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Self-adaptive Bat Algorithm With Genetic Operations

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Gesture recognition using elastic data glove based on FFT-BIGRU 9

Gesture recognition using elastic data glove based on FFT-BI...

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9th International Symposium on Advances in electrical, Electronics, and computer engineering, ISAEECE 2024

作者： Tao, Yehao Fan, Jianbo Shang, Jie Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9781510683303

Gesture recognition, as one of the important components of human-computer interaction, has a wide range of applications in important fields such as rehabilitation medicine, machine control, and virtual reality. Gesture recognition based on wearable data gloves has attracted widespread attention due to its strong universality and scalability. This paper utilizes an elastic data glove independently developed by our team to collect data. The glove integrates independently developed high-precision stretchable angle sensors and common inertial sensors, which can effectively recognize the bending and spatial position of the fingers. The innovative use of angular velocity thresholds for gesture segmentation, the utilization of linear interpolation and combination mean for data resampling, and the development of a network structure combining Fast Fourier Transform (FFT) with Bidirectional Gated Recurrent Unit (BIGRU) not only effectively captures the periodic patterns in the data but also handles long sequence dependencies. Achieving an accuracy of 94.70% in the classification of 14 sign language gestures, this approach outperforms other classical models. Ablation experiments have validated the effectiveness of the proposed network modules and stretchable angle sensors. © 2024 SPIE.

关键词： Gesture recognition

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Next Generation Memristor Reservoir Computing 3

Next Generation Memristor Reservoir Computing

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Nikiruy, K. Ivanov, T. Ziegler, M. Rossetti, D. Corinto, F. Ascoli, A. Tetzlaff, R. Demirkol, A.S. Schmitt, N. Micro-and Nanoelectronic Systems Tu Ilmenau Department of Electrical Engineering and Information Technology Ilmenau Germany Turin Italy Institute of Circuits and Systems Tu Dresden Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9798350378009

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators. © 2024 IEEE.

关键词： Memristors

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A real-time detection of battery pole before welding based on improved YOLOX 24

A real-time detection of battery pole before welding based o...

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2024 7th International Conference on computer Information Science and Artificial Intelligence, CISAI 2024

作者： Tian, Hongling Fang, Zaojun Zheng, Tianjiang Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo China

ISBN: (纸本)9798400707254

Rapid and accurate detection of the power battery pole area before welding is the prerequisite for accurately locating the welding starting point, and its performance determines the assembly efficiency and quality of the battery module. In view of the complex welding environment, low color contrast, and small area ratio, an improved model based on YOLOX is proposed. A multi-scale mixed attention module is introduced after the backbone feature to infer attention from different scales and enhance the key features of the target. A complex bidirectional fusion strategy that is conducive to small target detection is introduced at the neck of the model. The feature gradient flow is enriched through deep separable convolutions of multiple kernel sizes, and the original features are effectively utilized with the help of residual structures and multi-scale information is integrated. Experiments have shown that the optimized model is more stable to various environmental interferences while maintaining lightweight. The detection accuracy of the model is improved by 4.13% compared with the baseline model, the parameters are 6.27M, and the detection speed is 93 FPS. The overall performance is better than other models, providing an effective solution to the problem of welding initial point detection in power battery assembly. © 2024 Copyright held by the owner/author(s).

关键词： Poles

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Data Driven Vibration Control:A Review

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1898-1917页

作者： Weiyi Yang Shuai Li Xin Luo Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Faculty of Information Technology and Electrical Engineering University of OuluOulu 90570 VTT-Technology Research Center of Finland Oulu 90570Finlan College of Computer and Information Science Southwest UniversityChongqing 400715China

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic *** shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC *** convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration *** on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of *** doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

关键词： Data driven vibration control(DDVC) data science designing method feedforward control industrial robot input shaping optimizing method residual vibration

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Guest Editorial Introduction to the Special Section on Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases in Vehicular Networks

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IEEE Transactions on Vehicular technology 2024年第7期73卷 9221-9225页

作者： Dev, Kapal Chih-Lin, I. Marojevic, Vuk Khowaja, Sunder Ali Lien, Shao-Yu Wang, Yue Munster Technological University Department of Computer Science China Mobile Research Institute Mississippi State University Electrical and Computer Engineering University of Sindh Faculty of Engineering and Technology National Chung Cheng University Samsung Electronics

Cellular vehicle-to-everything (C-V2X) was introduced to support autonomous driving through 5G and beyond networks. C-V2X leverages cellular network infrastructure to integrate vehicle-to-network, vehicle-to-pedestrian, vehicle-to-infrastructure, and vehicle-to-vehicle communications. It has been suggested that Open RAN can be used to achieve the latency requirements essential to realize C-V2X as it achieves real-time optimization through the use of AI in Near real-time RAN Intelligence Controller (Near-RT RIC). The Open RAN will allow the access to historical traffic data or acquisition of data from vehicles. The data will then be transferred to Near-RT RIC for detecting network anomalies while maintaining reliable communication, which is essential for realizing autonomous driving. Open RAN also supports non-real-time RAN intelligent controller (Non-RT RIC) that allows more complex ML workflows such as policy-based feature extraction and optimization to guide vehicles when real-time acquisition is not available. Open RAN provides support for edge cloud, i.e. Open Cloud that helps to interface the Near-RT RIC with Open RAN central unit's user and control plane. Together, the Open RAN and C-V2X are considered to be the key-enabling technologies for achieving low-latency in autonomous vehicular communication networks. The issue attracted over 100 high-quality submissions from all over the world, among which 12 original contributions were eventually selected for publication. The novelty and key contributions of these articles are summarized as follows. © 1967-2012 IEEE.

关键词： Special issues and sections Vehicular ad hoc networks Cellular networks Autonomous driving 5G mobile communication Radio access networks Open RAN

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Unsupervised Vessel Segmentation Technique Using LTP and PCA 4

Unsupervised Vessel Segmentation Technique Using LTP and PCA

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4th International Conference on Communication Technologies, ComTech 2023

作者： Faridoon, Fatima Khan, Khurram Umair, Mir Yasir Mahmood, Zahid COMSATS University Department of Electrical and Computer Engineering Abbottabad Islamabad Pakistan GIK Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering KPK Topi Pakistan National University of Sciences and Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350337211

This paper presents a retinal vessels segmentation method than can be used by the ophthalmologists to diagnose different retinal diseases, such as thickening, thinning, or splitting. The proposed method consists of various interconnected steps. Initially, the G-channel is isolated from the input colored RGB image. Later, the Principal Component Analysis (PCA) is applied on the Green channel. Next, the Local Ternary Pattern (LTP) is used that feeds the processed image to the Morphological Filter Bank (MFB). The MFB uses various filters, such as spur, majority, and opening operations to segment the vessels image. Simulations are performed on the DRIVE and the STARE datasets. The proposed method yields mean accuracy of 94.07% and 93.45% on DRIVE and STARE datasets, respectively. Moreover, the proposed method is computationally less complex as compared to the few published methods. © 2023 IEEE.

关键词： Principal component analysis

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