2025年第1期40卷 229-251页

作者：伊恩泽牛凯张扶桑高睿杨罗骏张大庆 1School of Computer Science Peking University Beijing 100871 China 2Beijing Xiaomi Mobile Software Co. Ltd. Beijing 100085 China 3State Key Laboratory of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing 100190 China 4School of Computer Science and Engineering Nanyang Technological University Singapore 639798 Singapore 5Telecom SudParis Institut Polytechnique de Paris Paris 91000 France

Monitoring respiration is an important component of personal health *** recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based mul-ti-person respiration sensing mainly extract individual's respiration rate in the frequency domain using the fast Fourier transform(FFT)or multiple signal classification(MUSIC)method,leading to the following limitations:1)largely ineffec-tive in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths,2)un-able to acquire the time-varying respiration pattern when the subject has respiratory abnormity,such as apnea and chang-ing respiration rates,and 3)difficult to identify the real number of subjects when multiple subjects share the same or simi-lar respiration *** address these issues,we propose Wi-Fi-enabled MUlti-person SEnsing(WiMUSE)as a signal pro-cessing pipeline to perform respiration monitoring for multiple persons ***,as a pioneering time domain approach,WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms,so as to form a blind source separation(BSS)*** effective separation of the signal sources(respira-tory waveforms)further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects,and thus to identify the number of subjects along with their respective respiration *** implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that,compared with the approaches based on the FFT or MUSIC method,90％error of respiration rate can be reduced by more than 60％.

关键词： respiration monitoring multi-person sensing Wi-Fi sensing

来源：评论

学校读者我要写书评

暂无评论

RENOWNED: A Real-Time Anomaly Detection and Mitigation Framework in Edge-Enabled IoV

引用

IEEE Internet of Things Journal 2025年第12期12卷 20695-20706页

作者： Pal, Chandrajit Saha, Sangeet Zhai, Xiaojun McDonald-Maier, Klaus University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom

The rapid adoption of smart vehicles and their interconnection through the Internet of Vehicles (IoV) has increased the use of Electronic Control Units (ECUs) in cars. These ECUs, while enabling advanced features, also present a larger target for cyberattacks, which can disrupt critical functions and jeopardize safety. The time-sensitive nature of automotive systems necessitates swift responses, making the protection of ECUs crucial. The imprecise computation (IC) task model can mitigate the risk of task completion failures by generating acceptable approximation results within deadlines when achieving absolute accuracy becomes difficult within fixed deadlines and energy budgets. This paper introduces RENOWNED, a solution that ensures the normal functioning of these Controller area networks (CAN) controlled ECUs even in the face of anomalies. It combines anomaly detection and mitigation through the HEALING module to maintain the desired performance. The anomaly detection module uses Graph Attention Networks (GAT) to identify unusual processor behaviour. If an anomaly is detected, the HEALING module takes over, reallocating tasks based on the available resources to guarantee that deadlines are met and energy constraints are not exceeded. Experiments have shown that RENOWNED delivers a Quality of Service (QoS) of 25% to 64% when system utilisation is varied in the range from 40% to 90%. It exhibits an excelling performance in detecting anomalies, achieving a 97.6% accuracy even when the magnitude mixed anomaly signals are very minute. Thus our proposed RENOWNED offers a robust way to enhance the reliability and energy efficiency of safety-critical automotive applications prevalent in IoV. © 2014 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Towards an Explainable Artificial Intelligence approach for smart grid systems

引用

Discover Artificial Intelligence 2025年第1期5卷 1-20页

作者： Alfayan, Mahmoud Hagras, Hani School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

As global energy demands escalate, effective management of electrical grids and reducing carbon emissions have become critical objectives. This paper proposes a novel system which employs Explainable Artificial Intelligence (XAI) to enhance the operational efficiency of smart grids by predicting energy consumption and optimising resource allocation accordingly. The integration of XAI addresses the complexities of grid management by providing transparency into AI-driven predictions, thus fostering user trust and facilitating informed decision-making. The main contributions of this work include evaluating the status of smart energy grid management and presenting a pathway for integrating XAI to enhance these practices. Using a real-world dataset with variables related to operations, environment, and time, our system employs a type-2 fuzzy logic XAI based system to generate clear, interpretable predictions of energy demand. The transition from a Type-1 to a Type-2 fuzzy logic system resulted in enhanced prediction performance, as evidenced by a reduction in the root mean square error (RMSE) from 8.7734 to 5.9422, resulting in 32.2% enhancement in RMSE. The proposed system demonstrated comparable performance compared to conventional black-box models, including neural network. It also shows stakeholders how to interpret these predictions across a wide range of consumption scenarios, which emphasizes the importance of predictions in making the best use of resources. Ultimately, this paper presents a first step for the employment of XAI within smart grid management. © The Author(s) 2025.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Matching 5G Connected Vehicles to Sensed Vehicles for Safe Cooperative Autonomous Driving

