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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Marcus, Kyrollos S. Abdelsalam, Ibrahim Marei, Mostafa I. Energy & Renewable Energy Engineering Dep. Faculty of Engineering & Technology Egyptian Chinese University Cairo Egypt Electrical and Control Engineering Dep. College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9798331531836

This paper proposes a grid-tied electric vehicle (EV) charging station architecture with integrated renewable energy sources and battery storage to optimize energy utilization and minimize grid reliance. The proposed charging station has a power management strategy prioritizes renewable energy and battery power to meet EV charging demands, reducing dependence on the grid and minimizing energy costs. A hybrid maximum power point tracking (MPPT) algorithm ensures optimal energy harvesting from the photovoltaic (PV) array under varying solar irradiance conditions. The system's dynamic control strategy, coupled with battery storage, effectively mitigates the impact of PV variability, and maintains stable DC bus voltage, ensuring reliable EV charging services. Simulation results validate the effectiveness in optimizing energy utilization, reducing grid reliance, and maintaining stable operation, contributing valuable insights to the development of sustainable EV charging infrastructure. © 2025 IEEE.

关键词： Energy utilization

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A Secure and Transparent Voting System Framework Using Finger Vein and Blockchain Technology

A Secure and Transparent Voting System Framework Using Finge...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Chittapuli Bhupathi Padal Valli Kumari Vatsavayi Department of computer science and systems engieering Andhra university college of engineering Visakhapatnam India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

The traditional voting system faces widespread mistrust due to its vulnerability to exploitation, lack of transparency, and limited reliability, leading to concerns about the security of democratic rights. Attempts to digitize voting have also faced challenges, with current digital systems often failing to ensure the transparency and security necessary for fair elections. Both traditional and current digital voting systems are susceptible to exploitation, and their inherent weaknesses often lead to mistrust and vulnerability. This proposed solution is secure and transparent voting system framework that leverages finger vein recognition technique integrated with block chain technology to ensure the integrity and confidentiality of votes. Blockchain technology to create a more secure, transparent and reliable voting system. By utilizing blockchain, we aim to address the issues in both physical and digital voting systems, ensuring a fair election process that upholds voters' democratic rights. Blockchain technology offers a platform that maximizes transparency and reliability, fostering a trustworthy relationship between voters and election authorities. each Voter will be registered with Finger Vein as the password and this finger vein will be encrypted and secured by using Pepper-Salt algorithm. Our proposed framework allows voting activities to be conducted entirely online through blockchain, removing the need for physical polling stations. The system employs a scalable blockchain supported by flexible consensus algorithms, with a Chain Security Algorithm to enhance transaction security.

关键词： Performance evaluation Privacy Voting Fingers Smart contracts Process control Consensus algorithm Blockchains Security Reliability

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Integrating Chicken Swarm Optimization with Deep Learning for Microarray Gene Expression Classification

Integrating Chicken Swarm Optimization with Deep Learning fo...

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International Conference on Devices, Circuits and systems (ICDCS)

作者： B. Shyamala Gowri S. Anu H Nair K. P. Sanal Kumar S. Kamalakkannan Department of Computer Science and Engineering Annamalai University Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Department of CS RV Government Arts College Chengalpattu Department of IT School of Computing Sciences VISTAS

ISBN: (数字)9798350350470

ISBN: (纸本)9798350350487

Microarray Gene Expression classification is a computational model that is vital in interpreting biological data encoded in profiles of gene expression. Using microarray technology, which permits synchronized measurement of numerous genes, this classification model proposes to classify samples into different clusters dependent upon their gene expression patterns. By examining the massive amount of data produced from microarray experimentations, researchers can discover significant insights into numerous biological procedures, classify possible biomarkers for diseases, and improve understanding of cellular devices. The procedure includes preprocessing raw data, removing appropriate features, and using sophisticated classification methods to precisely allocate samples to exact pathological or phenotypic types, donating considerably to the areas of biomedical and genomics research. In this study, a Microarray Gene Expression Classification using Chicken Swarm Optimization with Deep Learning (MGEC-CSODL) model is presented, intended to optimize the accuracy and efficacy of gene expression classification. The technique combines adaptive histogram-based preprocessing to enhance data representation, uses DenseNet201 as an influential feature extraction for strong feature learning, employs Chicken Swarm Optimizer (CSO) for hyperparameter fine-tuning to improve model performance, and includes a Graph Convolutional Network (GCN) for precise classification of gene expression. The experimental outcomes establish the efficacy of the MGEC-CSODL model, showcasing important growths in classification accuracy when equated to present models. This state-of-the-art technique not only progresses the area of bioinformatics but also delivers an effective tool for precise and effectual analysis of gene expression in the era of high-throughput genomics.

