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RAN Explainable Anomaly Prediction for 6G Networks

RAN Explainable Anomaly Prediction for 6G Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Panagiotis Marantis Kostas Ramantas Christos Verikoukis Dept. of Signal Theory and Communications Universitat Politècnica de Catalunya Barcelona Spain Industrial Systems Institute Patras Greece Iquadrat Informatica S.L. Barcelona Spain Dept. of Computer Engineering and Informatics University of Patras Patras Greece Athena Research Center Athens Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly.

关键词： 6G mobile communication Time series analysis Key performance indicator Open RAN Predictive models Network architecture Prediction algorithms Forecasting Anomaly detection Long short term memory

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A semi-omnidirectional resonator for chipless RFID backscattered tag design 27

A semi-omnidirectional resonator for chipless RFID backscatt...

引用

27th International Conference on Telecommunications, ICT 2020

作者： Babaeian, Fatemeh Karmakar, Nemai Chandra Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (纸本)9781728165875

Chipless RFID technology with its low-cost printable tags is highly demanded to be used for sensing, identification, and tracking applications. However, many challenges are cooperating with this technology due to the passive nature of the tag. In the conventional backscattered chipless RFID tags, the tag is required to be aligned with the reader antenna to be able to detect the tag ID. Hence, any misalignment causes errors in tag detection. This issue is a barrier for many applications in « hich the directional of the tag is unknown to the reader, or the tag needs to he detected in motion. In this paper, a scmi-Omni directional resonator for chipless RFID tags is proposed, which has a very low sensitivity to its orientation in 3D rotational angles in both co-polar and cross-polar responses. It can be used to design cross-polar orientation insensitive tags which is highly needed for tag detection in a highly reflective environment. The realization of the resonator and a proposed orientation insensitive unit cell are provided in this paper. Also, a 6-bit tag with spiral resonator, printed on adhesive paper, was measured at a reading distance of 20 cm and the promising results are demonstrated. © 2020 IEEE.

关键词： Radar cross section

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Feature Distraction Based Backdoor Defense for Federated Trained Intrusion Detection System

Feature Distraction Based Backdoor Defense for Federated Tra...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yu-Wen Chen Bo-Hsu Ke Yen-Xin Wang Shih-Heng Lin Ming-Han Tsai Bo-Zhong Chen Jian-Jhih Kuo Ren-Hung Hwang Computer Systems Technology New York City College of Technology Brooklyn New York USA Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan College of Artificial Intelligence National Yang-Ming Chiao Tung University Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) incorporated with Federated Learning (FL) facilitates collaborative model training to secure user privacy and enhance data diversity, emerging as a promising solution to defend malicious flows. Yet, FL is vulnerable to trigger backdoor attacks. Existing methods have limited ability on IDS with FL for detecting or repairing a converged attacked model as they either require access to the training process or are dedicated to specific applications. To tackle the limitations, we propose a novel Features Distraction Defense Framework (FDDF) that does not interfere with the model training and requires only a few additional model inferences to protect users from the aforesaid attack. The experiment results show that FDDF can recover the accuracy effectively and preserve the accuracy of normal models.

关键词： Training Data privacy Accuracy Costs Federated learning Computational modeling Intrusion detection Feature extraction Data models Global communication

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Polarization-Insensitive Metalenses at Wavelengths in Ultraviolet Region 23

Polarization-Insensitive Metalenses at Wavelengths in Ultrav...

