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Outage Prediction and Improvement in 6G for UAV Swarm Relays Using Machine Learning

Progress In Electromagnetics Research B 2024年 107卷 33-45页

作者： Khalil, Hisham Ali, Gauhar Rahman, Saeed Ur Asim, Muhammad Affendi, Mohammed El Department of Intelligent Systems Faculty of Information Technology University of Lahore Pakistan EIAS Data Science and Block chain Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia School of Electronic Engineering Xidian University Xi’an China

The issue of signal outages in sub-THz frequency communication for future 6G networks is addressed by this research. A machine learning method is proposed, employing Random Forest and K-Means algorithms to predict the optimal frequency band and outage probabilities for UAV relays. Both space and frequency diversity are explored to enhance signal strength, and metasurface-carrying UAVs are introduced with a 16 × 16 mm2 design. This design significantly reduces the predicted outage probability from 0.1% to 0.0178%. Finally, triangular and hexagonal UAV swarm formations with metasurfaces are investigated, demonstrating improved performance through heatmap results. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Asymmetric Modulated Predictive Current Control for Dual Three-Phase PMSM With Improved Performance 10

Asymmetric Modulated Predictive Current Control for Dual Thr...

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10th IEEE International Power electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Li, Ze Zheng, Xingyu Xia, Jinhui Liu, Hangan Gao, Xiaonan Rodriguez, Jose Wang, Liling Zhang, Zhaoyan Liu, Zhiheng Hebei University College of Electronic and Information Engineering China Southeast University School of Automation China Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Sweden Universidad San Sebastian Faculty of Engineering Chile

ISBN: (纸本)9798350351330

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme. © 2024 IEEE.

关键词： Synchronous motors

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Light-Weight Gated Recurrent Unit for Electroencephalogram Emotion Classification

Light-Weight Gated Recurrent Unit for Electroencephalogram E...

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13th International Electrical engineering Congress, iEECON 2025

作者： Sitjongsataporn, Suchada Du, Wenzhang Sakarin, Pipat Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Faculty of Engineering and Technology Mahanakorn University of Technology Dept. of Computer Engineering Thailand

ISBN: (纸本)9798331543952

This experiment investigates the impact of reducing GRU units and dense layer neurons in a lightweight GRU architecture (LW-GRU-RU) compared to a baseline GRU model for electroencephalogram (EEG) emotion classification. A baseline GRU model is used as a reference, with an optimized GRU variant utilizing feature selection through a Random Forest-based algorithm. Experiments are conducted on a labeled emotion dataset, comparing accuracy and training efficiency across five trials. Results highlight the trade-off between model complexity, accuracy, and computational efficiency, providing insights for practical applications. Both models are evaluated on an emotion dataset for accuracy and training efficiency. The lightweight model achieves a competitive accuracy of 97.486% while reducing the average training time to 0.19 seconds per epoch, showcasing its potential for efficient real-world applications. © 2025 IEEE.

关键词： Baseline GRU model Electroencephalogram (EEG) signal GRU with Feature selection

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Securing Secondary Distribution with IoT-Based Smart Meters: Detection and Prevention of Power Theft 4

Securing Secondary Distribution with IoT-Based Smart Meters:...

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4th International Conference on electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Khan, Sher Dali Alamsyah, Nurwahyu Ul Haq, Qazi Mazhar Ahmad, Waqas Mufti, Naveed Haq, Muhamad Amirul Universiti Teknikal Malaysia Melaka Faculty of Electrical and Electronic Engineering Technology Melaka Malaysia Universitas Muhammadiyah Yogyakarta Information Technology Department Yogyakarta Indonesia Yuan Ze University Department of Computer Science and Engineering and IBPI Taoyuan Taiwan University of Engineering and Technology Department of Electrical Engineering Mardan Pakistan University of Engineering and Technology Department of Telecommunication Engineering Mardan Pakistan Universitas Muhammadiyah Surabaya Computer Science Department Surabaya Indonesia

