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Energy Efficiency in Domestic Appliances and Lighting & International Symposium on the Science and Technology of Lighting (EEDAL/LS:17), Joint Conference - International Conference on

作者： Leoš Kukačka Pascal Dupuis Georges Zissis Technical University of Liberec Institute of Mechatronics and Computer Engineering Studentska 1402/2 Liberec Czech Republic University Toulouse III Paul Sabatier LAPLACE UMR 5213 (CNRS INPT UPS) 118 rte de Narbonne Toulouse France

ISBN: (数字)9782492754005

ISBN: (纸本)9781665431439

This paper studies an algorithm for determining the flicker severity of luminous flux produced by artificial lighting systems. The algorithm is defined in time domain and constitutes an alternative to the widely used flicker observer model (light flickermeter) known as IEC technical Report 61547-1:2015. The paper describes and discusses a few aspects related to the implementation of the algorithm. An alternative filter definition is suggested. Finally, the algorithm is compared in simulation with the standard observer model using several properly chosen tests.

关键词： Meters Observers Writing Filtering algorithms Artificial light Time-domain analysis IEC Standards

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Enhancing the Accuracy of Forecasting of the partern of load usage of the Electrical appliences via Soft Computing

Enhancing the Accuracy of Forecasting of the partern of load...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Subuddi Nagaraju C. Rohith Bhat M. Baskar Gnanajeyaraman Rajaram L. Pattathu Rani K. Vijaya Bhaskar Department of EEE Aditya Institute of Technology and Management Tekkali Andhra Pradesh India Saveetha School of Engineering Saveetha institute of Medical and Technical Sciences Saveetha University Chennai India Department of Mechatronics Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai Saveetha School of Engineering SIMATS University Chennai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

precise estimations of consuming load profiles are crucial for the successful oversight of electrical energy systems in both financial & environmental terms. Based on this foundation, it is feasible to strategize & execute the utilization of manageable power generation and storage systems, along with power acquisition, considering the necessary preparation as well as technological and contract limitations. The documented electrical load profiles will significantly rise throughout the digitization of the energy sector. To enhance prediction accuracy, it is essential to create and analyze models that consider the increasing data volume and aim to enhance predicting accuracy through an increased number of data. Artificial neural networks (ANN) are being more commonly employed to address not linear issues involving large datasets influenced by humans and various other unexpected factors. The artificial neural network (ANN) technique is selected to forecast load patterns. The study aims to simulate and evaluate the efficacy & optimal performance of prediction models using an Artificial Neural Network for power consumption profiles.

关键词： Solid modeling Accuracy Power demand Artificial neural networks Predictive models State-space methods Forecasting

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A Lightweight Approach to Real-Time Speaker Diarization: From Audio Toward Audio-Visual Data Streams

SSRN

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SSRN 2023年

作者： Kynych, Frantisek Cerva, Petr Zdansky, Jindrich Svendsen, Torbjørn Salvi, Giampiero Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentska 2 Liberec461 17 Czech Republic Department of Electronic Systems Norwegian University of Science and Technology Torgarden TrondheimNO-7491 Norway KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Brinellvägen 8 StockholmSE-100 44 Sweden

This manuscript deals with the task of real-time speaker diarization (SD) for stream-wise data processing. Therefore, in contrast to most of the existing papers, it considers not only the accuracy but also the computational demands of individual investigated methods. We first propose a new lightweight scheme allowing us to perform speaker diarization of streamed audio data. Our approach utilizes a modified residual network with squeeze-and-excitation blocks (SE-ResNet-34) to extract speaker embeddings in an optimized way using cached buffers. These embeddings are subsequently used for voice activity detection (VAD) and block-online k-means clustering with a look-ahead *** described scheme yields results similar to the reference offline system while operating solely on a CPU with a low real-time factor (RTF) below 0.1 and a constant latency of around 5.5 seconds. In the next part of the work, our research moves toward much more demanding and complex real-time processing of audio-visual data *** this purpose, we extend the above-mentioned scheme for audio data processing by adding an audio-video module. This module utilizes SyncNet combined with visual embeddings for identity tracking. Our resulting multi-modal SD framework then combines the outputs from audio and audio-video modules by using a new overlap-based fusion strategy. It yields diarization error rates that are competitive with the existing state-of-the-art offline audio-visual methods while allowing us to process various audio-video streams, e.g., from internet or TV broadcasts, in real-time and with the same latency as for audio data processing. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Tool Classification in Laparoscopic Images Using Feature Fusion Convolutional Neural Networks: A Single Label Classification Approach

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IFAC-PapersOnLine 2024年第24期58卷 391-396页

作者： H. ElMoaqet H. Qaddoura T. AlMasri T. Abdulbaki Alshirbaji N.A. Jalal K. Möller Department of Mechatronics Engineering German Jordanian University Amman 11180 Jordan Department of Biomedical Engineering German Jordanian University Amman 11180 Jordan Institute of Technical Medicine (ITeM) Furtwangen University Villingen-Schwenningen 78054 Germany Innovation Centre Computer Assisted Surgery (ICCAS) University of Leipzig Leipzig 04103 Germany

