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Systematic Common information Model (CIM) Profile Management for Advanced Distribution Management System

Transactions of the Korean Institute of Electrical Engineers 2024年第1期73卷 1-9页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Jung, Wonwook Shim, Jaeseong Shon, Jin-Man Dept. of Electrical Engineering Hoseo University Korea Republic of Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Korea Electric Power Corporation Research Institute Rep. of Korea Korea Republic of

Common information Model (CIM) is an essential technique for ensuring interoperability in Energy Management Systems (EMS) by defining data profiles for information exchange. Given the changing system environment, CIM undergoes periodic revisions, necessitating updates to the profiles used in EMS. However, comparing and interpreting CIM as defined in the Unified Modeling Language (UML) can be time-consuming and may lead to human errors. In this context, this study proposes systematic approaches for managing data profiles using an automatic tracking function for CIM and a structured process for profile management. The effectiveness of this approach is demonstrated by applying it to profiles defining the key functions of Korea's advanced distribution management system (ADMS), such as operational limits, renewable energy sources, and power transformers. This research can serve as a guideline for addressing practical issues in securing interoperability in ADMS. © 2024 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Interoperability

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Improving Image Fidelity using Skip Connections Autoencoder 16

Improving Image Fidelity using Skip Connections Autoencoder

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16th International Conference on information technology and Electrical engineering, ICITEE 2024

作者： Rumaksari, Atyanta Nika Hidayat, Risanuri Hartanto, Rudy Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350375817

This study presents an advanced autoencoder model with skip connections designed to enhance the image fidelity quality. Traditional methods often face challenges in preserving high-resolution details and textures, particularly in tasks involving noise reduction, super-resolution, and low-light enhancement. Our model leverages skip connections to maintain essential spatial information across different layers, effectively addressing the vanishing gradient problem and improving gradient propagation during training. We compare our model against several state-of-the-art methods, including the Overall Improved Autoencoder (OIAE), Cascade Decoders-Based Autoencoders, and Edge-Aware Autoencoder Design, evaluating performance based on Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Our autoencoder consistently achieves superior results, with MSE of 5.889e-05, PSNR consistently above 40 dB, and SSIM close to 1. These findings demonstrate the significant advantages of incorporating skip connections, which allow for better retention of high-resolution features and more accurate image reconstructions. © 2024 IEEE.

关键词： Image reconstruction

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Smart Vehicle Procurement System using Blockchain technology 3

Smart Vehicle Procurement System using Blockchain Technology

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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Learning of the Exploratory Behavior of Mobile Robots for Environmental Segmentation 10

Learning of the Exploratory Behavior of Mobile Robots for En...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Gouko, Manabu Tohoku Gakuin University Faculty of Engineering Dept. of Information Technology Sendai Japan

ISBN: (纸本)9798350394245

A model that can describe the operating environment of robot is necessary for mobile robots to operate effectively in real-world environments. Environmental segmentation, which classifies environments based on their features, is useful for environmental modeling. To realize autonomous environmental segmentation by a robot, the robot must explore through its environment and collect data. In previous studies, the exploratory behavior of a robot for data collection was predesigned by a designer. In addition to the information on environment, those on physical features of robots are essential for design the robot's behavior. Additionally, the exploratory behavior must be redesigned based on the changes in the environment. In this study, an autonomous learning method is proposed for the exploratory behavior of mobile robots using reinforcement learning. Moreover, the effectiveness of the proposed method is verified via a simulation experiment. Results revealed that the feature-based environmental segmentation can be performed using the sensor information obtained from the exploratory behavior of robot, realized with the proposed method. By using the proposed method, robots can be expected that automatically explore new environments 'collect data' and segment the environment. © 2024 IEEE.

关键词： Mobile robots

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An Automatic Generation of Test Cases in JUnit Testing Framework Using Genetic Algorithm 11

An Automatic Generation of Test Cases in JUnit Testing Frame...

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11th IEEE International Conference on Systems and Control, ICSC 2023

作者： Amarif, Mabroukah Alfitouri, Aisha Allag, Zaydan Sebha University of Libya Faculty of Information Technology Dept. of Information Systems Sebha Libya Sebha University of Libya Faculty of Information Technology Dept. of Computer Sciences Sebha Libya Sebha University of Libya Information Technology Faculty Dept. of Computer Sciences Sebha Libya

ISBN: (纸本)9798350304886

The testing phase is one of the most important phases of the software life cycle, which ensures the efficiency and quality of the software product. Many tests are conducted on the software, not only to detect errors, but also to provide a complete picture of the quality of the outputs provided by the program and to increase confidence in all of the functions. Due to the difficulty in estimating most of the test data and the time spent in this process, many techniques are used in the automatic generation of test cases. The genetic algorithm is one of the most important used methods in this process, and it has achieved great success in generating limited and appropriate test cases to give a complete conceptualization of the function under test. The Junit testing framework is used in automated testing processes within test-based development methodologies, but it lacks of a mechanism to specify and generate a specific test data that put the program under test in the required scenario. In order for the programmer to be able to perform the test automatically in an integrated way within JUnit testing framework, the genetic algorithm has been improved in this research and emerged into the JUnit testing framework to fully automatic testing and generate various numeric test cases at the unit-test stage. Based on testing of the proposed algorithm within the Junit testing framework, it has proven its effectiveness and facilitation of generating perfect specific numeric test cases. © 2023 IEEE.