ISBN: (纸本)9798350378368

Electrical energy distribution faces the significant issue of losses, with power theft being a main factor contributing to these losses. This difficulty is not limited to Pakistan but is also prevalent in several other nations worldwide. This study introduces a technique that utilizes smart meters to identify the approximate whereabouts of power theft. In addition to the detection of power theft, this study also presents a mechanism for the eradication of unauthorized users. The methodology used involves comparing the overall energy with the energy that is lawfully utilized. Wireless communication systems, such as Global System for Mobile Communications (GSM) and Network Repository Function (NRF), are used for communication between the meters. © 2024 IEEE.

关键词： NRF based Smart Meter Power Theft Elimination Power Theft Prevention Smart Meter

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PMU-Based Backup Protection in the Presence of Inverter-Based Resources

PMU-Based Backup Protection in the Presence of Inverter-Base...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Jegarluei, Mohammad Rezaei Salehi Dobakhshari, Ahmad Popov, Marjan Terzija, Vladimir Azizi, Sadegh University of Leeds School of Electronic and Electrical Engineering United Kingdom University of Guilan Faculty of Engineering Iran Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands Shandong University School of Electrical Engineering China

ISBN: (纸本)9781665471640

Increasing penetration of inverter-based resources (IBRs) undermines the performance of conventional protection systems since IBRs' fault characteristics are fundamentally different from those of synchronous generators. In this paper, a PMU-based backup protection method is proposed for power transmission systems with high penetrations of IBRs. The method involves replacing all IBRs and the line suspected to be faulty by proper nodal current sources. For accurately detecting the faulted line from the set of candidates, a residual-based index is proposed, utilizing the concept of superimposed circuits and the weighted least-squares method. The method's robustness in the face of influential factors, including input errors, settings, numbers, and locations of IBRs is investigated through extensive simulations on the IEEE 39-bus test system. © 2023 IEEE.

关键词： Inverter-based resource (IBR) superimposed circuits synchrophasor wide-area backup protection

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Security and Reliability Performance Analysis of Cognitive NOMA Network Under Outage Constraint of Multiple Primary Users 23

Security and Reliability Performance Analysis of Cognitive N...

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12th International Symposium on Information and Communication technology, SOICT 2023

作者： Pham Huu, Tung Tran, Hung Tran, Duc-Tan Dang, Viet-Hung Nhan Vo, Van Nguyen Dinh, Viet Faculty of Information Technology Hanoi University of Civil Engineering Hanoi Viet Nam Faculty of Computer Science Phenikaa Univerity Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa Univerity Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this paper, we conduct an investigation into the security and reliability aspects of a cognitive non-orthogonal multiple access (NOMA) network operating under outage constraints imposed by multiple primary users (PUs). Specifically, we examine a scenario where secondary users (SUs) have the capability to operate in NOMA mode, allowing them to harness the licensed spectrum band of multiple PUs, provided that the interference generated by SUs remains below a predefined threshold. Given that context, both SUs and PUs are susceptible in this spectrum-sharing environment, a potential threat arises in the form of an eavesdropper who seeks to exploit the secrecy of messages exchanged among SUs. Given this setting, we derive a power allocation policy for the SUs, formulate closed-form expressions for outage probability (OP) and intercept probability (IP) to assess system performance, and analyze the trade-off between reliability and security. Finally, we supplement our findings with numerical examples and discussions to illustrate the proposed concepts. © 2023 ACM.

关键词： Economic and social effects

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry 10

Image Acquisition and Deep Convolutional Neural Network Inco...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Weerasekara, Dinithi Bhagya Kahatapitiya, Nipun Shantha Sanjeewa, Nuwan Wathudura, Vimukthi Madushan Prasanna, Nuwan Kalupahana, Deshan Wijenayake, Udaya Silva, Bhagya Nathali Wijethunge, Akila Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Department of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331529048

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact. © 2024 IEEE.

关键词： Convolutional neural networks

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