Surgical tool classification is essential for understanding surgical workflow of laparoscopic interventions. Despite many previous studies, there is still a high need to integrate recently developed technologies in deep learning to improve the performance in detecting surgical instruments in laparoscopic videos. This paper proposes a new approach for surgical tools classification using feature fusion networks and attention modules. The proposed model leverages a DenseNet-121 as a backbone CNN that incorporates feature fusion for mid-level and high-level CNN features, followed by a Squeeze-and-Excitation (SE) attention module. The proposed model was comprehensively evaluated with precision, recall and F1-score. Results show high performance of the proposed approach and demonstrate the importance of integrating feature fusion networks and attention modules for improved performance in surgical tool detection.

关键词： Surgical tool classification Feature fusion Cholecystectomy images Convolutional neural network (CNN) attention module

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Research of Control Systems for Robotic Spatial Planning Platforms 4

Research of Control Systems for Robotic Spatial Planning Pla...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Lebedenko, Yurii Polyvoda, Oksana Derkachenko, Anatolii Modlo, Yevhenii Demishonkova, Svitlana Pylypenko, Yurii Kyiv National University of Technologies and Design Information and Computer Technologies Department Kyiv Ukraine Kherson National Technical University Automation Robotics and Mechatronics Department Kherson Ukraine Technological Institute State University of Economics and Technology Kryvyi Rih Ukraine Kyiv National University of Technologies and Design Department of Computer Engineering and Electromechanics Kyiv Ukraine

ISBN: (纸本)9798350346831

The work is devoted to the study of the control system of the moving platform of the manipulative robot. The purpose of the work is the development and research of an instrumental complex of control, analysis and modeling of the operation of positioning platforms based on a spherical mechanism for use in automated industrial systems. The state of use of multi-link positioning platforms in various industries was analyzed and their classification was provided, the main areas of use were determined. A comparative analysis of mathematical methods for describing the kinematic properties of positioning platforms was performed, namely: the closed vector contour method, the coordinate transformation method. A model with an analytical apparatus for controlling the drive links of the spherical mechanism according to the given scenarios of the orientation of the positioning platform was developed, as well as a mathematical model of inverse kinematics was made, the working space was modeled, and the service angles were determined, and a calculation and real-life experiment was conducted. For the first time, the work uses the dependencies of analytical geometry to implement algorithms for controlling a spherical mechanism by inverse kinematics, and offers software and hardware that combines visual modeling tools with real-time data transmission to a controller that controls the drivers of a spherical mechanism by direct kinematics. The result of the field experiment is the testing of the proposed control algorithm on the manufactured manipulator mechanism on the most common movements of the platform of the spherical mechanism and its exit to the extreme positions in order to determine the working space and the values of the service angles. A system of remote control of the spherical mechanism at the hardware level with an interactive interface and tracking of the positioning target has been implemented. © 2022 IEEE.

关键词： Inverse kinematics

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DYNAMIC INDEPENDENT COMPONENT EXTRACTION WITH BLENDING MIXING VECTOR: LOWER BOUND ON MEAN INTERFERENCE-TO-SIGNAL RATIO

arXiv

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arXiv 2022年

作者： Čmejla, Jaroslav Koldovský, Zbyněk Kautský, Václav Adali, Tülay Faculty of Mechatronics Informatics Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian. Copyright © 2022, The Authors. All rights reserved.

关键词： Blind source separation

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Laparoscopic Tool Classification in Gynaecological Images Using Convolutional Neural Network and Attention Modules

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IFAC-PapersOnLine 2024年第24期58卷 386-390页

作者： T. Abdulbaki Alshirbaji N.A. Jalal H. Arabian P.D. Docherty H. ElMoaqet T. Neumuth K. Möller Institute of Technical Medicine (ITeM) Furtwangen University Villingen-Schwenningen 78054 Germany Innovation Centre Computer Assisted Surgery (ICCAS) University of Leipzig Leipzig 04103 Germany Department of Mechanical Engineering University of Canterbury Christchurch 8041 New Zealand Department of Mechatronics Engineering German Jordanian University Amman 11180 Jordan

Laparoscopic image analysis plays a crucial role in surgical workflow, as it enables the recognition of surgical tools and anatomical structures. While deep learning approaches demonstrate remarkable recognition performance, they demand large amount of labelled data for training. To date, studies were carried out mainly using data from cholecystectomy procedures due to availability of labelled data. In this work, the scope is extended to more intricate gynaecological surgeries. Laparoscopic videos of gynaecological procedures were recorded and surgical tools labelled. A convolutional neural network (CNN) model incorporated attention modules performed a tool classification task. The base-CNN model achieved a mean average precision of 63% over all tools. Experimental results showed a high classification performance for several surgical tools such as the grasper with an average precision of 93%. Furthermore, results revealed higher performance when attention modules were added to the base-CNN model, resulting in ∼7% improvement in the classification performance over all tools. This study demonstrates the CNN's capability in handling gynaecological images and highlights the potential of incorporating attention modules to enhance the model performance.

