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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics Research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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Parameter optimization for hovering multi-rotor drones-based free-space optical links

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Applied Nanoscience (Switzerland) 2023年第3期13卷 1981-1989页

作者： Mahdi, Abdullah Jameel Mazher, Wamidh Jalil Ucan, Osman Nuri Department of Electrical and Computer Engineering Engineering and Natural Sciences Faculty Altinbas University Istanbul Turkey

Multi-rotor (MR) Drones have recently emerged as a viable method for creating flexible free-space optical (FSO) connections. In this study, we look at how precise parameters optimization can be used to evaluate the benefits of using MR Drones for such nanometer links, with consideration of the combined effect of atmospheric turbulence along with pointing error factor h_p. The related parameters of h_p are the connection distance d and pointing error angle (PEA) θ. In contrast to past studies on the subject, this one increased the connection distance d between drones swarm for high values PEAs that had been got high Bit Error Rate (BER). The system consists of two channels type: Single Input–Single Output (SISO) and Multiple Input–Single Output (MISO), and was simulated using MATLAB 2020. The results showed that for PEA θ=10 ^{- 3} and 10 ^{- 2} rad, the connection distance d increased from 5 to 20 percent per one centimeter increasing in the receiver diameter D_r, and for PEA θ=10 ^{- 1} rad, the connection distance d increased few meters;therefore, it is not recommended to increase the receiver diameter D_r. © 2021, King Abdulaziz City for Science and Technology.

关键词： Bit Error Rate Healthcare system Hospitalize and healthcare Hovering Multi-Rotor Multi-rotor (MR)

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Advanced Ensemble Learning Classifiers for Power Transformer Fault Diagnosis with Imbalanced DGA Data 25

Advanced Ensemble Learning Classifiers for Power Transformer...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Moradi, Elahe Mahmoud, Karar Darwish, Mohamed M. F. Shahre Rey Branch Islamic Azad University Department of Electrical and Computer Engineering Tehran Iran Aswan University Faculty of Engineering Department of Electrical Engineering Aswan81542 Egypt Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Cairo11629 Egypt

ISBN: (纸本)9798350379648

Power transformers play a vital role in the reliability and functionality of power systems. In recent years, the growing complexity of power transformer fault diagnosis has driven the widespread adoption of machine learning classifiers. This paper explores the application of tree-based machine learning algorithms, including Decision Tree, AdaBoost, and CatBoost, to diagnose power transformer faults using Dissolved Gas Analysis (DGA) data, normalized via standard scaling. Specifically, the Duval Pentagon Method (DPM) is utilized as a fault classification tool. One of the key challenges addressed in this study is the inherent imbalance in DGA datasets, which is tackled effectively through the application of robust tree-based models. A comprehensive comparative analysis of these algorithms is performed, with their performance evaluated based on metrics such as accuracy, precision, F1-score, area under the curve (AUC), receiver operating characteristic (ROC) curve, and confusion matrix. The simulation results, obtained using Python, reveal that the CatBoost algorithm significantly outperforms its counterparts, achieving an impressive accuracy of 96.78%, F1-score of 96.78%, precision of 96.81%, and AUC of 99.79%. These findings highlight the effectiveness of CatBoost in handling imbalanced data and providing precise fault diagnosis, contributing to improved decision-making processes in power system maintenance and reliability management. © 2024 IEEE.

关键词： Distribution transformers

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Sliding mode control of three-phase AC/DC converters using exponential rate reaching law

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Journal of Systems engineering and Electronics 2022年第1期33卷 210-221页

作者： JEERANANTASIN Narin NUNGAM Suksun Department of Electrical and Computer Engineering Faculty of EngineeringKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Sliding mode control(SMC) becomes a common tool in designing robust nonlinear control systems, due to its inherent characteristics such as insensitivity to system uncertainties and fast dynamic *** modes are involved in the SMC operation, namely reaching mode and sliding *** the reaching mode, the system state is forced to reach the sliding surface in a finite *** major drawback of the SMC approach is the occurrence of chattering in the sliding mode, which is undesirable in most ***, the trade-off between chattering reduction and fast reaching time must be considered in the conventional SMC *** paper proposes SMC design with a novel reaching law called the exponential rate reaching law(ERRL) to reduce chattering, and the control structure of the converter is designed based on the multiinput SMC that is applied to a three-phase AC/DC power *** simulation and experimental results show the effectiveness of the proposed technique.

