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24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Grabowski, Dariusz Klimas, MacIej Lange, Andrzej Silesian University of Technology Faculty of Electrical Engineering Department of Electrical Engineering and Computer Science Gliwice Poland University of Warmia and Mazury Faculty of Technical Sciences Department of Electrical and Power Eng. Electronics and Automation Olsztyn Poland

ISBN: (纸本)9798350355185

This paper focuses on analyzing power quality issues resulting from electric arc furnace operation. It highlights the challenges posed by the stochastic nature of the arc and proposes strategies to mitigate its impact on the power grid. Through measurements carried out on the medium and low voltage side of the furnace, fluctuations in parameters such as voltage, current, and power factor were observed, indicating significant disturbances in power quality. Mitigation strategies, including e.g. installing compensators, are suggested to stabilize the network and improve power quality, enhancing efficiency and reducing energy consumption and electrode usage. © 2024 IEEE.

关键词： Electric arc furnaces

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GOLDi Labs as Fully Integrated Learning Environment

GOLDi Labs as Fully Integrated Learning Environment

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IEEE World Engineering Education Conference (EDUNINE)

作者： Johannes Nau Karsten Henke Faculty of Computer Science and Automation Ilmenau University of Technology Ilmenau Germany

Our vision is that an online lab will no longer be seen as a collection of monolithically constructed experiments but as a collection of laboratory devices communicating with each other. Nowadays, many interested learners have their own hardware at home. Therefore, an extension to the GOLDi remote lab, we provide learners with an interface unit, which in turn means they can easily connect to this interface with their control units and thus have the possibility to control these complex hardware models (from home). The availability of learning management systems and their LTI (Learning Tools Interoperability) extensibility ultimately leads to a fully integrated learning environment.

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Comparing Player Rating Awarding Systems: Evaluating Performance Metrics in Soccer: An in-Depth Analysis of Rating Methodologies and Their Impact on Player Assessment and Team Performance

Comparing Player Rating Awarding Systems: Evaluating Perform...

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International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Dan Toderici Sergiu-Rares Lupşa Camelia Lemnaru Computer Science Department Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

The primary objective of this paper is to develop a method for accurately and consistently rating football players. A secondary objective is to evaluate the suitability of artificial neural networks for this purpose, particularly with the available data. To address these objectives, data was collected from various sources, including APIs and the WhoScored website via web scraping. Multiple solutions were explored for implementing the player rating system, encompassing both AI-based and mathe-matical approaches. In the pursuit of AI-based models, ratings provided by WhoScored were utilized as a target due to their recognized precision and consistency. Following the implemen-tation of various models, comprehensive testing and evaluation using diverse metrics and methods were conducted to identify the most effective approach, or determine if certain models are better suited for specific scenarios. Overall, this research contributes to the integration of data from multiple sources to enhance player rating methodologies in football analysis.

关键词： Measurement Analytical models Target recognition Soft sensors Artificial neural networks Cognition Sports Testing

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Advanced Analysis of Electro-Oculographic Signal using Deep Neural Networks for Safety Purposes in automation and Production Systems 15

Advanced Analysis of Electro-Oculographic Signal using Deep ...

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15th International Conference on computer and automation Engineering, ICCAE 2023

作者： Kuric, Ivan Stenchlak, Vladimir Zajacko, Ivan Bohusik, Martin Wiecek, Dorota University of Žilina Department of Automation and Production Systems Žilina Slovakia University of Bielsko-Biala Faculty of Mechanical Engineering and Computer Science Bielsko-Biala Poland

ISBN: (纸本)9798350396225

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems. © 2023 IEEE.

关键词： Deep neural networks

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Evaluation of Convolutional Neural Network Model Architecture Performance

Evaluation of Convolutional Neural Network Model Architectur...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Pradana, Musthofa Galih Khoirunnisa, Hilda Pinastawa, I Wayan Rangga Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Bandung Polytechnic for Manufacturing Engineering and Mechatronics Department of Automation Bandung Indonesia

ISBN: (纸本)9798350307306

The identification of image types can be facilitated through the use of algorithms and automation in their detection. In this research, detection is performed on fashion images, which encompass various types and categorizations. The process of identification and detection can utilize the Neural Network algorithm known as Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) algorithm consists of multiple layers, and its application involves setting values for pixels as well as arranging the sequence of layers within it. In this study, two Convolutional Neural Network (CNN) algorithm models are employed for image classification in the fashion dataset. These two models differ in the order of layers within the Convolutional Neural Network (CNN) and in the values of strides applied for modification. Model 1 employs ZeroPadding2D in the first layer with a smaller stride value of 1 compared to Model 2, which places the Conv2D layer in the first layer and applies strides with 1, 2, and 3 values. This research indicates that Model 1 outperforms Model 2, achieving the highest accuracy in epoch 20 with an accuracy value of 0.9711. This is attributed to the fact that the strides value in the Conv2D layer of Model 1 is set to a smaller value compared to Model 2. © 2023 IEEE.

