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arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments

Numerical approach to design of low frequency magnetic field...

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ELEKTRO

作者： Roman Radil Marek Bajtoš Lucia Č arnecká Klaudia Hargaš ová Patrik Kamencay Department of Electromagnetic and Biomedical Engineering University of Zilina Zilina Slovakia Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dept. of Electromagnetic and Biomedical Engineering Organization University of Zilina Zilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments.

关键词： Radiation effects Numerical models Magnetic fields Electromagnetics

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Multi-method analysis of emotional and affective states

Multi-method analysis of emotional and affective states

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Progress in Applied electrical engineering (PAEE)

作者： Adrian Luckiewicz Bogdan Ruszczak Mariusz Pelc Dariusz Mikołajewski Adam Sudoł Barbara Grochowicz Edward J. Gorzelanczyk Patryk Mendon Aleksandra Kawala-Sterniu Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

关键词： electrical engineering Brain Data processing Hemodynamics Functional near-infrared spectroscopy

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

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Progress in Applied electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： electrical engineering Filters Biological system modeling Biomedical communication

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A Topology for Five-Level Active Neutral Point-Clamped Inverter

SSRN

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SSRN 2023年

作者： Obe, Emeka Simon Mtengi, Bokani Amuhaya, Lilian Livutse Obe, Pauline Ijeoma Ugwuishiwu, Helen Chikodili Odeh, Charles Ikechukwu Department of Electrical Engineering University of Nigeria Nsukka Nigeria Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and Technology Palapye Botswana Department of Industrial Technical Education University of Nigeria Nsukka Nigeria Department of Computer Science University of Nigeria Nsukka Nigeria Department of Electric Drives and Energy Conversions Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland

A combination of two shared input splitting capacitor banks and varying number of individualized inverter-leg flying-capacitors characterized topological feature of a large group of 5L ANPC inverters configurations. The inconveniences in the provision of these per phase flying-capacitors make these configurations less attractive in most applications. The 5L E-type inverter, characterized by four shared input capacitor banks, becomes a viable alternative topology. However, the high blocking-voltage rating of the constituent switches still places restrictions on the use of the 5L E-type inverter within this voltage range. In view of this, this paper presents a reconfiguration of the 5L E-type inverter. To lessen the voltage stress on the switching devices used here, the common node of the E-type inverter’s bidirectional switch is investigated. Inverter costs and losses are influenced by the decreased voltage stress. The carrier-overlapped pulse-width modulation, or COPWM, perspective serves as the foundation for the inverter control technique. Simulated results of the inverter performances are presented together with an analysis of the cost competitiveness of the proposed inverter power circuit. A 17.68% reduction in loss dissipation and a 13.61% cost savings were recorded by using the proposed power circuit. © 2023, The Authors. All rights reserved.

关键词： Pulse width modulation

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment

Integration of ANN for Accurate Estimation and Control in Wa...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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Optimal Power control Based-Metaheuristic Algorithm Based Variable Speed Wind Energy System of DFIG

Optimal Power Control Based-Metaheuristic Algorithm Based Va...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Boussaid Ibrahim Harrouz Abdelkader Merahi Farid Ibrahim Yaichi Ilhami Colak Korhan Kayisli Virgil Dumbrava Electrical Engineering Department Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Electrical Engineering Department Automatic Laboratory of Setif University of Ferhat Abbas Setif1 Setif Algeria Department of Electrical Electronics Eng. Faculty of Engineering Architecture Nisantasi University Istanbul Turkey Department of Electric Power Systems Polytechnic University of Bucharest Bucharest Romania

This article presents a novel method for controlling a doubly fed induction generator (DFIG) wind energy system in the presence of variable wind speeds. In order to achieve decoupling control of the active and reactive power, the rotor-side converter (RSC) of the DFIG is controlled using field-oriented control (FOC). The system is very complicated and nonlinear, making it challenging to adjust the PI gains appropriately. The present research proposes the utilization of the Grey Wolf Optimization (GWO) algorithm as an approach to optimize the gains of a Proportional-Integral (PI) regulator. The simulation results demonstrate that the GWO-PI provides a lower criterion value and an improved response than the conventional PI.

