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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Lee, Semi Wendha, Brigitte Jung, Jaesung Department of Electronical and Computer Engineering Ajou University Suwon Korea Republic of

This study addresses the urgent challenge of expanding the electric vehicle (EV) charging infrastructure to meet the growing demand as EV adoption grows. The cost difference between fast and slow chargers is as much as sevenfold, and EV aggregators require assistance designing more cost-effective charging stations. The proposed method provides a method for determining the ratio of fast chargers in an EV charging station based on simulation analysis. The EV charging demand model is generated by MCMC sampling using Markov Chain characteristics, and a probability distribution based on actual data is applied. Ratio analysis of multiple charger combinations presents an insightful design for the EV aggregator. © 2024 IEEE.

关键词： Charging stations

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Cost-based Optimal Design and Scheduling Operation of Hybrid Energy Stations in South Korea Using Battery Energy Storage Systems 11

Cost-based Optimal Design and Scheduling Operation of Hybrid...

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11th International Conference on Power and Energy Systems engineering, CPESE 2024

作者： Yoon, Donghyun Jung, Jaesung Ajou University Dept of Electronical and computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350377439

The recent rapid increase in electric vehicles (EVs) and EV charging stations has led to the emergence of hybrid energy stations (ESs) that combine photovoltaic systems (PV) and battery energy storage systems (BESSs) to produce power with optimized efficiency. Various types of ESs with different operational methods are being studied. This paper proposes an optimal sizing design and operation method for an ES, from the perspective of BESS operational modes, for cost optimization. The proposed algorithm divides the BESS modes into charging, discharging, and idle states based on the price differentials of peak times, system marginal price (SMP), and EV time of use (TOU), as well as the trend of EV arrivals. Using this algorithm, the optimal sizes of PV and BESS are determined, and the operation of BESS is analyzed. We hope that this paper will be helpful in cost optimization study of hybrid ESs. © 2024 IEEE.

关键词： Charging stations

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Paste-and-Cut: Collective Image Localization and Classification for Real-Time Multi-Camera Object Detection 14

Paste-and-Cut: Collective Image Localization and Classificat...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Kang, Young Eun Kang, Woosung Lee, Taehun Chwa, Hoon Sung Dgist Electronical Engineering and Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350313277

In recent years, object detection has emerged as a crucial task in various real-world applications, including security surveillance, autonomous vehicles, and robotics. However, traditional object detection models face numerous challenges, such as inefficient image processing, inadequate resource utilization, and a failure to consider the different criticality of input images, making it difficult to apply these models for timely inferences in practical applications. To overcome these challenges, this paper proposes a novel object detection framework, called Paste-and-Cut, that utilizes two techniques, image merging (paste) and RoI patching (cut), to optimize resource utilization and improve object detection performance. Additionally, our approach incorporates a dynamic merge size and canvas size decision mechanism to adapt to varying object detection environments. Experimental results obtained from experiments conducted with the MOT dataset demonstrate the effectiveness of our approach in achieving real-time object detection with improved detection accuracy and without generating any deadline miss. As such, Paste-and-Cut provides a promising solution for efficient and accurate real-time object detection in multi-camera scenarios. © 2023 IEEE.

关键词： Deep neural networks Object detection Real-time systems

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Prediction and Comparative Analysis of Rooftop PV Solar Energy Efficiency Considering Indoor and Outdoor Parameters under Real Climate Conditions Factors with Machine Learning Model

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computer Modeling in engineering & Sciences 2025年第4期143卷 1215-1248页

作者： Gokhan Sahin Ihsan Levent Gültekin Isik Wilfriedvan Sark Sabir Rustemli Copernicus Institute of Sustainable Development Utrecht UniversityPrincetonlaan 8AUtrecht3584 CBThe Netherlands Computer Engineering Department Igdir UniversityIgdir76000Turkiye Electronical and Electronic Engineering Department Engineering FacultyBitlis Eren UniversityBitlis13100Turkiye

This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy *** predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the *** this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed *** values obtained under real field conditions were used to train the models,and the results were *** Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual *** like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.

关键词： Machine learning model multi-layer perceptrons(MLP) random forest(RF) solar photovoltaic panel energy efficiency indoor and outdoor parameters forecasting

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Development of Diode Triggering SCR-Based ESD Protection Circuit with Improved Trigger Voltage for Low Voltage Application

Development of Diode Triggering SCR-Based ESD Protection Cir...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Seo, U Yeol Kwon, Sang wook Gu, Jeong Seung Lee, Jeong Min Lee, Kwang Yeob Koo, Yong Seo Dankook University Department of Electronics and Electronical Engineering Yongin-si16890 Korea Republic of Seokyeong University Department of computer Korea Republic of

ISBN: (纸本)9798350371888

In this paper, we propose a new structure that adds diodes to the Silicon Controlled Rectifier (SCR). Additional Diodes are used to compare and analyze the electrical characteristics with the conventional SCR to show the improvement of the trigger voltage and holding voltage of the proposed element. It also compares and analyzes the electrical characteristics of 1 diode and 2 diodes. Due to the high holding voltage and low trigger voltage characteristics due to the diode-added device, it is expected to be suitable for low voltage applications. © 2024 IEEE.