关键词： Software testing

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Optimizing Wind Energy Forecasting: A Comparative Study of ANN and Decision Tree Algorithms

Optimizing Wind Energy Forecasting: A Comparative Study of A...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Das, Arpan Saha, Debasree Gain, Shrayasee Vaibhav, Ankur Chakraborty, Payel De, Brishti Guru Nanak Institute of Technology Dept. of Electrical Engineering Kolkata India Guru Nanak Institute of Technology Dept. of Information Technology Kolkata India

ISBN: (纸本)9798350378047

Accurate wind power forecasting is crucial for efficient power grid operation with significant wind power integration. This study compares tree-based learning algorithms and advanced machine learning methods to optimize forecasting. Seventeen-year models using tree-based algorithms showed accuracy degradation with longer sampling intervals and height extrapolation. Models were tested with varied heights and locations, demonstrating tree-based algorithms' long-term forecasting effectiveness. Combined datasets and the k-nearest neighbors algorithm filled missing data. Statistical indicators showed ANN's robust forecasting capabilities. MATLAB and Jupyter Python provided insights into optimizing models for reliable wind energy production. © 2024 IEEE.

关键词： Wind forecasting

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Leveraging Deep Learning for Identification of Medicinal Plant Species 2

Leveraging Deep Learning for Identification of Medicinal Pla...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Vidya, H.A. Narasimha Murthy, M.S. Thara, D.K. KIT Tiptur Vtu Dept. of Computer Science and Engineering Belagavi India BMSIT&M Bengaluru Vtu Dept. of Information Science and Engineering Belagavi India CIT Gubbi Vtu Dept. of Information Science and Engineering Belagavi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373110

Identifying medicinal plant species is crucial in plant taxonomy because it allows us to harness the therapeutic properties of medicinal plants for human health. This information can be beneficial for the advancement of new drugs and treatments and for promoting the sustainable use of medicinal plants. Allopathic medicine has side effects in many cases. Traditional medicine, like Ayurveda, provides alternative treatments with minor side effects and helps protect human health. The decline of many medicinal plant species highlights the need for botanical research and biodiversity conservation. Artificial intelligence builds a gap between applications of computer technology in plant taxonomy. The goal of the study is to develop a reliable deep learning system for the recognition of medicinal plant species, which can subsequently aid in their preservation and support ongoing research efforts in this field. This paper focuses on a deep learning approach based on Convolutional Neural Network (CNN) to identify three distinct categories of medicinal plants. The model proposed uses CNN architecture and two pre-trained models, InceptionV3 and ResNet50, to classify three classes of medicinal plants, namely neem, peepal, and tulsi. The CNN model has an accuracy of 97.14% and a loss of 0.1274, the ResNet50 model has an accuracy of 97.06% and a loss of 0.1474, and the InceptionV3 model has an accuracy of 100% and a loss of 0.0199 in identifying the medicinal plant species. © 2024 IEEE.

关键词： Medicinal chemistry

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Digital Twin Bridging: Enabling Virtual Worlds to Manifest in the Physical 35

Digital Twin Bridging: Enabling Virtual Worlds to Manifest i...

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35th Irish Systems and Signals Conference, ISSC 2024

作者： Dooley, Adam Penica, Mihai McGrath, Sean O'Brien, William Bhattacharya, Mangolika O'Connell, Eoin Dept. of Electronic and Computer Engineering University of Limerick Limerick Ireland School of Information Technology Illinois State University IL United States

ISBN: (纸本)9798350352986

Virtual Reality (VR) technology is a powerful tool in the ongoing quest to address global challenges. VR's immersive and interactive nature enables more engaging and effective solutions for addressing real-world technical challenges and improving the overall human experience. It offers an innovative approach to simulate real-world scenarios within a virtual realm. The work being presented bridges the gap between the physical and the virtual by crafting a virtual environment that empowers users to interact in real time with critical real-world issues through the tool of virtual reality. The exhibited work offers more than just technological innovation;it immerses users in an intricately replicated virtual environment that seamlessly mirrors an actual building allowing the user to interact within the virtual domain and control the physical domain. Through this convergence of technology and reality, a glimpse of the future where advancing VR technologies can open doors (physically and virtually) delivering novel methods of interacting with and managing our surroundings. © 2024 IEEE.

关键词： Virtual reality

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A Novel Approach to Data Security in Cloud Storage using Erasure Coding and Re-Encryption 8

A Novel Approach to Data Security in Cloud Storage using Era...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Inakollu, Asritha Kranthi, S. Jashua, A. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350376425

The widespread adoption of cloud storage systems raises concerns regarding data confidentiality, particularly when data is stored on external clouds. Encryption techniques can protect data privacy but often impose limitations on storage system functionality, especially in decentralized distributed systems. This research study proposes a novel solution to address these challenges by combining a re-encryption algorithm with decentralized erasure coding. The proposed system enables users to forward their data to others without requiring decryption, ensuring reliable and secure data storage and retrieval. The primary technical contribution of this research lies in the development of a re-encryption system that facilitates both encryption and forwarding operations over encrypted and decrypted communications using the Blowfish algorithm. This innovative approach enhances data confidentiality and flexibility in decentralized cloud storage environments. By addressing the limitations of existing encryption techniques, this research contributes to the development of more secure and efficient cloud storage solutions, providing users with greater control over their data and ensuring its privacy and integrity. © 2024 IEEE.

关键词： Cloud storage

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