引用

23rd IEEE Electronics Packaging Technology Conference, EPTC 2021

作者： Halim, Bishoy I. Kanwal, Saima Queen 'S University Electrical and Computer Engineering Electrical and Computer Engineering Dept. Kingston Canada Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9781665416191

Metasurfaces in the ultraviolet band have attracted current research concern due to the expanding requirement for wearable UV optical and ultra-compact systems. The constraints of traditional plasmonic metasurfaces operating in transmission mode can be controlled by using an appropriate dielectric material. A metalens presents promising wavefront engineering for multiple applications. Metalenses have sophisticated advanced technology in the progress of miniaturized and integrated optical devices. However, metalenses using the Pancharatnam- Berry (PB) phase or resonance adjusting technique are limited to polarization reliance and for different applications, polarization-insensitive metalenses are extremely preferable. We present three designs of high-efficiency dielectric polarization-insensitive UV metalens using cylindrical nanopillars with focusing efficiency, offering full phase delay in a broadband spectrum of Ultraviolet light (380-400nm). The designed metalenses include titanium dioxide (cylindrical nanopillars on a glass substrate with geometrically different radii and present magnificent polarization-insensitive operation in the broadband UV band. It will considerably advance the miniaturization and integration of the UV photonic devices by limiting the use of Plasmonics structures that are undefended to the ohmic losses and absorption of the metals. The focusing efficiency of the designed metalenses is as high as 43%, 45%, and 47% for the designed wavelengths of metalenses lambda_{d}=396{n}{m}, 388 nm, and 380 nm, respectively. © 2021 IEEE.

关键词： Titanium dioxide

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Comparison of Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram Techniques to Classify Industrial Machine Sound

Comparison of Mel Frequency Cepstral Coefficient (MFCC) and ...

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Nur Fatinah Hafiz Syamsiah Mashohor M. H. S. E. M. A. Shazril M. Fadlee A. Rasid Azizi Ali Dept. of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Malaysia Wireless and Photonic Networks Research Centre Of Excellence (WiPNET) Universiti Putra Malaysia Serdang Selangor Malaysia

The utilization of sound recognition to detect the faulty machines are getting the interest to be explored with the technology advancement in IR 4.0. In this paper, we investigated the performance of two acoustic features in representing the sound of a fan in industrial machines, that is acquired from the MIMII dataset. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram techniques are explored and compared in this paper. The classification is performed using Convolutional Neural Network (CNN) with the input of images from both techniques. The dataset of the input images are the audio files of 6dB fans sound in industrial machine. The results suggest that Mel Spectrogram is an effective feature for faulty machine sound recognition when used with CNN model.

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Electricity Extraction from Unoccupied Land of Substation

Electricity Extraction from Unoccupied Land of Substation

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International Conference on Energy and Power engineering (ICEPE)

作者： Mohammad Rasel Fahmida Amin Safkat Islam Shah Mohazzem Hossain Dept. of High Voltage Substation Design and Quality Control PowerChina Hubei Electric Engineering Corporation Dhaka Bangladesh Dept.of High Voltage Substation Design and Quality Control Orient Energy Engineering Ltd Dhaka Bangladesh Dept. of Electrical Engineering and Control Systems University of Grenoble Alpes Grenoble France Dept. of Electrical Electronic and Communication Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9781665455787

In developing countries like Bangladesh, the optimum utilization of available sources is a prerequisite to meet up the rising electricity demand in recent years. This paper focuses on extracting clean electrical energy from an open-source, i.e., unoccupied land areas of an electrical utility called substation through analytical approach. The optimally sized PV is installed on those lands to harness electrical energy for further use. It provides significant energy support to the substation and its nearby consumers with better efficiency. This paper encourages increasing this type of solar installation in large numbers of national grid substations to reduce the country's overall electricity demand and shift dependency on traditional fossil fuel-generated electricity.

关键词： Degradation Renewable energy sources Power engineering Substations Government Solar energy Developing countries

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Multi-Model Federated Learning Optimization Based on Multi-Agent Reinforcement Learning

Multi-Model Federated Learning Optimization Based on Multi-A...