关键词： Surgical tool classification Gynaecological procedures Laparoscopic images Deep learning Convolutional neural network (CNN) attention module

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Reactive Laser Ablation in Acetone Towards Phase-Controlled Nonequilibrium Iron- and Nickel-Bi2o3 Nanoalloys

SSRN

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SSRN 2023年

作者： Havelka, Ondřej Abdallah, Sabrin Braun, Jan Lukowiec, Dariusz Plachý, Tomáš Cvek, Martin Torres-Mendieta, Rafael Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Studentská 1402/2 Liberec461 17 Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentská 1402/2 Liberec461 17 Czech Republic Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18 a St. Gliwice44-100 Poland Centre of Polymer Systems University Institute Tomas Bata University in Zlín Třída T. Bati 5678 Zlín760 01 Czech Republic

The ongoing development of new nanocatalysts is powered by the ambitious vision of finely controlling their structure while ensuring their remarkable features in heterogeneous chemical reactions. Especially in processes where the catalysts must be recycled, the union of catalytic activity and magnetic motility belongs among the most desirable characteristics. In recent years, the effort to optimize this synergy in photocatalysis has led to a focus on creating bismuth-based bimetallic nanoparticles (NPs), also known as nanoalloys. Nevertheless, the optimization of their properties and sustainable development still need to be well-considered. Therefore, we herein present for the first time the synthesis of phase-controlled nickel-Bi2O3 and iron-Bi2O3 NPs, whose non-equilibrium composition is provided by the sustainable method known as reactive laser ablation in liquids (RLAL), resulting in a remarkable band-gap engineering in both types of magnetic nanoalloys. Besides, the profound analyses of RLAL introduce the first report about the limitations and new perspectives of this relatively novel nanotechnology, in which the usage of chemically similar magnetic foils (iron and nickel) leads to extensively different NPs compositions and properties. Overall, these findings provide a groundwork for understanding the formation of non-equilibrium nanoalloys representing a rising generation of nanocatalysts. © 2023, The Authors. All rights reserved.

关键词： Bismuth compounds

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Zig-zag charged domain walls in ferroelectric PbTiO3

arXiv

引用

arXiv 2022年

作者： Marton, Pavel Gonçalves, Mauro A.P. Paściak, Marek Körbel, Sabine Chumchal, Věnceslav Plešinger, Martin Klíč, Antonín Hlinka, Jirka Institute of Physics Czech Academy of Sciences Na Slovance 2 Prague 8182 21 Czech Republic Institute of Mechatronics and Computer Engineering Technical University of Liberec Studentská 2 Liberec461 17 Czech Republic Institute of Condensed Matter Theory and Solid State Optics Friedrich Schiller University Jena Max-Wien-Platz 1 Jena07743 Germany Department of Mathematics and Didactics of Mathematics Technical University of Liberec Studentská 2 Liberec461 17 Czech Republic

We report a theoretical investigation of a charged 180◦ domain wall in ferroelectric PbTiO3, compensated by randomly distributed immobile charge defects. For this we utilize atomistic shell-model simulations and continuous phase-field simulations in the framework of the Ginzburg-Landau-Devonshire model. We predict that domain walls form a zig-zag pattern and we discuss its properties in a broad interval of compensation-region widths, ranging from a couple to over a hundred nanometers. Copyright © 2022, The Authors. All rights reserved.

关键词： Ferroelectric materials

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Autonomous Power Supply Development for Hydrometeorological Monitoring Station 7

Autonomous Power Supply Development for Hydrometeorological ...

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7th IEEE International Energy Conference, ENERGYCON 2022

作者： Prystupa, Anatoliy Kazymyr, Volodymyr Zabasta, Anatolijs Revko, Anatoliy Stepenko, Serhii Novyk, Kateryna Chernihiv Polytechnic National University Electrical Engineering Information and Measurement Technologies Department Chernihiv Ukraine Chernihiv Polytechnic National University Information and Computer System Department Chernihiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Chernihiv Polytechnic National University Electronics Automation Robotics and Mechatronics Department Chernihiv Ukraine

ISBN: (纸本)9781665479820

This paper presents the results of the investigation of the power supply system of the autonomous monitoring system. In particular, calculations of the system's daily balance of energy consumption were performed. Also, the question of the influence of the angle of inclination of solar converters on energy generation is considered and the necessity of collecting solar radiation during the whole daylight, not focusing on the maximum amount of energy, is substantiated. This research aimed to develop an algorithm for calculating the power supply system for autonomous monitoring systems. The proposed algorithm takes into account the coordination of diagrams of generation and consumption of energy within the autonomous monitoring station and statistical analysis of information from the NASA website. Experiments have shown the effectiveness of this method. It can be used in future design of power supply systems for autonomous monitoring stations. © 2022 IEEE.

关键词： Electric power systems

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