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Different MPPT Algorithms for DC-DC Converter

Different MPPT Algorithms for DC-DC Converter

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Kumar, Boddepalli Hemanth Ghosh, Arnab National Institute of Technology Dept. of Electrical Engineering Rourkela India

ISBN: (纸本)9798331529833

The efficient and appropriate power flow technologies are necessary for better performance of renewable energy system applications. The comparative study of different MPPT algorithms, which are best for RES applications due to their influence on efficiency, low response time and the stability under different intervals of irradiance and temperature. When comes to this context, the key focus is evaluation of algorithms to the converter which is bidirectional by considering the desired operational conditions. The observations of this study are a forward step to optimization of systems with bidirectional converters in improving the reliability and sustainability. © 2025 ieee.

关键词： Artificial Neural Network (ANN) Bidirectional DC-DC Converter Fuzzy Logic Control (FLC) Maximum Power Point Tracking (MPPT) Perturb and Observe (P&O) Photovoltaic (PV) Pulse Width Modulation (PWM) Renewable Energy systems (RES)

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Artificial Bee Colony Optimization Techniques' Utilization for Intrusion Detection systems' Analysis 4

Artificial Bee Colony Optimization Techniques' Utilization f...

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4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Tokgoz, Emre State University of New York Computer Security Department FarmingdaleNY United States

ISBN: (纸本)9798331518882

A variety of nonlinear optimization techniques have been used for measuring effectiveness of Intrusion Detection systems (IDS). The variability of these optimization techniques resulted in several different approaches for different systems that depended on the IDS selection. There is a tremendous increase in the number of nature-inspired optimization techniques introduced in recent years and the associated applications in IDS appear recently after their introductions. Artificial Bee Colony (ABC) optimization is one of the optimization techniques that attracted many researchers, and a comprehensive review of ABC is needed. While there are few other reviews of ABC algorithms in the literature, the combination of ABC with other optimization techniques and improvement opportunities with these optimization techniques are not covered in these articles. Therefore, in this work, an extensive review of the ABC optimization techniques introduced for IDS analysis as well as the most recent techniques that are applied to IDS in combination with ABC will be covered. New research areas to be explored are recommended as a part of the conclusions and future works. This work demonstrates a clear overview of the IDS and nonlinear optimization applications along with the optimization applications and directions followed by the research community. © 2025 ieee.

关键词： Optimization algorithms

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Advanced Facial Emotion Classification with 135 Classes for Enhanced Cybersecurity applications 4

Advanced Facial Emotion Classification with 135 Classes for ...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Powers, Gregory Jin, Aobo Tonmoy, Abdul Basit Cao, Haowei Gohel, Hardik Deng, Qixin Wabash College Mathematics and Computer Science Crawfordsville United States University of Houston Victoria Computer Science Victoria United States Johns Hopkins University Electrical and Computer Engineering Baltimore United States

ISBN: (纸本)9798331518882

A key component of behavior analysis and human-computer interaction (HCI) is facial expression detection, which helps systems understand and react to human emotions more effectively and nuancedly. While previous research predominantly focuses on classifying seven or eight primary emotions, there has been limited exploration into more detailed and varied emotional expressions. In this paper, we address this gap by utilizing a recently published dataset that includes 135 distinct emotional classes, significantly increasing the complexity and granularity of emotion recognition tasks. To tackle this challenge, we propose a novel framework that combines U-Net and ResNet architectures, leveraging the strengths of each to capture intricate emotional cues and spatial features. Our approach achieves higher accuracy than current state-of-the-art (SOTA) methods, demonstrating its effectiveness for high-dimensional emotion classification in complex, real-world applications. © 2025 ieee.

关键词： computer vision deep learning facial emotion recognition

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Multi-Objective Optimization Scheme for Energy systems in Distribution Networks Based on Cloud-Edge-Device Collaborative Framework 5

Multi-Objective Optimization Scheme for Energy Systems in Di...

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5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Liu, Jingzhi Zhang, Jianming Yang, Hongyi Bao, Zhenghong Zhan, Fei Liu, Zhidong State Grid Qinghai Electric Power Research Institute Xining Qinghai China State Grid Qinghai Electric Power Company Xining Qinghai China

ISBN: (纸本)9798331533694

As energy systems evolve, single-system economic optimization no longer meets the growing multi-energy demands of users. To address this problem, this paper proposes a multi-objective optimization method for distribution network energy systems based on cloud-edge-device collaborative (CEDC) architecture. First, a CEDC system for the energy system of the distribution network is constructed in response to energy scheduling requirements, and edge-device clusters are used to achieve distributed management of energy system units. Second, based on the energy scheduling requirements, corresponding energy scheduling commands are generated for the target energy systems. These energy scheduling commands are decomposed into at least one computational task, then the computational tasks are assigned to the edge cluster control units of the corresponding target energy systems. Then, a multi-objective optimization scheme is designed, which jointly minimizes the total power supply cost of a single energy system and the total delay time across multiple energy systems. Finally, the scheme is solved by the particle swarm optimization (PSO) algorithm introducing the simulated annealing. Simulation results show that the proposed algorith can minimize the total energy cost and overall system delay. © 2025 ieee.