引用

IEEE Network 2024年第3期38卷 227-235页

作者： Tang, Zuoyin He, Jianhua Yang, Kun Chen, Hsiao-Hwa Aston University School of Computer Science and Digital Technologies BirminghamB4 7ET United Kingdom Essex University School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

5G connected autonomous vehicles (CAVs) help enhance perception of driving environment and cooperation among vehicles by sharing sensing and driving information, which is a promising technology to avoid accidents and improve road-use efficiency. A key issue for cooperation among CAVs is matching communicating vehicles to those captured in sensors such as cameras, LiDAR, etc.. Incorrect vehicle matching may cause serious accidents. While centimeter level positioning is now available for autonomous vehicles, matching connected vehicles to sensed vehicles (MCSV) is still challenging and has rarely been studied. In this paper, we are motivated to investigate the MCSV problem for 5G CAVs, propose and assess solutions for the problem to bridge the research gap. We formulate the MCSV problem and propose two MCSV approaches to support cooperative driving. The first approach is based on vehicle registration number (VRN), which is unique to identify a vehicle and can be shared among CAVs for MCSV. VRN is hashed before sharing to protect privacy, and will be compared to the shared one for vehicle matching. The second MCSV approach is based on visual features of vehicle's external views, which are shared with other CAVs and compared to those obtained from visual sensors to match the vehicles of interest. A new MCSV dataset is developed to assess the effectiveness of the proposed approaches. Experiment results show that both approaches are feasible and useful, and they achieve a very low false positive rate, which is critical for cooperative driving safety. © 1986-2012 IEEE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Enabled Heterogeneous Semantic and Bit Communication

引用

IEEE Transactions on Wireless Communications 2024年第10期23卷 12949-12963页

作者： Zhang, Meng Zhong, Ruikang Mu, Xidong Liu, Yuanwei Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

A multi-user heterogeneous semantic communication (SemCom) and bit communication (BitCom) system is investigated. Each user can be served via either SemCom or BitCom for demanding semantic or bit data. Orthogonal/non-orthogonal multiple access (OMA/NOMA) techniques are employed to provide access for multiple users. Channel-based and user demand-based transmission protocols are proposed, where a joint optimization problem of the communication mode selection, frequency bandwidth and power allocation, and NOMA user pairing is formulated to maximize the long-term (equivalent) semantic throughput and user satisfaction, respectively. To solve the formulated problems: 1) channel-based transmission, a twin-delayed deep deterministic policy gradient with reference neuron enhanced Softmax (TD3-RNS) algorithm is proposed, where a fixed-value neuron is invoked to improve the training efficiency;2) user demand-based transmission, a transfer TD3-RNS (T2D3-RNS) algorithm is proposed, where the learned policy is transferred to address the sparse rewards and perverse incentive problem caused by the optimization objective and reward shaping, respectively. Simulation results demonstrate that: 1) proposed heterogeneous scheme outperforms the baselines which merely use SemCom or BitCom;2) compared to OMA, NOMA is more compatible with the proposed heterogeneous scheme;and 3) proposed algorithms outperform the benchmarks in channel-based and user demand-based transmission, respectively. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Towards Interpretable Machine-Learning-Based DDoS Detection

引用

SN computer science 2024年第1期5卷 115页

作者： Zhou, Qianru Li, Rongzhen Xu, Lei Nallanathan, Arumugam Yang, Jian Fu, Anmin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Electronic Engineering and Computer Science Queen Mary University of London London E1 4NS United Kingdom

The Internet is the most complex machine humankind has ever built, and thus, it is difficult to defend it from attacks. The most common attack to the Internet is DDoS attacks. With the growing popularity for QUIC protocol, DDoS detection tasks are increasingly rely on machine learning (ML), which is based on black-box model and cannot explain its decision. A interpretable and transparent ML model is the foundation of a trustworthy ML-based DDoS attack detection. Current ML interpretation methodologies in cyber intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable ML-driven omnipotent DDoS detection approach, based on knowledge compilation technologies. Details of rigorous interpretation calculation process for the ML model are presented, which include an accelerated prime implicant calculation method driven by knowledge compilation for the DDoS detection ML model, and a map, combine, and merge (M &M) algorithm to discretize continuous features into Boolean expression. The proposed Prime implicant reasons calculation algorithm has been tested on a DDoS LOIC and HOIC attack detection ML model with 100% accuracy, trained with real-life DDoS data. An exhaust list of explanations are given in detail as rules for the omnipotent DDoS intrusion detection learnt by the ML model used. As the ML interpretation method is an SAT problem-solving process, the explanations are rigorous and sufficient reasons for the ML model for DDoS attack detection, and are believed to shade light on DDoS detection research work in cybersecurity community. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS attack Explainable artificial intelligence Intrusion detection system Knowledge compilation Prime implicate

