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SSRN 2023年

作者： Wei, Zhenchun Fu, Yu Yuan, Xiaohui Lyu, Zengwei Zhang, Yu Shi, Lei School of Computer Science and Information Engineering Hefei University of Technology 193 Tunxi Road Anhui Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology 193 Tunxi Road Anhui Hefei230009 China Department of Computer Science and Engineering University of North Texas 3940 N. Elm DentonTX76203 United States

Cooperative edge caching has emerged as a promising solution to alleviate the traffic burden of backhaul and improve the Quality of Service (QoS) of 5G applications in the 5G era. Several cooperative edge caching methods alleviate the traffic burden of backhaul by transmitting contents between edge nodes cooperatively, thereby reducing the delay of content transmission. However, they only consider the cooperation on content transmission and do not consider the cooperation on cache replacement, which can not control content redundancy in the cooperative edge caching system and reduce the utilization of cache space. In this paper, we propose the cache replacement strategy of cooperative edge caching based on the Discrete Multi-Agent Deep Deterministic Policy Gradient (Discrete MADDPG) algorithm. We first propose a cooperative edge caching model to maximize the system throughput. Then we formulate the cache replacement process of the cooperative edge caching system to a Markov Game (MG) model. Finally, we use the cache replacement strategy based on the Discrete MADDPG algorithm to solve the MG model and control content redundancy. The simulation results show that the proposed algorithm achieves a higher system throughput and can control content redundancy. © 2023, The Authors. All rights reserved.

关键词： Redundancy

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Efficient Path-Following for Urban Logistics: A Fuzzy control Strategy for Consumer UAVs under Disturbance Constraints

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IEEE Transactions on Consumer Electronics 2025年

作者： Shao, Xingling Du, Jun Xia, Yi Zhang, Zekai Hou, Xiangwang Debbah, Merouane North University of China School of Electrical and Control Engineering Taiyuan030051 China State Key Laboratory of Extreme Environment Optoelecronic Dynamic Measurement Technology and Instrument Taiyuan030051 China Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University State Key laboratory of Space Network and Communications Beijing100084 China National University of Defense Technology College of Intelligence Science and Technology Hunan Changsha410073 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Khalifa University of Science and Technology KU6G Research Center Abu Dhabi127788 United Arab Emirates

The adoption of consumer unmanned aerial vehicles (UAVs) for logistics transportation is a key aspect of the emerging low-altitude economy, yet it faces significant challenges. Urban environments introduce complex obstacles, including dense buildings and unpredictable wind disturbances, while existing control methods struggle to balance path-following accuracy, disturbance rejection, and communication efficiency. To meet these demands, this paper proposes a quantized fuzzy learning path-following control for consumer UAVs. Firstly, a hysteresis quantized fuzzy disturbance observer (HQFDO) is proposed where the disturbances are approximately estimated by a neural network. Notably, a hysteresis quantizer is employed to reduce the communication bandwidth occupation by discretizing disturbance observations. Subsequently, a distributed velocity controller and a heading angle controller are designed to tackle the geometric and dynamic tasks separately. Specifically, the velocity controller introduces a projective arc length error to mitigate inefficiencies and safety risks associated with frequent acceleration and deceleration switches. Compared to conventional techniques, the proposed approach improves transient performance, enhances path attractivity, and optimizes communication resource utilization. Theoretical stability analysis is provided, and simulations validate the effectiveness of the proposed control strategy. © 1975-2011 IEEE.

关键词： Target drones

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Surface crack characterization using laser nonlinear ultrasonics based on the bispectrum

Surface crack characterization using laser nonlinear ultraso...

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作者： Liu, Hui Wang, Zhaoba Zheng, Bin National Key Lab for Electronic Measurement Technology North University of China Taiyuan030051 China School of Information and Communication Engineering North University of China Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

Laser nonlinear wave modulation spectroscopy (LNWMS) has been used to detect nonlinear ultrasonic signatures caused by fatigue cracks in materials. The nonlinear feature extraction from the spectral plot plays a significant role in LNWMS. This paper uses the bispectrum to distinguish harmonically related signals which result from nonlinear interactions and to remove those which do not. A feature called bispectrum damage factor (BDF), which is defined as the ratio of the bispectrum peak difference value of the damage and intact signals to the bispectrum peak value of intact signals at frequencies F 1, F 2, is extracted from the bispectral plot to characterize the crack features. The basic premise of the proposed laser nonlinear wave modulation bispectrum (LNWMBS) is that this BDF value will vary regularly as the level of crack-induced nonlinearity increases. A finite element model and a noncontact laser ultrasonic system have been built for LNWMBS measurement. The simulation results show that the BDF value is sensitive to crack depth and angle, and is insensitive to crack length and detecting position. BDF values of reflected Rayleigh waves are positive and approximately linearly increase with an increase of the crack depths in the range of 0.1 λ ∼ 0.6 λ (λ is the corresponding wavelength of adopted frequency);meanwhile, the values of the transmitted waves are negative and decreasing. The BDF values are larger than the crack angle at 90° when they are less than 90°, which is beneficial for crack detection. The proposed LNWMBS technique has been successfully tested for detecting artificial sub-millimeter surface-breaking cracks in aluminum alloy plates and closed fatigue cracks of torque load simulator connection blocks. © 2020 IOP Publishing Ltd.

