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2023 International Symposium on Electrical and Electronics engineering, ISEE 2023

作者： Tran, Khoa Dang Nguyen Ly, Trinh Thi Hoang, Khoa Truong Phuoc, Hoa Truong Le, Nhuan An Nguyen, Hung Duc Ho Chi Minh City University of Technology VNU-HCM Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam University of Science Vietnam National University Faculty of Physics and Engineering Physics Department of Physics and Computer Science Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350315486

Renewable energy sources (RESs) have become the predominant trend in the development of energy systems in many countries. To coordinate RESs, microgrids have emerged as an efficient and optimal solution. However, the microgrid is facing the problem of inaccurate power sharing between the generators. To address the issue, this paper proposes a control technique that combines a dynamic consensus with droop control algorithms. The proposed method calculates current sharing error by utilizing current values and consensus algorithm. Then, an external current-sharing controller is adopted to regulate the droop parameter in an inner controller for improving the current sharing among RESs. By applying the proposed control method, the current sharing accuracy is achieved in the microgrid system under different load conditions. The proposed technique is analyzed and evaluated with the scale-down microgrid consisting of three distributed generators. The simulation results of the scale-down microgrid are obtained from the PLECS simulation platform. © 2023 IEEE.

关键词： controllers

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer’s Patients

Integrating Decision Trees with Cloud Computing for Predicti...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： K. Meenakshi Bathini Ravinder Mohammed Rehaman Pasha K. Thinakaran Dabbara Jayanayudu S.P. Maniraj Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Siddartha Institute of Science and Technology Puttur Andhra Pradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer’s patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand.

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Infrastructure-dependent ramp-metering control for METANET-s

Infrastructure-dependent ramp-metering control for METANET-s

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IEEE Conference on Decision and control

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies.

关键词： Simulation Merging Numerical models Vehicle dynamics Traffic congestion

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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Mediterranean Conference on control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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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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UMGAD: Unsupervised Multiplex Graph Anomaly Detection

arXiv

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arXiv 2024年

作者： Li, Xiang Qi, Jianpeng Zhao, Zhongying Zheng, Guanjie Cao, Lei Dong, Junyu Yu, Yanwei Faculty of Information Science and Engineering Ocean University of China Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science The University of Arizona Tucson United States

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely applied in various real-world scenarios, including fraud detection and social network analysis. However, existing GAD methods still face two major challenges: (1) They are often limited to detecting anomalies in single-type interaction graphs and struggle with multiple interaction types in multiplex heterogeneous graphs. (2) In unsupervised scenarios, selecting appropriate anomaly score thresholds remains a significant challenge for accurate anomaly detection. To address the above challenges, we propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD. We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs and capture anomaly information during node attribute and structure reconstruction through graph-masked autoencoder (GMAE). Then, to further extract abnormal information, we generate attribute-level and subgraph-level augmented-view graphs, respectively, and perform attribute and structure reconstruction through GMAE. Finally, we learn to optimize node attributes and structural features through contrastive learning between original-view and augmented-view graphs to improve the model’s ability to capture anomalies. Meanwhile, we propose a new anomaly score threshold selection strategy, which allows the model to be independent of ground truth information in real unsupervised scenarios. Extensive experiments on six datasets show that our UMGAD significantly outperforms state-of-the-art methods, achieving average improvements of 12.25% in AUC and 11.29% in Macro-F1 across all datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Deep learning approach for credit card fraud detection 2

Deep learning approach for credit card fraud detection

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El Naby, Aya Abd El-Din Hemdan, Ezz El-Sayed, Ayman MSA University Department of Computer Science and Engineering Faculty of Electronic Engineering Egypt Menoufia University Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia Egypt

ISBN: (纸本)9781665418423

As technology evolves rapidly, the world is using credit cards instead of cash in its everyday lives, opening up a new way for fraudulent people to abuse them. Credit card fraud losses reached approximately $28.65 billion in 2019, according to Nilsson's report, and global card fraud is expected to reach around $32.96 billion by 2023. Providers should therefore develop an efficient model to detect and prevent fraud early. In this paper, we used deep learning techniques as an effective way to detect fraudsters in credit card transactions. Therefore, we present a model for predicting legitimate transactions or fraud on Kaggle's credit card dataset. The proposed model is OSCNN (Over Sampling with Convolution Neural Network) which is based on over-sampling preprocessing and CNN (convolution neural network). The MLP (Multi-layer perceptron) was also applied to the dataset. Comparing the MLP-OSCNN results, they proved that the proposed model achieved better results with 98% accuracy. © 2021 IEEE.

关键词： Deep learning

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