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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Tian, Zengshan Liu, Yishan Li, Ze Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9798350326277

In this paper, we propose a human respiration rate detection system, Shortresp, which is innovatively predicated on short-range distance communication(SRC) system. Shortresp amasses channel state information (CSI) phase via Sparklink Low Energy (SLE) access technology, which subsequently undergoes processing utilizing the round-trip time (RTT) mode with channel fusion. To decompose the CSI phase signal and extract the respiration component, Shortresp employs the empirical wavelet transform (EWT). Distinguished by a more compact antenna array, lower power utilization, and heightened accuracy in detection, Shortresp achieves superior phase precision on account of the utilizing of SLE system, making it a more effective solution compared to other extant wireless breath rate detection systems. © 2023 IEEE.

关键词： channel fusion Empirical wavelet transformation Respiration detection Short-range communication

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Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach 23

Task Offloading Optimization for Mobile Edge Computing Syste...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Long, Changqing Meng, Wenchao Zhang, Yongmin College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China School of Computer and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350325959

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms. © 2023 IEEE.

关键词： collaborative neurodynamic optimization (CNO) mixed integer nonlinear program (MINLP) mobile edge computing (MEC) resource allocation task offloading

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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Detecting Bias in LLMs' Natural Language Inference Using Metamorphic Testing 24

Detecting Bias in LLMs' Natural Language Inference Using Met...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Li, Zhahao Chen, Jinfu Chen, Haibo Xu, Leyang Guo, Wuhao School of Computer Science & Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China Asiainfo Security Technology Co. Ltd Nanjing210012 China

ISBN: (纸本)9798350365658

The increasing integration of Large Language Mod-els (LLMs) into pivotal decision-making contexts highlights the necessity for rigorous scrutiny to ensure fairness, especially in tasks involving Natural Language Processing (NLP). This paper introduces a novel method employing Metamorphic Testing (MT) to discern bias within the Natural Language Inference (NLI) task performed by LLMs. Our approach establishes a structured framework for generating test cases using Metamorphic Relations (MRs), incorporating nuanced demographic attributes such as sex, race, occupation, age, and socioeconomic status into textual inputs. We conduct prelimi-nary experiment with 3 common datasets, over 400k test cases in 4 state-of-art LLMs. Through evaluation of model predictions across these MRs, we unveil potential biases indicative of fairness violations. Preliminary investigations conducted with prominent LLMs underscore the effectiveness of our method in illuminating disparities in model performance, thus providing valuable insights into the bias implications of LLMs in NLI tasks. Our method is intuitive and adaptable for other NLP tasks. To the best of our knowledge, this represents the first application of MT to NLI bias detection. © 2024 IEEE.

关键词： Natural language processing systems

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Indoor Terminal Localization Technology Using MIMO Networks 11

Indoor Terminal Localization Technology Using MIMO Networks

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Tian, Zengshan Zou, Jian Li, Ze Huang, Chenglin Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9798350326277

To overcome the problems related to network-centric systems, we propose an indoor terminal localization system based on MIMO networks. Firstly, by arranging AP's antennas at different locations in the room. Then we estimate the angle of departure (AOD) and time of flight (TOF) using a compressed sensing algorithm. The objective equations for localization are established by combining the AOD and TOF of multiple paths. Finally, we solve the nonlinear problem using a modified Levenberg-Marquardt algorithm. Experimental results in a natural environment demonstrate that our system achieves an accuracy of 0.34m with just a single AP. © 2023 IEEE.

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Dehazing Quality Evaluation Algorithm Integrating Dark Channel Theory and Image Depth Estimation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1119-1133页

作者： Huang, Jia-Yan Xu, Xin-Xin Luo, Shao-Ye Zhao, Wan-Qi Fan, Hao-Yi Grau, Antoni Guerra, Edmundo Wang, Chuan-Sheng Zhang, Fu-Quan New Engineering Industry College Putian University Putian351100 China The University of Sheffield SheffieldS10 2TN United Kingdom School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Image dehazing is an important research topic and hotspot in the fields of image processing and computer vision. Therefore, evaluating the performance of image dehazing algorithms has become an import research issue. However, current image quality assessment (IQA) algorithms only judge the final dehazed image and do not give an analysis of the capabilities of the image dehazing algorithm. In order to fill this gap, we propose an image dehazing quality assessment method that integrated the dark channel prior with image depth estimation. The proposed method first uses the dark channel prior to estimate the absolute amount of haze removed from each pixel as an indication of the performance of dehazing. Then, a dual-scale variance ratio method is introduced to estimate the scene depth of the image. Finally, the relative dehazing is calculated from the obtained absolute dehazing and the image depth to represent the capability of the dehazing algorithm. The method proposed in this paper first introduces the concepts of relative and absolute dehazing quantities, along with pioneering a pixel-level evaluation of dehazing outcomes. Extensive experiments show that the algorithm proposed in this paper is more in line with human subjective judgment than other IQA algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Pixels

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Distributed Real-Time and Fair Resource Allocation for 5G Dense Cellular Networks Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Chen, Yong Liu, Zhixin Yuan, Yazhou Guan, Xinping Hebei Key Laboratory of Industrial Computer Control Engineering China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Qinhuangdao066004 China Shanghai Jiaotong University School of Electronic Information and Electrical Engineering Shanghai200240 China

This paper considers a 5G dense cellular network scenario where wireless channels are dynamically changing. To address the resource allocation problems in 5G dense networks, a distributed real-time and fair resource allocation algorithm based on a single deep Q-network (DQN) and multiple local deep neural networks (DNNs) architecture (DRFRA-SDML) is proposed. Specifically, each downlink is modeled as an agent. Each downlink is equipped with a local deep neural network (DNN), allowing each agent to input locally observed information into the local DNN to select spectral subband and transmission power in real-time. In the core network, the global experience replay buffer is utilized to collect local experiences collected by all local agents to train a global weight vector of the train DNN, which is shared by all local DNNs. This ensures that the complexity of the algorithm does not depend on the size of the cellular network, resulting in excellent scalability. To achieve fairness between user devices, a reward function based on fairness has been designed. The simulation results how that, while ensuring fairness between user devices, DRFRA-SDML significantly outperforms IFP-FRA, FP-FRA-D, and Random in both sum rate and real-time performance. © 2014 IEEE.

关键词： Deep neural networks

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Recent Developments in Event-Triggered control of Nonlinear Systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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A Discriminative Probability-Based Weighted Algorithm for Human Action Recognition 11

A Discriminative Probability-Based Weighted Algorithm for Hu...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Jiang, Boya Tian, Zengshan Li, Ze Liu, Kaikai Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9798350326277

Traditional machine learning classification algorithms, such as DT, KNN, RF, and SVM, because of the curse of dimensionality, their accuracies are often limited in recognizing human actions. In this paper, we first propose a discriminative probability-based weighted algorithm, which uses discriminative probability in both offline and online phases and weights them to obtain a more reliable recognition result. Secondly, we collect large amount of human action Channel State Information(CSI) data samples using commercial WiFi devices. Experimental results show that regardless of which model we choose from DT, KNN, RF, and SVM, its recognition accuracy is significantly improved after combining with our proposed algorithm, with a maximum improvement of 8.2%. © 2023 IEEE.

关键词： action recognition CSI weighted algorithm

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