关键词： Finite element method

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RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation measurement and Classification for ISAC Applications

arXiv

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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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CB-Path2Vec: A Cross Block Path Based Representation for Software Defect Prediction 6

CB-Path2Vec: A Cross Block Path Based Representation for Sof...

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6th IEEE International Conference on Computer and communications, ICCC 2020

作者： Zhang, Xiyu Lu, Yang Shi, Ke School of Computer Science and Information Engineering Hefei University of Technology China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

ISBN: (纸本)9781728186351

Software defects are common in software projects and threaten the security of software. Software defect prediction has been proven to be an effective method to solve software security problems, assisting developers in finding potential errors and effectively allocating test resources, in the early stage of the software life cycle. Traditional defect prediction models are designed based on hand-designed metric features, but these features usually cannot capture the semantic and structural information of code. In order to solve this problem, this paper proposes a novel software defect prediction model called CB-Path2Vec, which can automatically learn features composed of Cross Block path for representing source code and use them for software defect prediction. An evaluation on the public PROMISE dataset shows that on average CB-Path2Vec improvements over the state-of-the-art method both on within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). © 2020 IEEE.

关键词： Forecasting

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Multi-task Guided Blind Omnidirectional Image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： Image quality

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Public opinion analysis of public figures based on constrained logistic regression and grey relational analysis

Public opinion analysis of public figures based on constrain...

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9th International Symposium on Test Automation & Instrumentation (ISTAI 2022)

作者： T. Shao Q. Luo School of information and Communication Engineering Beijing Information Science and Technology University Beijing People's Republic of China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing People's Republic of China

In view of the limitations of subjective weighting method such as arbitrariness, a weight setting method based on logistic regression is proposed for the weighting of character attributes. Basic character attributes such as name and gender are selected to extract attribute features from public figures in different fields. Using the cross entropy loss function as the objective function, a convex optimization problem with constraints is constructed. In order to ensure the generalization ability of the model, Tikhonov regularization is added, the problem is solved by augmented Lagrange multiplier method (ALM) algorithm, and the effects of different penalty factors on the convergence speed are compared to obtain the attribute weight. Simulation results show that the weight obtained by this method has an accuracy of 97.2% for the classification of public figures in different fields. In addition, the advantages of the proposed method are analyzed by comparing with the entropy weight method. The weighted score of public figures in news public opinion is comprehensively evaluated by grey correlation model, so that public opinions can be analysed.

关键词： entropy feature extraction grey systems logistics optimisation regression analysis

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An IoT Based Safe Assembly Point Alert System 1

An IoT Based Safe Assembly Point Alert System

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Mangayarkarasi, T. Umapathy, K. Sivagami, A. Subitha, D. Department of Instrumentation and Control Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Tamil Nadu Kanchipuram India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai Thandalam India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai India

A large population depends on many industries for its livelihood like electrical, embedded, software product sectors. An IoT environment includes feedback phones that use smart devices, such as MCU (microcontroller unit), sensors and different sensors, collect, distribute, and act on messages they collect from their environment. IoT systems share the sensory data they receive when data has been either sent to the cloud for global applications or analysis by linking to an IoT gateway or other edge device. For the electrical maintenance occur in the industries will need a specific alert signal. The loss of manufacturing workers/workers may be controlled by a specific, minor accident. Therefore, it is essential to provide an emergency device able to detect the non-safety state of coal and produce an alarm to reduce the possibility of risks in the system. We are proposing an IoT-based alert system via mobile phones, as smartphones' general usage is very common in the industries rather than using a walkie-talkie. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Online Gyro Bias Estimation from Single Vector measurements Using Regression Models

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IFAC-PapersOnLine 2023年第1期56卷 252-257页

作者： Bowen Yi Lei Wang Weidong Zhang Australian Centre for Field Robotics The University of Sydney NSW 2006 Australia State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China School of Information and Communication Engineering Hainan University Haikou 570228 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity - both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.

关键词： Nonlinear systems observer parameter identification robotics

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Residential energy demand response management algorithm considering consumer usage patterns

Residential energy demand response management algorithm cons...

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第33届中国控制与决策会议

作者： Zhixuan Pi Xiaohui Li Yuemin Ding Min Zhao Zhenxing Liu School of Information Science and Engineering Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology School of Computer and Communication Engineering Tianjin University of Technology

Demand Response(DR) is a key attribute to enhance the operation of smart *** aims to schedule the various appliances in residence to let their energy consumption match the real-time electricity price provided by smart grid.A residential energy DR management algorithm is presented on the basis of real-time home energy management systems(RHEMS).It defines the user target price(UTP) to indicate different user requirements,learns the comfortable working time(CWT) of the appliances in the house by logistic regression and learns the comfortable working power(CWP) of the appliances in the house by difference exponential ***,the presented algorithm dynamically adjusts the maximum power threshold(MPT),and then automatically arranges the appliances operation to achieve buildings economic energy *** results show that the presented algorithm can significantly reduce the power demand during peak price period while ensuring the user's comfort,and the total energy cost can be reduced by16% compared with the user' s expectation.

关键词： Residential energy consumption Demand respond User target price Consumer usage patterns Logistic regression Differential exponential smoothing

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