关键词： Diodes

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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Investigating Machine Learning (ML) in Augmented and Virtual Reality: A Comprehensive Analysis in Prawn Farming

Investigating Machine Learning (ML) in Augmented and Virtual...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Painuly, Vaibhavi Kavitha, R. Ravela, Ratna Raju Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India Department of Electronical & Electronics Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

Commercial lakes ought to be kept up well in arrange to meet the expanding request for prawns universally and create at their best. This think about addresses the challenge by combining present day sensor innovation, expanded reality interfacing, and machine learning calculations. The plan employments state-of-the-art sensors to ceaselessly screen water quality, empowering proactive decision-making. Machine learning calculations analyze collected information to expect lake conditions, giving agriculturists with a see of potential future occasions. Increased reality representation encourages way better information collection and real-time decision-making through intuitively interfaces. This all-encompassing approach progresses efficiency, maintainability, and effectiveness within the prawn cultivating industry by revolutionizing current hones. © 2024 IEEE.

关键词： Adversarial machine learning

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A Terse Review of Heart Diseases Estimation Using Machine Learning Practices

A Terse Review of Heart Diseases Estimation Using Machine Le...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Pawar, Rahul Dash, Amit Kumar Mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

Heart diseases, a primary cause of global fatalities, represent a growing social matter on a worldwide scale. The integration of machine learning with advancing technology has greatly benefited the medical field, resulting in the provision of seamless solutions characterized by high accuracy, consistency and toughness. These advancements help the patient society through early detection and reduced medical care costs. They also offer the medical fraternity capable, accessible, precise and consistent prediction systems. This paper provides a comprehensive enquiry of machine learning algorithms engaged for the prediction and classification of several cardiovascular diseases, we explore several modalities, including physiological signals, heart sounds and electronic health records, to achieve effective heart disease recognition. We also provide insights into popular machine learning systems, hybrid systems and fuzzy systems in this context. It is noted in this review that Support Vector Machines (SVM) have gained popularity and widespread usage, with neural networks and ensemble techniques following closely behind in terms of their application. All these approaches are compared based on accuracy metrics. © 2024 IEEE.

关键词： Hybrid systems

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Navigation On Mobile Computing Devices with A Walking Using Object-Oriented Techniques to Communicate Stick 3

Navigation On Mobile Computing Devices with A Walking Using ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ramesh Prabhakaran, R. Guhan, P. Raj, E George Dharma Prakash Karthika, K. Angel Maanu, P. Mizoram University Department of Computer Engineering Mizoram India Panimalar Engineering College Department of Electrical and Electronical Engineering Chennai India Bharathidasan University School of Computer Science and Engineering Trichy India Karpagam College of Engineering Department of Information Technology Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331528201

Moving around in their surroundings is challenging for elderly people and people with visual impairments. The majority of functional sticks in use today are not intelligent and lack an innate ability to recognize obstacles. In an effort to address this issue, mobility and orientation specialists who assist the blind by teaching them to move freely and safely using their remaining senses are brought in. They can navigate around obstacles with the aid of guidance dogs as well. These techniques have drawbacks, such as the dogs' inability to comprehend complicated instructions, the high cost of maintaining these trained dogs, and the need to hire an expert. In light of the aforementioned difficulties, the "Smart walking stick"has become a potential answer. The "Mobile based walking stick"is a useful mobility aid with the capacity to call a relative who can help in the event of an accident, detect obstacles above ground level and pits using ultrasonic sensors, recognize when a collision has occurred, and call the user's mobile phone if it has been misplaced to share the location's GPS for use with GSM connectivity and to share with the user's mention contacts. With some visually disabled people as test subjects, a test case was run, and 95% of the time it was successful. © 2024 IEEE.

关键词： Ultrasonic sensors

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Mitigating GPU Side-Channels via Integrated Monitoring and Response 2

Mitigating GPU Side-Channels via Integrated Monitoring and R...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Lungu, Nelson Tembo, Simon Walubita, Ngula Patra, Sudhansu Shekhar University of Zambia Electrical and Electronical Engineering Lusaka Zambia Mukuni Secondary School Livingstone Zambia KIIT Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350317558

Graphics processing units (GPUs) are increasingly employed in security-sensitive domains, making side-channel attacks on shader execution a pressing threat. We present an integrated shader execution framework incorporating real-time performance monitoring, machine learning anomaly detection, and architecture-aware response primitives to detect and mitigate GPU side-channel vulnerabilities with minimal overhead reliably. A shader execution monitors non-invasively profiles hardware counters to extract execution features, passed to an isolation forest classifier for attack detection within 50ms. Transparent mitigations like cache flushing, warp throttling, and shuffle masking are triggered to disrupt identified side channels by leveraging GPU architectural capabilities. Evaluations against simulated attacks demonstrate that the framework reduces key leakage by 4-5x and achieves over 80% attack disruption for a 400ms attack at under 5% performance cost. The integrated GPU-specific security solution significantly enhances real-world shader execution protection against side-channel risks. This paper present an integrated shader execution framework that leverages advanced monitoring, anomaly detection, and finely tuned response mechanisms. © 2024 IEEE.

关键词： anomaly detection GPU security shader execution side-channel attacks

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