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Periodica polytechnica electrical engineering and computer science 2024年第3期68卷 309-318页

作者： Szénási, Sára Harmati, István Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok körútja 2 Budapest1117 Hungary

Nowadays there is a more and more common need for one-off data collection in a specified area. For example, in case of searching for the survivors of disasters or wars, or for skiers who get into trouble. The simplest way of collecting such data is using wireless sensor network (WSN). However, data transmission within the network uses great amount of energy that may significantly reduce the lifetime and operation time of the battery-operated sensors. For this reason, the data transfer between the given sensor nodes is carried out by robots. In order to minimize the latency of data collection, it is possible to use cooperating robots. It frequently occurs that one of the nodes requires more urgent visit than the other ones, for example, the serious injuries require urgent medical attendance. The sensor nodes can also be moved to collect more usable data in a larger field. To solve this, path planning for data collection from priority and moving nodes are also presented here. The algorithm developed here to perform the one-off data collection operates so that the agents start from preliminary determined or random points and then visit all the sensor nodes, download the data from these nodes and after this arrive at a designated point where they upload the collected information. The goal here is the minimization of cost that include not only the walk-through time but also the consequences of the late visit of the higher priority nodes. The optimization is solved using the ant colony optimization. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Motion planning

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Peak Current Mode Control for Grid-Connected Energy Storage Inverters

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2024年第3期22卷 132-136页

作者： Stranyóczky, László Futó, András Veréb, Szabolcs Varjasi, István Sütő, Zoltán Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary

Nowadays, three-phase inverters are playing an increasingly important role in various applications, such as drives, solar systems, energy storage systems. The commonly used PI current control method in the d-q coordinate system satisfies the requirements of most of the cases. However, in some specific applications, such as a grid-connected inverter for an energy storage system, the PI controller response time may not be sufficient to meet the criteria. This paper describes the application of the PCMC (Peak Current Mode Control) control technique, which is commonly used for DC-DC converters, and applies it to the case of three-phase two-level inverters. Advantages and disadvantages of PCMC compared to a conventional PI control solution are presented, and the performance of the two control techniques is compared for a grid connected energy storage inverter. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy storage

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Highly efficient ternary organic solar cells with excellent open-circuit voltage and fill factor via precisely tuning molecular stacking and morphology

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Science China Chemistry 2024年第3期67卷 963-972页

作者： Dinghong Sun Zhenyu Chen Jianfeng Zhang Wei Song Jingyu Shi Jintao Zhu Yuanyuan Meng Fei Jin Shuncheng Yang Ziyi Ge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Faculty of Material Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049China Department of Chemical and Environmental Engineering University of Nottingham NingboNingbo 315100China

The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.

关键词： alkylbenzene side-chain morphology regulation ternary blends organic solar cells

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Sensitivity Analysis of Finite Control Set Model Predictive Direct Speed Control of PMSM 15

Sensitivity Analysis of Finite Control Set Model Predictive ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Pancurak, Lukas Marcin, Daniel Kyslan, Karol Technical University of Košice Faculty of Electrical Engineering and Informatics Dept. of Electrical Engineering and Mechatronics Košice Slovakia

ISBN: (纸本)9798350372359

Finite control set model predictive control (FCS-MPC) stands out as a highly promising and extensively researched control approach due to its exceptional dynamic performance and the versatility it offers for defining control goals. This paper presents a systematic analysis of parameter mismatch and its impact on the control performance of finite control set model predictive direct speed control (FCS-MPDSC) for permanent magnet synchronous motor (PMSM). Variations in resistance, inductances, and inertia are studied, and root mean square error (RMSE) metrics are employed to quantify the effects of parameter mismatch on the phase current and angular speed. Results analyze the control performance and provide insights into addressing parameter mismatch challenges in FCS-MPDSC. The predictive control algorithm is designed in dq coordinate system and a linear Kalman filter (LKF) is utilized for the estimation of load torque. © 2024 IEEE.

关键词： Model predictive control

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Evaluating Time Series Approaches for Cellular Network Traffic Forecasting 18

Evaluating Time Series Approaches for Cellular Network Traff...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Inggrianti, Ibni Yuliana, Hajiar Hendrawan Iskandar School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Universitas Jenderal Achmad Yani School of Electrical Engineering and Informatics Institut Teknologi Bandung Faculty of Engineering Department of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798331527686

This paper examines the predictive accuracy of four distinct models-ARIMA, ARMA, AR, and Linear Regression-applied to network traffic forecasting. Through a comprehensive analysis, the ARIMA model was identified as the most effective for non-stationary data, demonstrating its robustness in capturing complex temporal patterns. The linear regression model, however, outperformed the others across the dataset, with the lowest Mean Squared Error (MSE) of 1.223 and Root Mean Squared Error (RMSE) of 1.106, indicating minimal prediction errors. The results underscore the importance of selecting appropriate models based on data characteristics and prediction requirements. Furthermore, the study highlights the potential of linear regression, a relatively simple method, to outperform more complex time series models under specific conditions. © 2024 IEEE.