关键词： Power distribution networks

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Advanced Navigation and Payload systems for Underground Drone Operations

Advanced Navigation and Payload Systems for Underground Dron...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sundaramoorthy, Prabhu Gari, Shoaib Shaik Faridbhai Nayeem, Mohammad Nandu, Pusula Mohan, Kothapalli Joshitha Arun, Vijayakumar Electrical and Electronics Engineering Tirupati India

ISBN: (纸本)9798331529833

This paper explores advancements in underground drone technology, focusing on key systems that enable navigation, sensing, and communication in GPS-denied environments. It covers the integration of GPS, cameras, LiDAR, and inertial navigation systems, highlighting how each technology contributes to effective drone operations in complex underground settings. The payload capabilities, including sampling tools, robotic arms, and advanced sensors, enable diverse applications, from mining and environmental monitoring to infrastructure inspection. Furthermore, the study discusses an iterative optimization algorithm for task offloading and resource allocation within multi-drone networks, enhancing computational efficiency. By merging data from multiple sensors and applying innovative mapping techniques, these drones achieve precise navigation and obstacle avoidance. This work underscores the role of customizable payloads in expanding drone utility and ensuring operational efficiency, offering valuable insights for future developments in autonomous underground systems. © 2025 ieee.

关键词： Resource allocation

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management 5

Research on Optimization Algorithm and Simulation Model of O...

引用

5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Hu, Yinquan Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9798331533694

This paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. The algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. These experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications. © 2025 ieee.

关键词： Battery management systems

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Detection of Cyberattacks on the Metaverse based on First-order logic 6

Detection of Cyberattacks on the Metaverse based on First-or...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Al Amro, Sulaiman Qassim University Department of Computer Science College of Computer Buraydah51452 Saudi Arabia

ISBN: (纸本)9798331506520

There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual space that integrates various technologies, and therefore susceptible to cyber threats such as malware, phishing, and identity theft. This has led recent studies to propose the development of Metaverse forensic frameworks and the integration of advanced technologies, including machine learning for intrusion detection and security. In this context, the application of first-order logic offers a formal and systematic approach to defining the conditions of cyberattacks, thereby contributing to the development of effective detection mechanisms. In addition, formalizing the rules and patterns of cyber threats has the potential to enhance the overall security posture of the Metaverse and thus the integrity and safety of this virtual environment. The current paper focuses on the primary actions employed by avatars for potential attacks, including Interval Temporal Logic (ITL) and behaviour-based detection to detect an avatar's abnormal activities within the Metaverse. The research established that the proposed framework attained an accuracy of 92.307%, resulting in the experimental results demonstrating the efficacy of ITL, including its superior performance in addressing the threats posed by avatars within the Metaverse domain. © 2025 ieee.

关键词： Phishing

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Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Label Consistent Generalized Adaptive Weighted Recursive Lea...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class. © 2025 ieee.

关键词： Self-supervised learning

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Co-Designing NVM-based systems for Machine Learning and In-memory Search applications 24

Co-Designing NVM-based Systems for Machine Learning and In-m...

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43rd international conference on computer-Aided Design, ICCAD 2024

作者： Henkel, Jörg Siddhu, Lokesh Nassar, Hassan Bauer, Lars Chen, Jian-Jia Hakert, Christian Seidl, Tristan Chen, Kuan Hsun Hu, Xiaobo Sharon Li, Mengyuan Yang, Chia-Lin Wei, Ming-Liang Karlsruhe Institute of Technology Germany TU Dortmund Germany University of Twente Netherlands University of Notre Dame IN United States National Taiwan University Taiwan

ISBN: (纸本)9798400710773

With the rapid development of the Internet of Things, machine learning applications on edge devices with limited resources face challenges due to large data scales and irregular memory access patterns. Non-volatile memory (NVM) technologies provide promising solutions by offering larger capacity, low leakage power, and data persistence. In this paper, we discuss the potential of NVM technology in enhancing machine learning applications by improving energy efficiency and reducing latency through in-memory computation and different NVM write modes. The insights from this analysis provide valuable guidance to device researchers and system architects working to develop high-performance systems for machine learning and accelerators in large-scale search applications using NVMs. © 2024 Copyright is held by the owner/author(s).

关键词： computer aided instruction

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A Speech Enhancement Algorithm Combining Wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines Wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with Wavelet Transform significantly improves noise reduction, outperforming the use of Wavelet Transform alone. © 2025 ieee.

关键词： Adaptive filtering

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