来源：评论

学校读者我要写书评

暂无评论

Optimizing V2G Dynamics: An AI-Enhanced Secure Protocol for Energy Management in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 312-320页

作者： Ahmed, Shafiq Anisi, Mohammad Hossein University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The rapid advancement of intelligent transportation systems and the growing demand for sustainable energy solutions have elevated the Vehicle-To-Grid (V2G) paradigm in Industrial Cyber-Physical Systems (ICPS). This paper presents an AI-Enhanced Secure Protocol for V2G Energy Management, integrating Artificial Intelligence (AI) through Long Short-Term Memory (LSTM) networks with advanced cryptographic techniques for optimizing energy distribution between smart grids and electric vehicles. This protocol enhances system security and device integrity, effectively countering cyber threats and physical tampering. Emphasizing practical applicability, it demonstrates scalability and versatility across various smart grid environments, marking a significant step in AI-integrated cybersecurity for sustainable energy management. Comparative analysis reveals reductions in computation and communication costs by 49.79% and 23.24%, respectively, highlighting the efficiency of the protocol and its potential to enhance smart grid security frameworks. © 2023 IEEE.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3832-3844页

作者： Gupta, Prashant K. Sharma, Deepak Andreu-Perez, Javier Bennett University School of Computer Science & Engineering Technology Uttar Pradesh Greater Noida201310 India Christian-Albrechts-University Kiel Department of Computer Science Kiel24118 Germany University of Essex Centre for Computational Intelligence School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom University of Jaen Spain

Scoring the driver's behavior through the analysis of his/her road trip data is an active area of research. However, such systems suffer from a lack of explainability, integration of expert bias in the calculated score, and ignoring the semantic uncertainty of various variables contributing to the score. To overcome these limitations, we have proposed a novel perceptual computing based unsupervised scoring system. The prowess of the proposed system has been exemplified in a case study of driver's scoring from telemetry data. Our proposed approach yields scores that showed a higher significant separability between drivers performing responsible or irresponsible (aggressive or drowsy) driving behaviors, than the formal method of computing these scores (a p value of 3.94×10-4 and 3.42×10-3, respectively, in a Kolmogorov-Smirnov test). Further, the proposed method displayed higher robustness in the bootstrap test (where 30% of original data were omitted at random) by providing scores that were 90% similar to the original ones for all results within a confidence interval of 95%. © 2020 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

TaxDiff: taxonomic-guided diffusion model for protein sequence generation

引用

science China(Information sciences) 2025年第4期68卷 385-386页

作者： Zongying LIN Hao LI Liuzhenghao LV Yu WANG Bin LIN Junwu ZHANG Zijun CHEN Calvin Yu-Chian CHEN Li YUAN Yonghong TIAN School of Electronic and Computer Engineering Peking University AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Peng Cheng Laboratory School of Chemical Biology and Biotechnology Peking University

Protein design aims to generate protein variants with targeted biological functions, which is significant in multiple biological areas, including enzyme reaction catalysis, vaccine design, and fluorescence intensity. Protein design contains two paradigms: sequence generation and structure generation. Recently, EvoDiff [1] proposed a universal designing paradigm, combining structure and sequence generation using the diffusion framework, which improves the protein design efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revisiting LARS for Large Batch Training Generalization of Neural Networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1321-1333页

作者： Do, Khoi Nguyen, Minh-Duong Hoa, Nguyen Tien Tran-Thanh, Long Tran, Nguyen H. Pham, Quoc-Viet Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02PN40 Ireland Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam University of Warwick School of Computer Science CoventryCV4 7AL United Kingdom The University of Sydney School of Computer Science DarlingtonNSW2006 Australia Kyung Hee University Department of Computer Science and Engineering Yongin446-701 Korea Republic of

This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the warm-up, tends to be trapped in sharp minimizers early on due to redundant ratio scaling. Additionally, a fixed steep decline in the latter phase restricts deep neural networks from effectively navigating early-phase sharp minimizers. To address these issues, we propose time varying LARS (TVLARS), a novel algorithm that replaces warm-up with a configurable sigmoid-like function for robust training in the initial phase. TVLARS promotes gradient exploration early on, surpassing sharp optimizers and gradually transitioning to LARS for robustness in later stages. Extensive experiments demonstrate that TVLARS consistently outperforms LARS and LAMB in most cases, with up to 2% improvement in classification scenarios. In all self-supervised learning cases, TVLARS achieves up to 10% performance improvement. © 2020 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：