关键词： Classification (of information)

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Anesthesia Control Using Fractional Order Controller

Anesthesia Control Using Fractional Order Controller

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International Conference on Control, automation, Robotics and Vision (ICARCV)

作者： Eva-H. Dulf Paul A. Pintea Cristina I. Muresan Department of Automation Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Anesthesia plays a crucial role in every surgery. Clinicians are faced with numerous conditions that need to be monitored and controlled. A computerized monitoring and control system would help them in this challenging task. Designing a control law for general anesthesia presents numerous challenges, particularly due to the nonlinear mapping of outputs and the complexity of achieving a feasible input trajectory. While the internal states of the mathematical model are linear, the output exhibits nonlinear behavior. This paper introduces a decomposition of the standard pharmacokinetic-pharmacodynamic (PK-PD) model to develop an effective control strategy for drug administration in anesthesia. It is proposed a method to control the bispectral index (BIS), the main measure of unconsciousness of the patient. This approach aims to optimize anesthesia delivery by maintaining the BIS within a desired range, thereby enhancing patient safety and comfort. The methodology involves designing a fractional order controller to accurately track BIS reference values and adjust anesthetic infusion rates. The effectiveness of the controller is validated through simulations, demonstrating its potential to enhance the precision of anesthesia management. Key findings reveal that such type of control outperforms traditional methods in maintaining target BIS levels, minimizing the risks of over- or under-dosing.

关键词： Drugs Robust control Target tracking Surgery Anesthesia Mathematical models Trajectory Safety Standards Robots

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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A Note on the Frequency Characteristics of Discrete Systems in the Complex Plane

A Note on the Frequency Characteristics of Discrete Systems ...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Matousek, Radomil Svarc, Ivan Faculty of Mechanical Engineering Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Frequency characteristics of discrete-time systems in the complex plane and in linear coordinates are used to design controllers (correction terms) by frequency methods, to determine stability by frequency stability criteria, to assess the dynamic properties and behavior of the discrete system with respect to the harmonic components of the input signal, and to design various filters. Therefore, their determination is of considerable importance and their mapping and compilation into comprehensive summaries from which their shape and waveform can be determined is not yet done for discrete systems. This part is the first aim of the paper. Furthermore, a comparison of the construction of frequency characteristics of continuous and discrete linear time-invariant (LTI) systems is made and their differences are highlighted. Also, a comparison is made between the shape and waveform of the frequency characteristics of continuous and discrete systems with formally similar transfer functions. This comparison showed a relatively high similarity, which could be used to determine the dynamical properties of these systems. From the comparison of the constructions can also be seen how difficult it is to construct discrete versus continuous characteristics. © 2022 IEEE.

关键词： Stability criteria

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Automated Topology Synthesis of Analog Integrated Circuits With Frequency Compensation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 832-844页

作者： Zhao, Zhenxin Liu, Jun Zhao, Wen-Sheng Zhang, Lihong Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology Zhejiang Hangzhou310018 China Memorial University of Newfoundland Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering St. John'sNLA1C 5S7 Canada

Analog circuit topology synthesis suffers from weak synthesis capability and low-synthesis efficiency, which result in a bottleneck toward its practical industrial applications. This article presents a proximal-policy-optimization-based circuit topology synthesis framework, which features a superior convergence rate. To further promote its synthesis efficiency, we have improved a deterministic optimization method by incorporating a bias-aware scheme and group concept, which is applied as a filter to eliminate the undesirable topologies in the early evaluation stage. Moreover, a graph-based refinement scheme is proposed to perform deterministically on the generated circuit topologies, which can efficiently add frequency compensation circuits. Compared with the state-of-the-art approaches, our proposed method not only boosts the synthesis efficiency by at least 3 times but also enhances the synthesis capability with a deterministic compensation scheme, showcasing significant advancement of performance efficacy. © 2024 IEEE.

关键词： Network topology

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Working on the Structural Components of Evolutionary Approaches 16th

Working on the Structural Components of Evolutionary Approac...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Tzanetos, Alexandros Kůdela, Jakub Jönköping University Gjuterigatan 5 Jönköping553 18 Sweden Institute of Automation and Computer Science Faculty of Mechanical Engineering Brno University of Technology Czech Republic

ISBN: (纸本)9789897587214

Several researchers have turned their attention to the structural components of Evolutionary Computation and Swarm Intelligence-oriented approaches. This direction offers various opportunities, such as developing automatic design and configuration frameworks and integrating operators and mechanisms addressing known limitations. This work lists recent operators and discusses promising mechanisms found in existing natureinspired approaches. It also discusses how these algorithmic components can be integrated into modular frameworks and how they can be assessed and benchmarked. The work aims to emphasize the importance of the research direction about nature-inspired mechanisms and operators in the Evolutionary Computation field. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Swarm intelligence

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