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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： S. Kaveh Atapour S. Jamal Seyedmohammadi S. Mohammad Sheikholeslami Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Department of Computer & Electrical Engineering Tarbiat Modares University Tehran Iran Concordia Institute of Information Systems Engineering (CIISE) Montreal Canada The Edward S. Rogers Sr. Dept. of Electrical & Computer Engineering University of Toronto Canada Department of Electrical Engineering Yazd University Yazd Iran

This paper addresses the problem of Multi-Model Federated Learning (MMFL) in a typical wireless network, where a cellular Base Station (BS) cooperates with multiple clients to simultaneously train several Machine Learning (ML) models. Accordingly, the objective of this paper is to make an efficient joint decision for client association and communication-computation resource allocation to optimize the performance of the MMFL algorithm. In this regard, an optimization problem is formulated to minimize the average global loss of ML models under clients' energy and delay constraints. It is shown that the problem is a mixed-integer optimization whose objective is implicit in terms of the decision variables. To solve the optimization problem, we propose a Multi-Agent Multi-Model Federated Learning (MAMMFL) scheme based on a cooperative multi-agent configuration to intelligently assign models and resources to clients. Specifically, the problem is first converted to a Markov Decision Process (MDP) problem, then it is divided into four sub-MDP problems, where each problem relates to a phase in MMFL. The reinforcement learning algorithm solves each sub-problem, and a team-Q algorithm is adopted to coordinate agents in a cooperative multi-agent setting. Simulation results show that the proposed method can outperform other baselines in terms of average global loss and resource consumption.

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Client-server Mobile System for Diagnosis of Asynchronous Motors 4

Client-server Mobile System for Diagnosis of Asynchronous Mo...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Chorna, Olha Kurliak, Petro Busher, Victor Chornyi, Oleksii Tytiuk, Valerii Zachepa, Iurii Institute of Education and Science in Electrical Engineering and Information Technologies Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Electrical Power Engineering Electrical Engineering And Electromechanics Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350395532

This paper presents client-server mobile system for diagnosis of asynchronous motors. The system is created as a mobile application for smartphones and tablets. The system provides processing of the measured values of the currents and voltages of the stator phases of the asynchronous motor. According to the processing results, the results of diagnostics and forecasting of the current state of asynchronous motors are calculated. The software runs on the Android OS. The main purpose is to predict the current state of AD, which has a parametric electromagnetic asymmetry of the stator phases. © 2023 IEEE.

关键词： Induction motors

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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International Electrical engineering Congress (iEECON)

作者： Pruek Vanna-Iampikul Visutr Boonnateephisit Somphop Limsoontharakul Jednipat Moonrinta Matthew Dailey Mongkol Ekpanyapong Department of Electrical Engineering Burapha University Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. Computational Science Prince of Songkla University Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. of Industrial Systems Engineering School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments.

关键词： Geometry Accuracy Laser radar Plantations Estimation Vegetation Production Real-time systems Time measurement Monitoring

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Brownian motion simulation of SiO2 abrasive nanoparticle on SiC substrate surface 10

Brownian motion simulation of SiO2 abrasive nanoparticle on ...

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10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

作者： Saenna, Soraya Khajornrungruang, Panart Blattler, Aran Permpatdechakul, Thitipat Suzuki, Keisuke Bakier, Mohammed A. Graduate School of Computer Science and Systems Engineering Kyushu Institute Technology 680-4 Kawazu Iizuka-shi Fukuoka820-8502 Japan Dept. of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology 680-4 Kawazu Iizuka-shi Fukuoka820-8502 Japan

Chemical-Mechanical Polishing (CMP) process has emerged as the method of choice for planarization in the semiconductor industry. The interaction force between the surface being polished and the abrasive nanoparticles plays a significant role in flattening the substrate surface in the CMP process. For example, when the SiO2 nanoparticle is distant from the SiC substrate surface, the interaction force between the surface and the nanoparticles is presented. This force could be analysed using Derjaguin and Landau and Verwey and Overbeek (DLVO) theory and particle Brownian motion. According to the simulation results, we found that very fine abrasive nanoparticle such as smaller than approximately 10 nm is comparatively approach to the surface being polished more certainly. © 2021 The Japan Society of Mechanical Engineers.

关键词： Silicon carbide

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