关键词： Prediction models

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On Improved Z-R Realtion Derived from Czech Distrometer Data

On Improved Z-R Realtion Derived from Czech Distrometer Data

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Kovalchuk, Maria Zikesova, Aneta Albakawe, Ahmad Fiser, Ondrej University of Pardubice Faculty of Electrical Engineering and Informatics Department of Electrical Engineering Pardubice Czech Republic

ISBN: (纸本)9798350312843

The contribution deals with the analysis of DSD (Drop Size Distribution) data measured by the videodistrometer, which is operated by the Institute of the Atmospheric Physics of Czech Academy in Prague. The radar reflectivity factor and rain intensity are precisely calculated from the measured DSDs through pertinent formulas. Using the regression method, we then looked for a power law relation between these quantities, which would better suit the Czech climate conditions than the more general published relationships. After finding a relationship for all DSDs analysed one-minute DSDs, we then classified the rains according to the rain intensity (above and below 5 mm/h). We further categorized rains as the stratiform and convective types. For this, we used 3 criteria that are based on DSD parameters of analytical approximations. We have come to the conclusion that if we know more about the rain (whether it is convective or stratiform), we can determine the approximate relationship between the radar reflectivity factor and the intensity of rain more accurately. After 3 criteria we used, rain categorization appears to be the most accurate according to J. Testud's criterion. © 2023 IEEE.

关键词： Rain

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Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

Triple Parallel LSTM Networks for Classifying the Gait Disor...

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2023 International Conference on electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shayestegan, Mohsen Zalabsky, Tomas Mareš, Jan University of Pardubice Pardubice Faculty of Electrical Engineering and Informatics Czech Republic Research Center University of Pardubice Pardubice Faculty of Electrical Engineering and Informatics Czech Republic University of Pardubice Pardubice Faculty of Electrical Engineering and Informatics Department of Process Control Czech Republic

ISBN: (纸本)9798350322972

This paper presents a new methodology for the data processing and classification method for gait disorders, which is observed with a Kinect camera. The study of gait and motion stability in gait disorders is one of the most interesting research areas in the field. The patient and the physician must monitor the progress of the rehabilitation process before and after surgery to obtain an objective view of the rehabilitation process. In this study, the patient is scanned with the Kinect camera placed on a mobile robotic platform. For feature extraction and feature analysis, the exercises (three walking exercises) frames are collected and saved in data folders. This study uses 84 measurements of 37 patients with complex observations based on the physician's opinion in a clinical setting to address classification problems. In the analysis of gait disorders, motion data play an essential role. Furthermore, it reduces the selection of helpful body features for assessing gait disorders. The proposed system uses a key-point detector that computes body landmarks and classifies gait disorders using triple-parallel long short-term memory (LSTM) networks. The present study demonstrates the success of the method in classification evaluation when combined with the state-of-the-art pose estimation method. Around 81 percent accuracy was achieved for given sets of individuals using velocity-based, angle-based, and position-based features. © 2023 IEEE.

关键词： Long short-term memory

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Effect of the Bending Radius on the Breakdown Strength of Rectangular PAI/PEEK Insulated Winding Wire of Electric Motors 5

Effect of the Bending Radius on the Breakdown Strength of Re...

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5th IEEE International Conference on Dielectrics, ICD 2024

作者： Tamus, Zoltán Ádám Cselkó, Richárd Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electric Power Engineering Budapest Hungary

ISBN: (纸本)9798350308976

The high efficiency and mass production of electric motors for e-mobility solutions raised the hairpin technology in motor manufacturing. The reliability of these machines depends on the insulation quality of the windings, which can be investigated by the breakdown test of winding wires according to existing standards. In this study, two groups of U-shaped samples prepared from rectangular wire covered with double-layer polyamide-imide (PAI) and polyetheretherketone (PEEK) insulation were prepared with 25 mm and 50 mm bending diameters. The samples were tested by 50 Hz A.C. progressive stress. The results show that the breakdown voltage of the sample group with a smaller bending diameter is significantly lower than that of samples with a greater bending diameter. The result of the study shows that the breakdown voltage of rectangular cross-section conductors with layered insulation is very sensitive to the bending radius. This affects the long-term reliability of rotating machines wounded with hairpin technology. © 2024 IEEE.

关键词： Winding

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MANAGEMENT OF MANAGERIAL COMPETENCY 32

MANAGEMENT OF MANAGERIAL COMPETENCY

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32nd Interdisciplinary Information Management Talks: Changes to ICT, Management, and Business Processes through AI, IDIMT 2024

作者： Holá, Jana Čegan, Lukáš University of Pardubice Faculty of Health Studies Czech Republic University of Pardubice Faculty of electrical engineering and informatics Czech Republic

ISBN: (纸本)9783991515272

Competency Manager is a software application designed to manage key competencies in nursing, providing systemic support to manage managerial competencies, which are also crucial for nursing but are currently undervalued. This application was developed within a project of applied research for a specific client, a health care facility. The architecture and significance of the software application have already been published. This article focusses on creating a model for managing managerial competencies, which, unlike other competency groups, were not subject to legislative norms, but aimed to meet the client's needs. The article outlines theoretical foundations and evidence-based methods (stakeholder engagement strategy, evidence-based implementation with change agents, discussion, brainstorming, and Delphi method for review within expert panel) leading to the selection, description, and implementation of 9 core managerial competencies within the created model. © IDIMT *** rights reserved.

关键词： Competency Management managerial competencies Nursing

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