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2025 IEEE International Students' Conference on electrical, Electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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Electromagnetic Simulation of Radiated Fields Inside an Aircraft Cockpit 1

Electromagnetic Simulation of Radiated Fields Inside an Airc...

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1st International Conference on computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Osman, Husam Bray, Joey R. Antar, Yahia M.M. Royal Military College of Canada KingstonON Canada Royal Military College of Canada Department of Electrical and Computer Engineering KingstonON Canada

ISBN: (纸本)9798350351736

This paper presents the modelling and simulation of the electromagnetic environment inside and surrounding the confines of a fighter aircraft cockpit that is being subjected to electromagnetic radiation from portable electronic devices (PEDs) located within the cockpit. To assess the resulting electromagnetic fields and identify the potential risk and severity of electromagnetic interference, a commercial computational electromagnetic (CEM) solver will be used, along with a human phantom model. The simulated results will be compared to experimental field measurements that were taken in a real cockpit. © 2024 IEEE.

关键词： aircraft cockpit attenuation computational electromagnetics electromagnetic compatibility electromagnetic interference human phantom portable electronic devices

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A Review on Partial Discharge Detection Techniques in High Voltage Rotating Machines 50

A Review on Partial Discharge Detection Techniques in High V...

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

作者： Refaat, Shady S. Saleh, Mohammad AlShaikh 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 Department of Electrical and Computer Engineering Texas A&M University College Station United States

ISBN: (纸本)9781665464543

Monitoring partial discharges (PDs) in high voltage (HV) electrical equipment is an effective tool to prevent unscheduled shutdown and eliminate possible failures. The inception of PDs can take various forms within the insulation system and can be detected using established approaches. The most common cause of HV rotating machine failure is the initiation of PDs in the stator winding insulation system. Various online and offline techniques have been proposed to monitor, detect, diagnose, and locate PDs in HV rotating machines. This paper gives an overview of existing PD detection and localization technologies in HV rotating machines. A comparison of online and offline PD detection approaches is discussed in this paper. Finally, a brief review of the methods of PD classification is provided. © 2024 IEEE.

关键词： Machine windings

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A Bayesian Model Based on Link Distribution Features for Multitarget Passive Localization in Visible Light Sensing

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IEEE Sensors Journal 2025年第11期25卷 20037-20050页

作者： Zhang, Shuai Zhang, Liyi Liu, Kaihua Wang, Ya Tianjin University of Commerce School of Information Engineering Tianjin300134 China Tianjin University School of Microelectronics Tianjin300072 China Tianjin Renai College Information Engineering Department Tianjin301636 China Texas A&M University J. Mike Walker '66 Department of Mechanical Engineering The Department of Electrical and Computer Engineering Department of Biomedical Engineering College StationTX77843 United States

Passive localization using visible light (VL) sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multitarget positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shadowed link information of candidate targets. Numerous simulation results show that the proposed multitarget passive localization scheme can improve the problem of false target localization in multitarget localization scenarios. And it also can achieve outstanding performance in localization accuracy. © 2025 IEEE.

关键词： Sensors Location awareness Transmitters Light emitting diodes Accuracy Bayes methods Vectors Receivers RF signals Time measurement

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on electrical, Electronics and computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

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computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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Decision Tree-Based IoT Botnet Attack Detection 2

Decision Tree-Based IoT Botnet Attack Detection

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors. © 2024 IEEE.

关键词： Forecasting

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Physics-guided Deep Learning for Distribution Network SE 3

Physics-guided Deep Learning for Distribution Network SE

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3rd International Conference on New Energy and Power engineering, ICNEPE 2023

作者： Chen, Shiqi Yang, Dechang Dehghanian, Payman China Agricultural University College of Information and Electrical Engineering Beijing China Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350393224

With the rapid advancement of modern power systems, the structure and operation of the grid have become increasingly complex, demanding higher levels of real-time accuracy and efficiency in state estimation (SE). To address the limitations of traditional SE methods for distribution networks, which often rely heavily on model precision, exhibit lower estimation accuracy, and have slower response times, a novel approach called Physics-Guided Temporal Convolutional Network (PGTCN) is proposed. The PGTCN method combines the strengths of data-driven techniques with the physical advantages of model-driven approaches. It begins by training a Temporal Convolutional Network (TCN) model using historical operational data from the distribution network. The model's predictions of the state variables are then integrated into the power flow equations, allowing for the examination of their consistency with the underlying physical relationships. Through this process, the PGTCN model achieves synergy between data-driven and model-driven methodologies, resulting in higher accuracy and improved estimation precision. Simulations conducted on a three-phase unbalanced distribution system with IEEE 14 nodes demonstrate the superior performance of the proposed SE method. Overall, the PGTCN approach offers a promising solution to the challenges posed by the ever-evolving landscape of modern power systems, promptly ensuring robust and accurate SE. © 2023 IEEE.

关键词： deep learning power system State estimation Temporal Convolutional Network (TCN)

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A Reliability Oriented Method for Optimal Placement of Sectionalizing Switches Using BPSO 17

A Reliability Oriented Method for Optimal Placement of Secti...

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17th International Conference on Protection and Automation in Power System, IPAPS 2023

作者： Fayyazi, Saeed Azad-Farsani, Ehsan Isfahan University of Technology Golpayegan College of Engineering Department of Electrical and Computer Engineering Golpayegan Iran

ISBN: (纸本)9798350311532

Sectionalizing switches and lines play an essential role in improving the performance of the distribution automation system as well as reducing the outage duration of power grid customers. The operation of the sectionalizing switches and whether the lines are energized or de-energized are also dependent on each other. Considering the structural complexities related to the mathematical modeling of such problems, optimizing the planning and performance of switches and lines usually requires the use of heuristic and meta-heuristic approaches or oversimplification of network complex topology. To deal with such issues, this research presents an efficient computational model for reliability-based simultaneous switching in distribution networks with complex topologies. © 2023 IEEE.

关键词： Electric power distribution

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Monolithic Integration of Silicon Quantum Photonics and Electronics in a 45nm SOI CMOS Foundry Platform

Monolithic Integration of Silicon Quantum Photonics and Elec...

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2024 Optical Fiber Communication Conference, OFC 2024

作者： Kramnik, Danielius Wang, Imbert Fargas Cabanillas, Josep M. Ramesh, Anirudh Gluhović, Ðord Buchbinder, Sidney Zarkos, Panagiotis Adamopoulos, Christos Kumar, Prem Popović, Miloš A. Stojanović, Vladimir M. Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Center for Photonic Comm. and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

Silicon photonics can enable useful quantum information processing, but scaling the interface to calibration and control circuits remains challenging. We monolithically integrate quantum photonics with electronics in ... 详细信息

ISBN: (纸本)9781957171326

关键词： Silicon photonics

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