关键词： exponential rate reaching law(ERRL) multi-input sliding mode control(SMC) chattering reduction three-phase AC-to-DC converter

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Optimizing User-Centric Clustering and Pilot Assignment in Cell-Free Networks for Enhanced Spectral Efficiency

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IEEE Internet of Things Journal 2025年第13期12卷 23139-23153页

作者： Aboulfotouh, Ahmed Fadlullah, Zubair Md Fouda, Mostafa M. Ismail, Muhammad Niyato, Dusit Lakehead University Department of Computer Science Thunder BayON Canada Western University Department of Computer Science Ontario LondonN6G 2V4 Canada Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States Tennessee Tech University Faculty of Engineering Department of Computer Science CookevilleTN United States Nanyang Technological University School of Computer Science and Engineering Singapore

Cell-free networks have emerged as a new paradigm for beyond-5G networks, offering uniform coverage and improved control over interference. However, scalability poses a challenge in full cell-free networks, where all access points (APs) serve all users. This challenge is addressed by user-centric clustering, where each user is served by a subset of APs, reducing complexity while maintaining coverage. In this paper, we provide an analysis of the relation between the user-centric clustering and pilot assignment problems in cell-free networks, and introduce a formulation which decouples both problems enabling each to be solved independently. We present a general problem formulation for the user-centric clustering problem, allowing the use of diverse per-user and network-wide performance metrics. Specifically, we focus on one instance of this framework, utilizing per-user spectral efficiency and network-wide sum spectral efficiency (SE) as metrics. Additionally, we formulate the pilot assignment problem to minimize overall channel estimation error while considering the user-centric clusters in evaluating the desirability of pilot assignments, which leads to better performing solutions. Both problems are classified as binary nonlinear programs that are at least NP-hard. To solve these optimization problems, our proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and utilizes the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions surpass baseline solutions, leading to significant improvements in spectral efficiency. © 2014 IEEE.

关键词： Genetic algorithms

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua Science and Technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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Towards in-Depth Trustworthy Communications in NextG Networks 15

Towards in-Depth Trustworthy Communications in NextG Network...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Ibrahim, Yomna Abdel-Malek, Mai A. Azab, Mohamed Rizk, Mohamed R.M. Alexandria University Faculty of Engineering Department of Electrical Engineering Alexandria Egypt The University of Arizona Department of Electrical and Computer Engineering AZ United States Virginia Military Institute Department of Computer and Information Sciences VA United States

ISBN: (纸本)9798331507695

The current 5G Authentication and Key Agreement (5G-AKA) protocol remains susceptible to several vulnerabilities that can lead to massive privacy leakage. The absence of mutual authentication in the connection between User Equipment (UE) and Serving Network (SN) elements, represented by the Base Station (BS), opens doors for false-base station deployment and user tracking. Those vulnerabilities are exacerbated by unsecured signaling in the 5G-AKA signaling process. In this paper, we identify and discuss the issues above in the 5G-AKA protocol that affect user privacy and network reliability, especially those evident during the initial bootstrapping phase and the plaintext message exchange in the AKA process. The paper presents initial designs for potential solutions that can help counter the lack of authentication between the UE and the SN and secure the unshielded messages in the 5G-AKA signaling. Initial evaluation is demonstrated to assess the effectiveness and the potential of the presented approach. © 2025 IEEE.

关键词： Authentication Protocol

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