关键词： Representation learning Adaptation models Accuracy Biological system modeling Genomics Data models Gene expression

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Analysis on the Hesitation and its Application to Decision Making

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 15-34页

作者： Yang, Youpeng Lee, Sanghyuk Kim, Kyeong Soo Zhang, Haolan Huang, Xiaowei Pedrycz, Witold School of Advanced Technology Xi’an Jiaotong-Liverpool University Jiangsu Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science New Uzbekistan University Tashkent Uzbekistan SCDM Centre Zhejiang University Ningbo China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

A novel score function based on the Poincaré metric is proposed and applied to a decision-making problem. Decision-making on Fuzzy Sets (FSs) has been considered due to the flexibility of the data, and it is applied to the decision-making. However, decisions with FSs are sometimes nondecisive even for different membership degrees. Hence, Intuitionistic Fuzzy Sets (IFSs) data is applied to design a score function for the decision-making with the Poincaré metric. This function is supported by the profound information of IFSs;IFSs include hesitation degree together with membership and non-membership degree. Hence, IFS membership and non-membership degree are expressed as two-dimensional vectors satisfying the Poincaré metric for simplification. At the same time, the proposed approach addresses the hesitation information in the IFS data. Next, a score function is proposed, constructed and provided. The proposed score function has a strict monotonic property and addresses the preference without resorting to the accuracy function. The strict monotonic property guarantees the preference of all attributes. Additionally, the existing problem of score function design in IFSs is addressed: they return zero scores even with different meanings for the same membership and non-membership degree. The advantages of the proposed score function over existing ones are demonstrated through illustrative examples. From the calculation results, the proposed decision score function discriminates between all candidates. Hence, the proposed research provides a solid foundation for the hesitation analysis on the decision-making problem. © 2024 The Authors.

关键词： Decision making

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Image Enhancement Using Adaptive Fractional Order Filter

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computer systems science & engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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FingFor: a Deep Learning Tool for Biometric Forensics

FingFor: a Deep Learning Tool for Biometric Forensics

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Baha Eddine Lakdher Mohamed Mejri Ridha Ghayoula Elyes Manai Marwa Ziadia Department of Computer Science and Software Engineering Laval University Quebec Canada Faculty of Science University of Monastir Monastir Tunisia Faculty of Engineering University of Moncton Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Intentionally mutilated fingerprints pose a significant challenge in forensic identification. Such deliberate actions typically stem from individuals seeking to evade detection or association with past or prospective criminal activities. The detection of damaged fingerprints presents a formidable obstacle for most of current forensic systems, often leading to a pronounced incidence of false negatives. The ramifications of a false negative are profound, as they preclude the establishment of links between suspects and crime scenes, impeding the acquisition of vital evidence and potentially stalling investigative progress. In response to this critical issue, this paper delves into the development of a deep learning based model expressly designed to accurately discern and capture patterns present in damaged fingerprints.

关键词： Deep learning Forensics Transfer learning Process control Fingerprint recognition Robustness Convolutional neural networks Information technology Research and development Optimization

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Tacformer: A Self-attention Spiking Neural Network for Tactile Object Recognition 16th

Tacformer: A Self-attention Spiking Neural Network for Tact...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Hu, Jiarui Wang, Zhipeng Lu, Ping Yuan, Philip F. Zhou, Yanmin Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China College of Architecture and Urban Planning Tongji University Shanghai201804 China

ISBN: (纸本)9789819964949

Tactile sensor plays an important role in the human-robot interaction by providing environmental information to robots. How to effectively use the haptic information of array sensors to encode and extract haptic features for object recognition is the focus of current research. Previous studies have provided methods to construct array sensors in order to acquire tactile information efficiently. New advances in event-driven array sensors could capture spatial spiking tactile data, which include rich spatial information. In this paper, we propose a Residualized Graph Self-attention Spiking Neural Network (Tacformer) for tactile object recognition. The proposed Tacformer pays attention to both the spatial properties and the temporal sensing values of each sensor point in event-driven array sensors. Experimental results on two tactile datasets show that the accuracy, precision, recall and F1-value of the method in this paper are improved. On the EvTouch-Objects dataset, Tacformer has improved the accuracy by 8.34% compared with the GNN-SNN based a baseline approach. On the EvTouch-Containers dataset, Tacformer also has increased the accuracy by 16.17%. The Tacformer could be applied to facilitate robot tasks like grasping and sorting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Object recognition

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： M Saradha T Nithesh Priyan D U Shreeram S Viknesh Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well.

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LLaMA-Based Models for Aspect-Based Sentiment Analysis 14

LLaMA-Based Models for Aspect-Based Sentiment Analysis

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned for ABSA remains unexplored. This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models. We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca 2 model surpasses state-of-the-art results in all tasks. However, all models struggle in zero-shot and few-shot scenarios compared to fully fine-tuned ones. Additionally, we conduct error analysis to identify challenges faced by fine-tuned models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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