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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Nguyen, Quang Thanh Nguyen, Trung Van Tran, Binh Ngoc Nguyen, Dung Quang Vo, Hau Huu Phan, Liem Dung Brandstetter, Pavel Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Facility Management Ton Duc Thang University Ho Chi Minh City Viet Nam Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam R&P Electronics Company Limited Ho Chi Minh City Viet Nam Faculty of Electrical Engineering and Computer Science VSB–Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9789819987023

The paper presents integration of fuzzy logic into adaptation mechanism of Luenberger observer for sensorless pulse width modulation-direct torque controlled (PWM-DTC) induction motor (IM) drive. At first, DTC strategy and PWM technique are described. Next, basic mathematical model for state representation, adaptive signal and proportional-integral (PI) adaptation mechanism of Luenberger observer is shown. However, the PI adaptation mechanism cannot ensure desired motor speed responses in both dynamic and steady-state operations of IM drive. Fuzzy logic control theory is added to overcome the disadvantage of PI adaptation mechanism via update proportional gain and integral constant time of PI controller. Simulations of sensorless control for two adaptation mechanisms are carried out at different reference speeds. Speed deviations are utilized to evaluate the performance of two adaptation mechanisms at both dynamic and steady-state operations. Theoretical assumptions of high performance of fuzzy logic are validated by assessments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy logic

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Power factor correction of MV microgrids by adjusting LV photovoltaic inverters

Power factor correction of MV microgrids by adjusting LV pho...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Dullius, Darlene J. De Souza, Andre L. S. Tonkoski, Reinaldo Vargas, Renzo Melo, Joel D. Engineering Modeling and Applied Social Sciences Santo André Brazil Department of Electrical Engineering and Computer Science South Dakota United States

ISBN: (纸本)9781665444217

In recent years, utilities require microgrids connected to the medium voltage distribution network to correct power factor at the point of common coupling (PCC). This paper proposes an approach based on the golden search method and the Fibonacci search method to define the photovoltaic inverters' power factor at the low voltage side of the network to achieve adequate power factor at the microgrid PCC (medium voltage). The performance of the proposed approach is evaluated using a simulation benchmark implemented using the OpenDSS COM interface in Matlab. This simulation is based on a real microgrid located at the Federal University of ABC in Santo André, Brazil, that includes nine photovoltaic systems with a total capacity of 473 kVA. The results showed that utilizing the proposed approach in microgrids can avoid operating with low power factors. © 2021 IEEE.

关键词： Electric inverters

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Composability Verification of Complex Systems Using Colored Petri Nets

Composability Verification of Complex Systems Using Colored ...

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2021 Winter Simulation Conference, WSC 2021

作者： Mahmood, Imran Askari, Syed Hassan Sarjoughian, Hessam S. Center for Research in Modeling Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9781665433112

The discipline of component-based modeling and simulation offers promising gains including reductions in development cost, time, and system complexity. This paradigm promotes the use and reuse of modular components for adequate development of complex simulations. Achieving effective and meaningful model reuse through the composition of components still remains a daunting challenge. 'Composability', an integral part of this challenge, is the capability to select and assemble model components in various combinations to satisfy specific user requirements. In this paper, we propose the use of Colored Petri Nets for component-oriented model development, model composition, and the verification of composed models using state-space analysis technique. We present a case study of an elevator model as a proof of concept. Our case study explains the proposed process of developing and composing CPN-based model components and verifying the composed model using state-space analysis. © 2021 IEEE.

关键词： Petri nets

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Giuseppe D'Aniello Roberto Falcone Matteo Gaeta Zia Ur Rehman Giancarlo Fortino Dept. Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano SA Italy Dept. Computer Engineering Modeling Electronics and Systems University of Calabria Rende Cs Italy

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset.

关键词： Accuracy Correlation Decision making Hidden Markov models Medical services Predictive models Time factors Wearable devices Long short term memory Cybernetics

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Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis

arXiv

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arXiv 2024年

作者： Lanzino, Romeo Fontana, Federico Cinque, Luigi Scarcello, Francesco Maki, Atsuto Sapienza University of Rome Computer Science Department Rome00198 Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende87030 Italy KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper introduces the Neural Transcoding Vision Transformer (NT-ViT), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of NT-ViT is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model’s accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, NT-ViT addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets;NT-ViT outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a 10× reduction in RMSE and a 3.14× increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model’s overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at https://***/rom42pla/ntvit. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Text-independent Speaker Recognition: A Deep Learning Approach

Text-independent Speaker Recognition: A Deep Learning Approa...

