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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Sykam, Venkata Narendra Kumar Goyal, Ayush Nijim, Mais Mishra, Avdesh Hicks, David Texas A&m University Kingsville Department of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9798350365306

In the rapidly advancing era of self-driving cars, the imperative of ensuring robust cybersecurity measures to safeguard against evolving threats becomes paramount. This paper investigates the intricate cyber-physical systems within autonomous vehicles, emphasizing the critical role of 'AVTAR Cyber Shield' as a comprehensive cybersecurity framework for fortifying against potential vulnerabilities. The paper provides a basic understanding on how autonomous car manufacturers and/or technology providers like ***, Waymo, WeRide and Zoox implement safety measures which prompted us to introduce a new driver's self-preparation safety module before riding to qualitatively evaluate the preride risk(s) associated with self-driving cars. An exploration of current cybersecurity threats, and compliance with industry standards, is complemented by the identification of challenges such as technical complexities and human factors. The paper concludes with analyzing the existing statistical result focus on recent risk calculation, contributing to the future trends and innovations, proposing recommendations that advocate for continuous monitoring, best practices, and adaptive strategies to address evolving cybersecurity threats and traditional human un/known mistakes. This comprehensive examination contributes valuable insights to the discourse on securing the future of self-driving vehicles, offering guidance for researchers, practitioners, and policymakers navigating the complex landscape of autonomous transportation. © 2024 IEEE.

关键词： Shielding

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Investigation of XLPE Power Cable Insulation Degradation Under Chemical, electrical, and Mechanical Stress 50

Investigation of XLPE Power Cable Insulation Degradation Und...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Abuelrub, Abdelaziz Refaat, Shady S. Kameli, Sayed Mohammad Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9781665464543

High voltage (HV) power cables are exposed to various stresses, such as chemical, electrical, mechanical, and thermal. These stresses contribute to the eventual failure of electrical power cables in an unpredictable manner. Cross-linked polyethylene (XLPE) is an essential component of a power cable’s insulation layers, and unwanted stresses negatively affect the insulation layers’ performance in various ways. In this paper, three 3D Maxwell finite element analysis (FEA) models of a single core, 30 kV DC power cable are built to study the effects of chemical stress, electrical stress, and mechanical stress on the characteristics of polymer insulation material. The main parameter utilized for determining insulation health in this work is the polarization index. Temporal evolution in the polarization index is investigated, as aging-induced stresses degrade the XLPE’s structure. The severity of chemical stress, electrical stress, and mechanical stress is estimated and compared based on the polarization index, with a focus on chemical stress, highlighting the severity of the deformation caused within the inner polymeric structure of XLPE. The severity is obtained for the innermost and outermost insulation layers. It was discovered that the insulation layer’s proximity to the cable’s core is the main factor influencing degradation. Moreover, the obtained results successfully demonstrate the polarization index’s ability to detect degradation after the 95-day mark, indicated by the substantial increase in polarization index values, common between all stress types applied. © 2024 IEEE.

关键词： Crosslinking

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An Adaptive Metaheuristic Framework for Changing Environments 13

An Adaptive Metaheuristic Framework for Changing Environment...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ahmed, Bestoun S. Karlstad University Department of Mathematics and Computer Science Karlstad65188 Sweden Czech Technical University in Prague Faculty of Electrical Engineering Department of Computer Science Prague16627 Czech Republic

ISBN: (纸本)9798350308365

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF's capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem's development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm's performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems. © 2024 IEEE.

