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International Workshop on Advanced Motion Control (AMC)

作者： Haruta Shimamoto Takashi Yoshioka Shiro Urushihara Advanced Course in Industrial and Systems Engineering National Institute of Technology Kagawa College Takamatsu Japan Dept. of Electrical and Computer Engineering National Institute of Technology Kagawa College Takamatsu Japan

In this paper, to expand a control bandwidth of AC servo motor, a new current vector control considering an output delay of PWM Inverter is proposed. The proposed current control is a I-P control with an additional feedback gain, which is based on an extended state feedback consisted from a d-q motor model, the output delay of PWM inverter and an integrator of ACR. In addition, the proposed method is easy to implement to the existing AC servo system. The effectiveness of proposed method is validated in a computer simulation and an experiments using a 3.7 kW class motor test bench. In the condition that a cut-off frequency of ACR is close to a carrier frequency, the simulation results show that the proposed method dramatically improves the current response at the higher cut-off frequency. Moreover, the experimental results show that the proposed method dramatically improves the current response at each operating point. From the results, the proposed method is effective to the expand the bandwidth of AC servo motor.

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Integrated Modeling and Optimal Operation of Multi-Energy Sy...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Chen, Yang Chen, Jun Liu, Chenang Liu, Guodong Ferrari, Maximiliano Sundararajan, Aditya Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University Greensboro United States Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States School of Industrial Engineeering and Management Oklahoma State University Stillwater United States Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9781665493765

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands. © 2023 IEEE.

关键词： Desalination

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Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology

Between Generating Noise and Generating Images: Noise in the...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nati Daniel Eliel Aknin Ariel Larey Yoni Peretz Guy Sela Yael Fisher Yonatan Savir Department of Physiology Biophysics and Systems Biology Faculty of Medicine Haifa Israel Faculty of Industrial Engineering Haifa Israel Faculty of Computer Science Haifa Israel Faculty of Electrical Engineering Haifa Israel Division of Pathology Rambam Health Care Campus Haifa Israel

Artificial intelligence and machine learning techniques have the promise to revolutionize the field of digital pathology. However, these models demand considerable amounts of data, while the availability of unbiased training data is limited. Synthetic images can augment existing datasets, to improve and validate AI algorithms. Yet, controlling the exact distribution of cellular features within them is still challenging. One of the solutions is harnessing conditional generative adversarial networks that take a semantic mask as an input rather than a random noise. Unlike other domains, outlining the exact cellular structure of tissues is hard, and most of the input masks depict regions of cell types. This is also the case for non-small cell lung cancer, the most common type of lung cancer. Deciding whether a patient would receive immunotherapy depends on quantifying regions of stained cells. However, using polygon-based masks introduce inherent artifacts within the synthetic images – due to the mismatch between the polygon size and the single-cell size. In this work, we show that introducing random single-pixel noise with the appropriate spatial frequency into a polygon semantic mask can dramatically improve the quality of the synthetic images. We used our platform to generate synthetic images of immunohistochemistry-treated lung biopsies. We test the quality of the images using a three-fold validation procedure. First, we show that adding the appropriate noise frequency yields 87% of the similarity metrics improvement that is obtained by adding the actual single-cell features. Second, we show that the synthetic images pass the Turing test. Finally, we show that adding these synthetic images to the train set improves AI performance in terms of PD-L1 semantic segmentation performances. Our work suggests a simple and powerful approach for generating synthetic data on demand to unbias limited datasets to improve the algorithms’ accuracy and validate their robustness.

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Mitigation of Equipment Overload and Undervoltage for Electric Vehicle Charging in Low-Voltage Distribution System

Mitigation of Equipment Overload and Undervoltage for Electr...

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IEEE International Smart Cities Conference (ISC2)

作者： Papungkorn Sihawong Somporn Sirisumrannukul Ongorn Rattananatthawon Noppatee Sabpayakom Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical Engineering Technology College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

This paper examines the impact of the rising use of electric vehicle (EV) chargers on an 18-bus low-voltage distribution system, with a focus on challenges such as overloading, energy losses, and voltage quality degradation that may compromise the efficiency of the power distribution network. To closely replicate real conditions, this research employs DIgSILENT PowerFactory to simulate and analyze a 18-bus distribution system, utilizing the Quasi-Dynamic Simulation function to accurately model the charging behavior of EV users. The analysis considers critical variables, including EV charging power, battery capacity, charging start and end times, and the state of charge (SOC). The simulation results reveal that the concurrent operation of multiple EV chargers during peak load periods can lead to transformer and feeder overloading, increased energy losses, and voltage drops below acceptable thresholds. Consequently, this study proposes an EV charger management system designed to mitigate overloads, reduce energy losses, and address voltage quality issues, thereby enhancing the stability and efficiency of the distribution network to support the expanding adoption of EV technology.

