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36th Information systems Education Conference, ISECON 2023

作者： Salach, Mateusz Dzierwa, Andrzej Belzo, Artur Bolanowski, Marek Paszkiewicz, Andrzej Department of Complex Systems The Faculty of Electrical and Computer Engineering Rzeszow University of Technology Al. Powstańców Warszawy 12 Rzeszów35-959 Poland Department of Manufacturing Processes and Production Engineering The Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology Al. Powstańców Warszawy 12 Rzeszów35-959 Poland

The paper presents a didactic application created in virtual reality to visualize a processes of milling and turning inside CNC machines with pre-modeled raw materials. The paper focuses on preparing and working with 3D models and configuring the VR app. The virtual reality application and classes are part of the idea of Education 4.0 – the new and future method of learning in schools. As a result of the work carried out, the possibilities of using VR tools in the process of training engineers were identified, as well as the areas that should be taken into account in the preparation of this class of solutions. The developed solution was tested in a group of students in order to assess the usefulness of the proposed solution and determine the directions for further work. © 2023 Proceedings of the Information systems Education Conference, ISECON. All rights reserved.

关键词： 3D modeling

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Enhanced Diagnosis of Broken Rotor Bars in Induction Machines Using Combined Envelope and Maximum Likelihood Estimation Approach

Enhanced Diagnosis of Broken Rotor Bars in Induction Machine...

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Advances in electrical and Communication Technologies (ICAECOT), International Conference on

作者： Hamid Khelfi Salim Hamouda Samir Hamdani Kamel K Baghdouche Zineb Ammari Abdelkader Attab Department of Electrical Engineering (DGE) Electrical and Industrial Systems Laboratory (EISL). Faculty of Electrical Engineering (FGE) University of Science and Technology Houari Boumediene (USTHB) El Alia BP. 32 Bab Ezzouar Algiers Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

This paper presents an improved approach for diagnosing broken rotor bars in induction machines by combining peak detection envelopes with the maximum likelihood estimation approach (MLEA). The proposed method releases the fault signature by transforming three current envelopes into two in-phase and quadrature components using MLEA. The modulus of these components (MMLEA) serves as the fault signature. The classical Fast Fourier Transform (FFT) subsequently analyses this signature in the frequency domain. The approach is theoretically demonstrated and experimentally confirmed by testing three induction motors under various operational circumstances, including low load. The experimental results are compared with classical technique such as motor current signature analysis (MCSA), MLEA, and combining the envelope with MLEA extracted by peak detection and Hilbert transform (HT). These comparisons demonstrate the robustness and effectiveness of the proposed approach for diagnosing broken rotor bars.

关键词： Maximum likelihood estimation Maximum likelihood detection Accuracy Fast Fourier transforms Rotors Transforms Robustness Frequency estimation Bars Testing

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Parameter fault estimation in distributed heating/cooling systems 7

Parameter fault estimation in distributed heating/cooling sy...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Kurniawan, Wijaya Hangos, Katalin M. Marton, Lorinc Faculty of Computer Science University of Brawijaya Malang Indonesia Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Institute for Computer Science and Control Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Targu Mures Romania

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon. © 2022 ACM.

关键词： Parameter estimation

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IPMSM Drive System for Electric Vehicle Use FOC with MTPA and FWC Technique

IPMSM Drive System for Electric Vehicle Use FOC with MTPA an...

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International electrical engineering Congress (iEECON)

作者： Sippakorn Chotipun Natawat Srijunth Sikharin Wayhanarut Bunlung Neammanee Jirawut Benjanarasut Pratchaya Pathomchaiwat Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical Engineering Technology College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Electric vehicle motors must exhibit high efficiency, high power density, high torque, and minimal use of magnetic materials, making the interior permanent magnet synchronous motor (IPMSM) an optimal choice. However, achieving maximum torque across the entire operational range is a challenging task. This paper presents a control strategy for IPMSM using field-oriented control (FOC). For operations below the rated speed, the maximum torque per ampere (MTPA) method is applied, while for speeds exceeding the rated level, field weakening control is employed. Moreover, rotor position measurement is enhanced by integrating an adaptive gain mechanism with a complementary filter, effectively mitigating the effects of external disturbances. This results in a system with low steady-state error. The system also incorporates CAN bus communication to facilitate seamless interfacing. The proposed controller was tested on a 13 kW IPMSM. The test results reveal that the control system achieves excellent dynamic response, accurately tracks reference speeds, and maintains minimal steady-state error.

关键词： Power system measurements Torque Power demand Torque control Rotors Position measurement Permanent magnet motors Electric vehicles Steady-state Vehicle dynamics

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IPMSM Drive System for Electric Vehicle Use FOC with MTPA and FWC Technique

IPMSM Drive System for Electric Vehicle Use FOC with MTPA an...