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

作者： Arman Shirzad Ali Nasiri Razieh Darshi Zohreh Safarpour Razieh Abdollahipour Dept. of Computer Engineering University of Science and Culture Tehran Iran Dept. of Electrical and Computer Engineering University of Tabriz Tabriz Iran Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran Dept. of Electrical Engineering Berlin University of Applied Sciences Berlin Germany Dept. of Sensors and Modeling Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this study, we introduce an artificial neural network (ANN) model specifically designed for speaker recognition and compare its performance and accuracy with that of the established Keras speaker recognition model. Our investigation utilizes two distinct datasets: the first comprises voice recordings in three different languages from both male and female speakers; the second is the widely recognized LibriSpeech dataset, a comprehensive corpus of English speech derived from 50 individuals reading audiobooks. The proposed model leverages the Fast Fourier Transform (FFT) to analyze the input data, which consists of labeled .wav files. These files undergo a preprocessing stage before being input into the ANN for training. Our results indicate that while the proposed model outperforms the Keras model on the first dataset, it exhibits marginally lower performance on the LibriSpeech dataset. This finding suggests that even well-established models like those provided by Keras may not universally deliver optimal results across different datasets.

关键词： Training Analytical models Accuracy Fast Fourier transforms Computational modeling Speech recognition Artificial neural networks Data models Speaker recognition Recording

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Enhancing Sentiment Analysis with Attention Mechanism and Bayesian-Lipschitz Recurrent Neural Networks

Enhancing Sentiment Analysis with Attention Mechanism and Ba...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Mahmudul Hasan Sachin Shetty Peter B. Foytik Department of Electrical and Computer Engineering Old Dominion University Norfolk Virginia USA Virginia Modeling Analysis and Simulation Center (VMASC) Old Dominion University Suffolk Virginia USA

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

In medical chatbots and other healthcare domains, government agencies and other essential industries use sentiment analysis for public safety, improving the service quality of products, and other necessary purposes. Sometimes, the data is noisy, ambiguous, and contains mixed sentiments, where different parts of the text may convey different emotions, creating a problem in adequately detecting the sentiment. We proposed a novel recurrent neural network called the Bayesian-Lipschitz RNN with an attention mechanism to address these challenges. The Bayesian approach manages noisy and ambiguous data by representing uncertainty and avoiding overfitting. At the same time, the attention mechanism focuses on the most relevant parts of the text, effectively capturing context and mixed sentiments. Then, the Lipschitz Recurrent Neural Networks (LRNN), based on hidden-to-hidden matrices, are employed and constructed using a symmetric-skew decomposition scheme. The hidden-to-hidden matrices are used to update the hidden state and this hidden state is used to predict the sentiment, mitigate the vanishing gradient and stability issues, and got accuracy with 92.45 %.

关键词： Sentiment analysis Recurrent neural networks Attention mechanisms Symmetric matrices Uncertainty Stability analysis Public security Bayes methods Noise measurement Matrix decomposition

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Intelectual Methods for Analysis of Renewables in the Energy Balance for Low Emission Strategy

Intelectual Methods for Analysis of Renewables in the Energy...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Ruslan Omirgaliyev Svetlana Zhakiyeva Korhan Kayisli Ideyat Bapiyev Zhanserik Nurlan Aidos Satan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Mathematical and Computer Modeling Eurasian National University Astana Kazakhstan Department of Electrical-Electronics Engineering Gazi University Ankara Turkiye Department of Science and Innovations Astana IT University Astana Kazakhstan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The growing global focus on mitigating climate change has emphasized the need for efficient strategies that integrate renewable energy sources (RES) while minimizing emissions. This paper explores the application of various intellectual methods-Energy System Models (e.g., TIMES, LEAP), Multi-Objective Optimization, Computable General Equilibrium (CGE) Models, Life Cycle Assessment (LCA), and Machine Learning (ML)-for analysing energy balances and developing low-emission strategies. These methods offer distinct capabilities in balancing technological, economic, and environmental aspects of energy systems. The paper examines how these approaches facilitate the integration of RES, address operational challenges, and handle uncertainties in energy demand and supply. Case studies demonstrate how AI and ML techniques optimize energy dispatch and demand forecasting, while system models provide long-term planning solutions. A comprehensive comparison of these methods is presented, highlighting their strengths and limitations in transitioning towards sustainable and low-emission energy systems.

关键词： Climate change Energy management Life cycle assessment Machine learning Carbon emissions Low carbon economy Renewable energy sources

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Wearable Sensor Gait Analysis of Fall Detection using Attention Network

Wearable Sensor Gait Analysis of Fall Detection using Attent...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Yhdego, Haben Li, Jiang Paolini, Christopher Audette, Michel Old Dominion University Computational Modeling and Simulation Engineering Norfolk United States Old Dominion University Electrical and Computer Engineering Norfolk United States San Diego State University Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9781665401265

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding-founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models. © 2021 IEEE.

关键词： Fall detection

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of Complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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2021 International Conference on Applied Mathematics, modeling and computer Simulation, AMMCS 2021

作者： Zhu, Huawei Mao, Yihao Zhao, Yiming Shi, Liyuan Shao, Yanli School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781643682549

With the development of urbanization, the number of residents' motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation ***, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction. © 2022 The authors and IOS Press.

关键词： Traffic congestion

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