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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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Intelligent malware – trends and possibilities

Mendel

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Mendel 2021年第1期27卷 18-22页

作者： Plucar, Jan Frank, Jiří Walter, Daniel Zelinka, Ivan Faculty of Electrical Engineering and Computer Science VŠB-TU Department of Computer Science Czech Republic

In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a successful defence, we must be able to anticipate future threats that may come. For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment. This software has not only malware capabilities but also antimalware and can be used on both sides. This article introduces the reader to the main principles of our design, which can serve as a future platform for cyber defence systems. © 2021, Brno University of Technology. All rights reserved.

关键词： Neural networks

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The 5S Method and Its Strategic Determinants Within the Organization of Production Plants 32nd

The 5S Method and Its Strategic Determinants Within the Orga...

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Crnjac Milić, Dominika Faculty of Electrical Engineering Computer Science and Information Technology in Osijek Josip Juraj Strossmayer University of Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031514937

Racing for the global advantage, companies have been paying increasing attention to workplace design, employee safety, workplace satisfaction, labor productivity and product quality. One of the methods that can help them achieve these goals is the 5S method, which is also considered a management tool for better organization and management of the workplace. Although the 5S method is applicable to a wide range of workplaces, using published research results, this paper provides a comprehensive overview of its challenges and benefits in manufacturing companies. They are analyzed and synthesized in a new way and special attention is paid to the method steps and its implementation. The paper points to a wide scope of the method application as companies have to meet various standards and present certificates that guarantee the safety and quality of manufactured products. Since managers are constantly trying to find ways to obtain and maintain them, one of the objectives of this paper is to provide an overview and critical evaluation of existing published research results that show the impact of the method application on increasing the product quality, the production process, health and safety. The methods from the qualitative methodological framework were used and research results that contribute to the scientific discourse related to the results that can be achieved by applying the 5S method in the organization of production plants were presented. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industrial research

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Motor Speed Control Based on Enhanced Indirect-Field-Oriented Control Using Convolutional Neural Network 49

Motor Speed Control Based on Enhanced Indirect-Field-Oriente...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Sepeeh, Muhamad Syazmie Zulkifli, Shamsul Aizam Yi, Sim Sy Chiu, Huang-Jen Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronics Engineering Department of Electrical Power Engineering Johor Malaysia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798350331820

In this study, an indirect-field-oriented control (IFOC) of an induction motor (IM) was simulated and its speed was estimated using the model reference adaptive system (MRAS) topology of the modified voltage-model-based rotor-flux-oriented current model. As this study dealt with the convolutional neural network (CNN) implemented as the speed controller for the outer-loop control, the inner-loop control used proportional-integral (PI) control to control the current that was fed to the space vector pulse-width modulation (SVPWM) algorithm. The CNN used two inputs based on speed error, and it was designed and trained offline by employing the backpropagation (BP) algorithm to minimize the error between the desired speed and the speed estimated by the estimator and to improve the model's speed-tracking capability when there was an unpredicted speed at the input. The study also used a variable speed trajectory for testing with various speed references to mimic road speed conditions. The results are in good agreement with those of similar studies, and a comparison between the performances of the PI controller and the proposed CNN controller indicated that the CNN controller yielded a superior performance on IM speed. © 2023 IEEE.

关键词： Induction motors

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PiGPDS: Perspective Independent Ground Plane Detection and Segmentation for Complex 3D Indoor Scenes

PiGPDS: Perspective Independent Ground Plane Detection and S...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Ebrahimi, Ali Czarnuch, Stephen Memorial University of Newfoundland Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering St. John's Canada Memorial University of Newfoundland Discipline of Emergency Medicine Department of Electrical and Computer Engineering St. John's Canada

ISBN: (纸本)9798350382204

Ground plane detection and segmentation techniques can benefit and help improve the accuracy and robustness of a wide range of computer vision applications, from 3D object segmentation and autonomous navigation to mixed and augmented reality. Existing approaches often rely on restrictive assumptions to simplify the problem, such as the ground plane being the largest plane in the scene or the camera location or orientation being ideal. We present a ground plane segmentation technique for real-world 3D indoor scenes where the position and orientation of the sensor are unrestricted and unknown. Our method only requires one 3D point cloud of an indoor scene and assumes that the scene contains at least one surface parallel to the actual ground plane, which is generally true for 3D indoor scenes. We begin by utilizing a voxelized grid downsampling method to enhance the speed of the algorithm. Subsequently, we use K-medoids clustering and an angular-based zone determination technique to identify the ground zone. Next, we divide the ground zone into several clusters using the Euclidean clustering algorithm, and we employ the M-estimator Sample Consensus (MSAC) algorithm to fit the largest plane in each cluster with a specific orientation. Finally, based on the geometric relationship between the fitted planes of the ground zone, we estimate the ground plane, verify it and segment all its associated points using a distance-based approach. We evaluated our method on public and self-generated datasets, in which we positioned a depth sensor at various locations, pitches, and yaws. Our experimental results demonstrate that our proposed method can robustly and efficiently detect and segment the ground plane of complex 3D indoor scenes and supports varied sensor locations and orientations. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, with average experimental results of 98.28, 95.48, 96.64, a

关键词： Clustering algorithms

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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Split Cost Function based Single-Horizon Finite-Set Model Predictive Control for Boost Converter with Loading Current Estimation

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tatari, Fatemeh Rezayof Bizhani, Hamed Iwanski, Grzegorz Warsaw University of Technology Faculty of Electrical Engineering Warsaw Poland Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