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13th International electrical engineering Congress, iEECON 2025

作者： Chotipun, Sippakorn Srijunth, Natawat Wayhanarut, Sikharin Neammanee, Bunlung Benjanarasut, Jirawut Pathomchaiwat, Pratchaya King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok College of Industrial Technology Department of Electrical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： Permanent magnets

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An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces

An Accurate and Hardware-Efficient Dual Spike Detector for I...

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2022 IEEE Biomedical Circuits and systems Conference, BioCAS 2022

作者： Guo, Xiaorang Shaeri, MohammadAli Shoaran, Mahsa Institute of Electrical and Micro Engineering Center for Neuroprosthetics Epfl Geneva1202 Switzerland Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden01069 Germany Chair of Computer Architecture and Parallel Systems Technische Universität München Garching85748 Germany

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Spike detection plays a central role in neural data processing and brain-machine interfaces (BMIs). A challenge for future-generation implantable BMIs is to build a spike detector that features both low hardware cost and high performance. In this work, we propose a novel hardware-efficient and high-performance spike detector for implantable BMIs. The proposed design is based on a dual-detector architecture with adaptive threshold estimation. The dual-detector comprises two separate TEO-based detectors that distinguish a spike occurrence based on its discriminating features in both high and low noise scenarios. We evaluated the proposed spike detection algorithm on the Wave Clus dataset. It achieved an average detection accuracy of 98.9%, and over 95% in high-noise scenarios, ensuring the reliability of our method. When realized in hardware with a sampling rate of 16kHz and 7-bits resolution, the detection accuracy is 97.4%. Designed in 65nm TSMC process, a 256-channel detector based on this architecture occupies only 682μm2/Channel and consumes 0.07μW/Channel, improving over the state-of-the-art spike detectors by 39.7% in power consumption and 78.8% in area, while maintaining a high accuracy. © 2022 IEEE.

关键词： Data handling

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Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling (G-S-M) approach

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computers in Biology and Medicine 2025年 187卷 109813-109813页

作者： Bakir-Gungor, Burcu Temiz, Mustafa Canakcimaksutoglu, Beyza Yousef, Malik Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Electrical and Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Zefat Academic College Israel

Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier Ltd

关键词： Colorectal cancer Human gut microbiome Machine learning Taxonomic biomarkers MicroBiomeGSM

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Federated Learning for Lidar Super Resolution on Automotive Scenes

Federated Learning for Lidar Super Resolution on Automotive ...

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International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Gerasimos Arvanitis Aris S. Lalos Konstantinos Moustakas Industrial Systems Institute Athena Research Center Patras Greece Electrical and Computer Engineering University of Patras Greece

In this paper, the problem of lidar super-resolution is explored under a federated learning perspective. The high cost of high-resolution lidar sensors is a major obstacle to the widespread adoption of connected and autonomous vehicles (CAVs). To reduce the cost, this study investigates the use of low-cost sensors in conjunction with super-resolution algorithms. Unlike previous studies that approach this problem with centralized solutions, this work employs federated learning to leverage private lidar data from different autonomous vehicles under different environmental conditions, resulting in more robust and diverse deep learning model. Extensive experiments on a real-world lidar odometry dataset highlight the merits and applicability of the proposed lidar super-resolution method with federated learning.

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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International Conference on Intelligent Transportation

作者： Zhengming Zhang Zhengming Ding Yaobin Chen Stanley Chien Lingxi Li Rini Sheroy Joshua Domeyer Renran Tian School of Industrial Engineering Purdue University USA Department of Computer Science Tulane University USA School of Electrical and Computer Engineering Purdue University USA Toyota Collaborative Safety Research Center (CSRC) USA Department of Industrial and Systems Engineering North Carolina State University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users.

关键词： Deep learning Accuracy Urban areas Predictive models Traffic control Birds Trajectory Forecasting Vehicle dynamics Micromobility

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Real-Time Testing of Microgrid Control Algorithms: Development of Experimental Setup

Real-Time Testing of Microgrid Control Algorithms: Developme...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Miodrag Forcan Goran Dobrić Amir Abed Mileta Žarković Marko Ikić Jovana Forcan Manuel Barragán-Villarejo José Maria Maza-Ortega Francisco de Paula García-López Faculty of Electrical Engineering University of East Sarajevo East Sarajevo Bosnia and Herzegovina School of Electrical Engineering University of Belgrade Belgrade Serbia Department of Computer Science and Systems Faculty of Philosophy University of East Sarajevo East Sarajevo Bosnia and Herzegovina Department of Electrical Engineering University of Sevilla Seville Spain

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided.

关键词： Battery energy storage system Power amplifiers Focusing Microgrids Control systems Real-time systems Information technology Testing

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