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Customizable Inverse Sensor Model for Bayesian and Dempster-Shafer Occupancy Grid Frameworks 20th

Customizable Inverse Sensor Model for Bayesian and Dempster-...

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20th Polish control Conference, PCC 2020

作者： Porębski, Jakub Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Aptiv Services Poland S.A. Kraków Poland

ISBN: (纸本)9783030509354

Occupancy grid mapping is an important component in a road scene understanding for autonomous driving. It can encapsulate data from heterogeneous sensor sources like radars, LiDARs, cameras and ultrasonics. At the core of occupancy grid (OG) generation, there is usually an inverse sensor model (ISM), which infers the occupancy representation from the sensor readings. Traditional ISMs are characterized by a very rigid structure, suited only for one sensor type, and specific occupancy grid representation. This paper proposes a novel ISM framework, which offers a separation between free and occupied space, supporting both Bayesian and Dempster-Shafer OG representations. The framework is especially useful when dealing with multiple different sensors where custom or preselected probability distribution can be applied. The presented ISM architecture is modular and flexible, which is described in an illustrative example of application customized for different detection sources. © 2020, Springer Nature Switzerland AG.

关键词： Probability distributions

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Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Zhang, Han Lin, Shengxiang Zhang, Xingyi Wang, Yu Zhang, Yangguang College of Aritificial Intelligence and Automation Hohai University Nanjing211100 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Control and Computer Engineering North China Electric Power University Beijing071003 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction. Recent advancements involve using deep learning to process calorimeter images from various sub-detectors in experiments like the Large Hadron Collider (LHC) for energy map reconstruction. This paper compares classical algorithms—MLP, CNN, U-Net, and RNN—with variants that include self-attention and 3D convolution modules to evaluate their effectiveness in reconstructing the initial energy distribution. Additionally, a test dataset of jet events is utilized to analyze and compare models’ performance in handling anomalous high-energy events. The analysis highlights the effectiveness of deep learning techniques for energy image reconstruction and explores their potential in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS IN SANITARY INSTALLATIONS 21

METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS ...

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21st International Multidisciplinary Scientific Geoconference: Water Resources, Forest, Marine and Ocean Ecosystems, SGEM 202

作者： Czajkowski, Adrian Remiorz, Leszek Pawlak, Sebastian Remiorz, Eryk Szygula, Jakub Miscea.pl Engineering Sp. z o.o. Poland Department of Power Engineering and Turbomachinery Faculty of Energy and Environmental Engineering Silesian University of Technology Poland Doctoral School The Silesian University of Technology Poland Faculty of Mechanical Engineering Scientific and Didactic Laboratory of Nanotechnology and Materials Technologies Poland Department of Mining Mechanization and Robotisation Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland Department of Distributed Systems and Informatic Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9786197603361

The article presents the issue of scale deposits in sanitary installations. Scale contributes to improper operation of sanitary devices and leads to their degradation. Scale build-up in pipes and measuring elements results in higher water consumption due to inaccurate estimation of this resource consumption. The consequence is that devices based on process automation do not work properly due to disturbances in the actual water flow. The formation of deposits also leads to economic and ecological losses, as sanitary installations elements have to be replaced, which affects the increased use of raw materials and the generation of waste. This is not in line with current trends and global objectives related to circular economy. Scale also results in increased energy consumption and reduced energy efficiency. This is due to the resistance of the flows and the increased work of the pumps. Scale has insulating properties, which results in less efficient heat transfer. In this stady, a mathematical model was developed to detect the formation of scale deposits in sanitary installations. On this basis, it is possible to control the quality status of sanitary installation components. In this way, by taking appropriate action at an early stage of the formation of deposits, it can extend the life of the installation. Having information on scale deposits, it will help avoid wasting water and lead to more accurate measurement of water flows. This is important for the proper operation of devices such as heat pumps, where their operation is automated and requires precise measurements of the energy processes taking place. Scale is a significant problem for households, and also for factories or public institutions that are exposed to hard water environments. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： Deposits

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