关键词： Adaptive Metaheuristics Dynamic Optimization Evolutionary Computation Optimization in Changing Environments Real-Time Algorithm Adaptation

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Improving Speed of MUX-FSM-based Stochastic Computing for On-device Neural Networks

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Journal of Computing science and engineering 2022年第2期16卷 79-87页

作者： Kang, Jongsung Kim, Taewhan Department of Electrical Engineering and Computer Science Seoul National University Seoul Korea Republic of

We propose an acceleration technique for processing multiplication operations using stochastic computing (SC) in ondevice neural networks. Recently, multiplexor driven finite state machine (MUX-FSM)-based SCs, which employ a MUX controlled by an FSM to generate a (repeated but short) bit sequence of a binary number to count up for a multiplication operation, considerably reduce the processing time of MAC operations over the traditional stochastic number generator (SNG) based SC. Nevertheless, the existing MUX-FSM-based SCs still do not meet the multiplication processing time required for the wide adoption of on-device neural networks in practice even though it offers a very economical hardware implementation. In this respect, this work proposes a solution that speeds up the conventional MUX-FSMbased SCs. Precisely, we analyze the bit counting pattern produced by MUX-FSM and replace the counting redundancy by shift operation, resulting in a shortening of the length of the required bit sequence significantly, together with analytically formulating the number of computation cycles. Through experiments, we have shown that the enhanced SC technique can reduce the processing time by 44.1% on average over the conventional MUX-FSM-based SCs. © 2022. The Korean Institute of Information Scientists and Engineers

关键词： Embedded systems

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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Analysis and Practice of Ideological and Political Education in computer Public Basic Courses in Architecture Universities Based on Machine Learning and Deep Learning 24

Analysis and Practice of Ideological and Political Education...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Education Digitalization and computer science, EDCS 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400710360

In response to the shortcomings of ideological and political education in the computer basic course of our school, the teaching team has conducted in-depth research on the connotation of ideological and political education in the course, accurately grasped the direction of ideological and political education in the course, innovatively proposed the"progressive method of ideological and political education", and constructed an integrated curriculum system of ideological and political education in the course;Using Orange3 software and machine learning algorithms to deeply explore the ideological and political elements of the curriculum, constructing an analytical model, formulating personalized teaching and learning strategies, and constructing a double-layer teaching evaluation system that emphasizes both morality and ability. Through the analysis of course data, it is shown that the effectiveness of ideological and political education in the curriculum has been comprehensively improved, and the quality of teaching has been comprehensively enhanced. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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A Transformer-Enabled Phonological Study of Genuine and Synthetic Voices

A Transformer-Enabled Phonological Study of Genuine and Synt...

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Reynolds, Sarah MacHado, Daniel Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona Beach United States

ISBN: (纸本)9798350390995

This study bridges the gap between traditional linguistic studies, particularly phonology, and modern Machine Learning (ML) developments. It explores the mutually beneficial interactions between the two domains: by utilizing Speech-To-Text (STT) models to aid in phonology and by integrating phonological insights into the evaluation of Text-To-Speech (TTS) generation. Phonology, the study of phonemes-fundamental sound units in human speech-is pivotal for understanding speech variations caused by accents, speech disorders, and individual pronunciation differences. Leveraging the International Phonetic Alphabet (IPA), this work analyzes speech patterns to enhance explainability and accuracy in TTS models, particularly those employing transformer architectures. This approach for phoneme analysis is tested on the Speech Accent Archive and synthetic speech samples generated by OpenAI's text-to-speech system. By employing the wav2vec model for phoneme detection and DeepPhonemizer for grapheme-to-phoneme conversion, we calculate phoneme distances using an adapted Wagner-Fischer algorithm. This analysis reveals significant differences in phoneme accuracy between native English speakers, non-native speakers, and synthetic speech, highlighting areas for improvement in synthetic speech generation. The results indicate that synthetic voices exhibit lower phoneme variation compared to human speakers, suggesting potential for enhancing both the naturalness and diversity of synthetic speech. This approach not only aids in refining TTS systems but also offers insights into developing more unique and personalized synthetic voices. Future work aims to refine ML tools, expand the phonological approach, and further integrate linguistic knowledge into TTS evaluation and improvement. © 2024 IEEE.

关键词： Speech enhancement

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Surface phase diagram of CsSnI3 from first-principles calculations

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Physical Review Materials 2024年第9期8卷 093401页

作者： Kejia Li Chadawan Khamdang Mengen Wang Department of Electrical and Computer Engineering Materials Science and Engineering Program

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge-carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.

关键词： Electronic structure Surface reconstruction Surface states Perovskites Semiconductors Density functional theory

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