This paper presents a robust single-horizon finite-set model predictive control (FS-MPC) method for controlling the output voltage of boost converters. Unlike the long-horizon FS-MPC approach, which addresses the non-minimum phase (NMP) behavior of the converter by extending the prediction horizon (thereby increasing computational effort), the proposed method ensures stable voltage regulation by incorporating inductor current regulation into the cost function. To achieve this, the desired inductor current is calculated based on input-output power balance theory. Unlike recent studies that require an additional sensor to measure the loading current, this paper develops a loading current estimation approach to eliminate the need for extra current sensors, thereby enhancing the robustness of the proposed FS-MPC method. Additionally, over-current protection, which is typically absent in conventional FS-MPC used for direct voltage regulation, is also incorporated into the proposed method. The conventional cost function is also modified to tackle the NMP behavior more effectively. The performance of the proposed method in regulating the output voltage is evaluated through simulation results. Finally, its effectiveness across a wide range of output power and various load characteristics, including resistive, constant power, and constant current loads, is validated via experimental results. The results demonstrate that the proposed method is not only able to eliminate the overshoot and undershoot, but can also improve the settling time by 41% compared to a well-tuned conventional cascaded PI controller. In addition, the proposed method can reduce the computational burden by 84% compared to the conventional long-horizon FS-MPC approach. © 2013 IEEE.

关键词： Cost functions

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An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer

arXiv

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arXiv 2025年

作者： Zare, Hadi Abbasi, Mostafa Ahang, Maryam Najjaran, Homayoun Department of Electrical and Computer Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada Department of Mechanical Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada

Purpose - In Business Process Management (BPM), accurate prediction of the next activities is vital for operational efficiency and decision-making. Current Artificial Intelligence (AI)/Machine Learning (ML) models struggle with the complexity and evolving nature of business process event logs, balancing accuracy and interpretability. This paper proposes an entropy-driven model selection approach and DAW-Transformer, which stands for Dynamic Attribute-Aware Transformer, to integrate all attributes with a dynamic window for better accuracy. Design/methodology/approach - This paper introduces a novel next-activity prediction approach that uses process entropy to assess the complexity of event logs and dynamically select the most suitable ML model. A new transformer-based architecture with multi-head attention and dynamic windowing mechanism, DAW-Transformer, is proposed to capture long-range dependencies and utilize all relevant event log attributes. Experiments were conducted on six public datasets, and the performance was evaluated with process entropy. Finding - The results demonstrate the effectiveness of the approach across these publicly available datasets. DAW-Transformer achieved superior performance, especially on high-entropy datasets such as Sepsis exceeding Limited window Multi-Transformers by 4.69% and a benchmark CNN-LSTM-SAtt model by 3.07%. For low-entropy datasets like Road Traffic Fine, simpler, more interpretable algorithms like Random Forest performed nearly as well as the more complex DAW-Transformer and offered better handling of imbalanced data and improved explainability. Originality/ value - This work’s novelty lies in the proposed DAW-Transformer, with a dynamic window and considering all relevant attributes. Also, entropy-driven selection methods offer a robust, accurate, and interpretable solution for next-activity prediction. © 2025, CC BY-NC-SA.

关键词： Administrative data processing

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EEG Information Transfer Changes in Different Daily Fatigue Levels during Drowsy Driving

IEEE Open Journal of Engineering in Medicine and Biology

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IEEE Open Journal of engineering in Medicine and Biology 2024年 5卷 180-190页

作者： Huang, Kuan-Chih Tseng, Chun-Ying Lin, Chin-Teng National Yang Ming Chiao Tung University Brain Science and Technology Center Hsinchu300 Taiwan University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and IT SydneyNSW2007 Australia National Yang Ming Chiao Tung University Brain Science and Technology Center Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power;and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median-and high-fatigue) with the EEG data transfer entropy. According to the results, only low-and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low-to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue. © 2020 IEEE.

关键词： Electroencephalography

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Enhancing Power Grid Stability: Design and Integration of a Fast Bus Tripping System in Protection Relays

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 561-570页

作者： Ghiasi, Mohammad Wang, Zhanle Mehrandezh, Mehran Alhelou, Hassan Haes Ghadimi, Noradin University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada School of Engineering CambridgeMA United States Monash University Department of Electrical and Computer System Engineering Australia Islamic Azad University Young Researchers and Elite Club Ardabil Branch Ardabil Iran

This article introduces a novel method for efficiently and promptly operating protection relays within a power system, with a specific emphasis on adaptive overcurrent (OC) protection in a power grid. The approach utilizes SEL751 and SEL751A relays that communicate through the IEC-61850 GOOSE protocol, establishing connections using intelligent electronic devices (IED) at several locations in the system. The power system, consisting of transformers, bus bars, and feeders, employs protection relays to identify OC faults based on time and current parameters. Relay configurations are tested in a case study at the Omicron Lab involving a 138/25kV substation. The test encompasses various fault scenarios on the low-voltage (LV) and high-voltage (HV) sides, incorporating different current values and fault times, utilizing the chosen time-overcurrent U3 (very inverse) curve. The proposed approach exhibits the ability to promptly remove faults in transformers and substations at both the HV and the LV levels. Furthermore, the experimental results closely match the theoretical calculations, validating the effectiveness of the proposed approach in the protection of AC grids. Additionally, built-in cybersecurity measures in SEL relays ensure secure and reliable operation, mitigating the risks of cyber-related relay failures. © 1975-2011 IEEE.

关键词： Relay protection

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