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Smart Grid Conference (SGC)

作者： Mohammad Mohsen Hayati Hassan Majidi Hossein Shahinzadeh Mehdi Abapour Mahmud Fotuhi-Firuzabad Gevork B. Gharehpetian Faculty of Electrical and Computer Engineering Energy Systems Research Institute (ESRI) University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Center of Excellence in Power System Management and Control (CEPSMC) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Smart energy networks (SENs) face numerous challenges related to security, reliability, operational uncertainties, and increasing penetration of renewable energies (REs), necessitating advanced methodologies for adequacy and security assessment. This study delves into the utilization of machine learning-driven approaches (MLDAs) to quantify reliability and gauge the dependability of future sustainable SENs. By autonomously acquiring knowledge from datasets, MLDAs offer swift and reliable evaluations, surpassing traditional methodologies. The integration of artificial intelligence (AI) in enhancing power system reliability is highlighted, emphasizing the importance of extensive datasets and evolving AI algorithms. Data-driven approaches empower energy network operators to proactively identify and mitigate security risks, enhancing the reliability of critical infrastructure systems. The research underscores the significance of constructing reliable databases, addressing challenges in real-time operations, and ensuring the performance and trustworthiness of machine learning (ML) algorithms. Overall, the study showcases the potential of ML techniques in revolutionizing reliability management and risk assessment in smart energy networks, paving the way for sustainable and reliable power systems.

关键词： Uncertainty Machine learning algorithms Databases Machine learning Power system reliability Smart grids Management Reliability Security Risk management

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Brain Tumor Classification system Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Thaiparnit, Sattarpoom Janchidfah, Thanakrit Farooq, Adil Rajamangala University of Technology Suvarnabhumi Faculty of Business Administration Dep. Of Information System and Computer Business Suphan Buri72130 Thailand Muban Chombueng Rajabhat University dep. Of Digital Media Technology Ratchaburi70150 Thailand Maynooth University Department of Electronic Engineering Ireland

ISBN: (纸本)9798331533267

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors. © 2025 IEEE.

关键词： Random forests

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN 16

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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16th IEEE International Conference on Intelligent systems and Knowledge Engineering, ISKE 2021

作者： Zhou, Lei Yu, Cheng Liu, Hu Li, Xin Li, Fenfen Zhang, Haiyan National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology huai'An Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Jiangsu China Nanjing University of Technology Nanjing College of Computer Science and Technology Jiangsu211816 China Huaiyin Institute of Technology huai'An Faculty of Chemical Engineering Jiangsu China

ISBN: (纸本)9781665405539

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved. © 2021 IEEE.

关键词： Fault detection

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Sensor Infused Emperor Penguin Optimized Deep Maxout Network for Paralyzed Person Monitoring

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IEEE Sensors Journal 2024年

作者： Yang, Jing Hu, Ruoyang Wu, Chenwei Jiang, Gaozhe Alkanhel, Reem Ibrahim Elmannai, Hela Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Boston University Sargent College BostonMA02115 United States Evident Battery Inc MA02138 United States National University of Singapore Singapore Institute of Operations Research and Analytics Singapore119007 Singapore Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia

Paralyzed people must maintain proper posture to prevent respiratory problems, pressure sores, and muscular contractures. A paralyzed person may slip out of their wheelchair several times as a result of poor sitting position. To overcome these challenges, a novel Emperor Penguin optimized sensor Infused wheelChair (EPIC) framework has been proposed to constantly monitor the real-time posture of the paralyzed person's health state. The proposed model aims to monitor wheelchair posture and health in real-time, which can automatically detect posture issues, providing timely alerts and feedback to the user via a mobile application. The proposed framework utilizes the Emperor Penguin Optimizer (EPO) algorithm for feature selection to improve the accuracy of posture detection. A Deep Maxout Network (DMN) analyzes the features to predict the posture of the wheelchair-bound patient. The proposed EPIC framework has been assessed using a Python simulator. The effectiveness of the proposed EPIC framework has been determined using evaluation metrics such as precision, specificity, accuracy, and sensitivity. The proposed EPIC technique advances the overall accuracy by 10.1%, 7.73%, and 2.84% better than posture recognition, Graphic User Interface (GUI), and Independent Component Analysis-Kantorovich Distance (ICA-KD) respectively. © 2024 IEEE.

关键词： Graphical user interfaces

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A Survey of Information Security Roles and Trends in the Quranic Perspective

A Survey of Information Security Roles and Trends in the Qur...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Mohd Amin Mohd Yunus Nurul Hidayah Ab Rahman Isredza Rahmi A. Hamid Applied Information System Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Centre of Information Security Research Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

In this study, we surveyed the existing information security literature related to the Quran to contribute to the knowledge gap(s) in seeking the role of information security in the Quran research area. Materials published in English were located between January 2019 and February 2024 by searching various academic databases, including Google Scholar, IEEEXplore, ACM Digital Library, Springer and ScienceDirect. Thirty-two publications were collected and classified into ten domains of information security Common Body of Knowledge to identify the research trends. The results show that Domain 3 (Cryptography and Digital Watermarking) is the most studied area, followed by Domain 2 (Ethical and Legal Issues) with eight publications and Domain 5 (Access Control) with three publications. Other domains consist of only 1 or 2 publications each. Our observation also showed that three recurring themes of studies that are the implementation of information security techniques to preserve digital Quran authenticity, the association of Quran verses in supporting information security approaches; and the development of information security techniques from the Quran epistemology. Overall, exploring the research trends and interconnected areas could lead to advancements in information security practices and Quranic studies, fostering how traditional wisdom influences modern technologies.

关键词： Surveys Ethics Law Databases Information security Watermarking Market research Libraries Internet Cryptography

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Collisions in block ciphers: Application to small-scale AES variants

Collisions in block ciphers: Application to small-scale AES ...

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7th International Cryptology and Information Security Conference 2020, CRYPTOLOGY 2020

作者： Z'aba, Muhammad Reza Mohd Zuki, Muhammad Akmal Hakim Abdul Wahab, Ainuddin Wahid Kiah, Miss Laiha Mat Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

A block cipher should act as a permutation. For a given secret key, two different input plaintext blocks would never result in a collision, i.e. be encrypted to the same ciphertext. This article investigates whether or not two different secret keys would yield a collision (i.e. encrypted to the same ciphertext). Such collisions may have negative effects on hash function constructions based on block ciphers such as Davies-Meyer and Matyas-Meyer-Oseas where the block cipher key is the input message to the hash function. In order to find such collisions, we conducted several experiments on small scale variants of the block cipher AES. © 2020 ACM.

关键词： Hash functions

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, Engineering, Science, and technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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A Surveillance system Utilizing mmWave-FMCW Radar for Restricted Control Area

A Surveillance System Utilizing mmWave-FMCW Radar for Restri...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we introduce a surveillance system utilizing mmWave-FMCW radar designed for restricted control areas. The experiment, conducted in our laboratory, defines the restricted control area parameters. Employing the TI-IWR6843AOPEVM model, operating at 60–64 GHz, and providing point cloud and Doppler information, we assumed walking paths for testing, partitioning scenarios into four distinct cases to simulate real-world scenarios. system performance evaluation, assessed through confusion matrices, demonstrates an achieved accuracy of up to 76.9%. Notably, errors are mainly caused by multipath effects due to static objects within the testing environment.

关键词： Point cloud compression Measurement Accuracy Surveillance system performance Radar tracking Doppler radar

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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