关键词： Energy loss Low voltage Power distribution Power system stability Transformers Control systems Stability analysis Electric vehicle charging State of charge Thermal stability

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Evaluation of Solar and Lunar Eclipse Impacts on Danish Power System as a Case Study

Evaluation of Solar and Lunar Eclipse Impacts on Danish Powe...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Hamed Kheirandish Gharehbagh Sajjad Miralizadeh Jalalat Navid Bayati Thomas Ebel Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Departemant of Electrical and Computer Engineering University of Urmia Urmia Iran Centre for Industrial Electronics University of Southern Denmark Sønderborg Denmark

Power systems stability and reliability are two critical issues that are considered in designing and operational states of the power systems. In addition to the technical failures that can affect the appropriate performance of the power systems, there are several environmental menaces, namely, storms, earthquakes, freezing weather, animal-caused outages, etc. In this paper, the effect of solar and lunar eclipses on the Danish power system has been investigated among the other environmental threats. The power system of Denmark, due to the high penetration of renewable energy sources, is contemplated as a case study. The reports and data depict that despite the often occurrence of an eclipse, these astronomical events' impacts should have been taken more into consideration to maintain stability and improve the reliability of the power system.

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A Comparative Study of Energy Graph-Based Fault Detection and Isolation Techniques Applied to a Lignite Plant

SSRN

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SSRN 2023年

作者： Smith, JanHendrik van Schoor, George Uren, Kenneth Richard van Eldik, Martin Worlitz, Frank School of Electrical Electronic and Computer Engineering Faculty of Engineering North-West University South Africa Unit for Energy and Technology Systems Faculty of Engineering North-West University South Africa School of Mechanical Engineering Faculty of Engineering North-West University South Africa Faculty of Electrical Engineering and Computer Science University of Applied Sciences Zittau Görlitz Germany

Energy and exergy distribution in a system holds theoretical meaning across the physical domains of these systems and can therefore convey important information regarding its health. This energy information, however, requires careful analysis and representation to sufficiently convey its meaning. In this paper, a comparative study of three such analysis methods for the purpose of fault detection and fault isolation are discussed. These methods are designated as: 1) distance parameter method, 2) eigenvalue decomposition method and 3) residual method. The study utilises data from a physical lignite plant in Janschwalde, Germany, in combination with simulation data of specific faults in order to compare the sensitivity and robustness of the three methods. This is evaluated with respect to fault detection and isolation when noise and fluctuation in the system’s operating conditions are present at the full load operating point of the system. Each of these methods is an energy graph-based technique that relies on the energy information encapsulated within a node signature matrix, as defined by modern graph theory. The graphs capture the process topology and connectivity showing the causal relationship between the energy variables in a system, making it ideal for FDI applications. © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Using Ensemble Learning, A Cosine Similarity-Based Model for Detecting Security Anomalies in Software-Defined Networks

Using Ensemble Learning, A Cosine Similarity-Based Model for...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Mohammadreza Samadzadeh Mohammad Hadi Zahedi Elham Farahani Department of Electrical and Computer Engineering University of Tehran Tehran Iran Information Technology department Faculty of Industrial Engineering K. N. Toosi University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

Currently, Software-Defined Network (SDN) is as one of the most commonly used network architectures. It requires using a software-based, centralized controller to communicate with the underlying network hardware to control the flow of the traffic through the network. This helps achieve easy, flexible, and integrated control and management of the entire network. In SDNs, the controller is the sole entity that monitors the entire network and is accountable for traffic management based on its comprehensive understanding of the network. Software-Defined Networks (SDNs) rely on a centralized controller for network management, yet this centralized control exposes vulnerabilities, leaving networks susceptible to disruptive attacks. To fortify SDN security, this article presents an innovative approach that merges machine learning and deep learning techniques through ensemble learning. Our method introduces several key innovations. Firstly, it employs ensemble learning, combining multiple models to enhance predictive capability. Secondly, the utilization of cosine similarity-based classification enables the grouping of attacks with varying degrees of similarity, refining the distinction between attack types. Thirdly, the approach includes customized classifiers, trained specifically for distinct attack classes, optimizing detection accuracy for each type. Finally, the K-Nearest Neighbor (KNN) algorithm is applied for final classification of new data samples, improving precision in identifying attack types. With an exceptional accuracy rate of 99.91%, our method surpasses the performance of analogous studies. By amalgamating these innovations, our approach establishes a robust framework for detecting security anomalies in SDNs, reinforcing network integrity and reliability.

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Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第10期18卷 90-105页

作者： El Ghazouani, Mohamed Ikidid, Abdelouafi Zaouiat, Charafeddine Ait Aziz, Layla Ichahane, Moulay Youssef Er-Rajy, Latifa ESIM Polydisciplinary Faculty of Sidi Bennour Chouaîb Doukkali University El Jadida Morocco Laboratory of System Analysis Information Processing and Industrial Management EST Salé Mohammed V University Salé Morocco Information Technology Laboratory National School of Applied Sciences Chouaîb Doukkali University El Jadida Morocco Computer Science Department Laboratory of Information Systems Engineering Cadi Ayyad University Marrakesh Morocco

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios. © 2024 by the authors of this article.

关键词： Multi agent systems

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Data Augmented Rule-based Expert System to Control a Hybrid Storage System

Data Augmented Rule-based Expert System to Control a Hybrid ...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Ricardo J. Bessa Francisco Lobo Francisco Fernandes Bernardo Silva Centre for Power and Energy Systems (CPES) INESC TEC Porto Portugal CPES & Department of Electrical and Computer Engineering INESC TEC & Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Li-ion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.

关键词： Training Renewable energy sources Power system measurements Reinforcement learning Microgrids Redox Power system stability

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