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A Study on the Efficient ESS Charging/Discharging Operation Algorithm in LVDC Microgrid Environment

Transactions of the Korean Institute of Electrical Engineers 2024年第6期73卷 1063-1073页

作者： Jung, Sang-Woo An, Yoon-Young Kim, Ki-Il Dept. of Computer Science and Engineering Chungnam National University Korea Republic of ICT Convergence Standards Research Division Electronics and Telecommunications Research Institute Korea Republic of

Modern power systems are developing in a sustainable and energy-efficient direction, and research on intelligent power grids that accommodate new and renewable energy is being actively conducted. Since new and renewable energy-based distributed power generation systems and ESS are output as DC power, the DC microgrid reduces the power conversion step and improves the stability and efficienc of the power distribution network. The core of this study is to compare and analyze the performance of the three charge/discharge operation algorithms based on AI prediction in the LVDC microgrid environment. The AI server learns real-time data through interworking with the EMS to predict future load usage and solar power generation, and performs the charge/discharge operation algorithm of ESS based on the collected ESS state data. This paper presents the configuration of the LVDC microgrid testbed, the integration method between EMS and electrical facilities, and analyzes the performance of the developed ESS charge/discharge operation algorithm. It also evaluates the effectiveness of each operation algorithm through actual operation data and simulation, analyzes how these strategies contribute to system load reduction and energy efficiency improvement, and provides guidelines for selecting the optimal ESS charge/discharge operation algorithm under specific conditions. © The Korean Institute of Electrical Engineers.

关键词： Solar energy

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Source Free Domain Adaptation with Pseudo-Labeling Quality Assessed by Sam in Fundus Image Segmentation 22

Source Free Domain Adaptation with Pseudo-Labeling Quality A...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yaacovi, Bar Goldberger, Jacob Tel-Aviv University Computer Science Dept. Israel Bar-Ilan University Faculty of Engineering Israel

ISBN: (纸本)9798331520526

Precise segmentation of the optic cup and optic disc in fundus images is crucial for measuring the cup-to-disc ratio, a key indicator for glaucoma screening and diagnosis. When applying a network trained on data from one medical site to data from another site, performance declines due to domain shift problems. This study centers on the realistic scenario of source-free unsupervised domain adaptation. We propose a new pseudo-labeling strategy that takes advantage of the capabilities of the SAM segmentation foundation model by using it to assess the quality of the pseudo-label segmentation of the target images created by the source model. We implemented our method on publicly available retinal segmentation datasets collected from several medical sites and reported improved performance compared to previous methods. © 2025 IEEE.

关键词： Image segmentation

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Machine Learning-Based Estimation of Dynamic Travel Times Between Points of Interest

Machine Learning-Based Estimation of Dynamic Travel Times Be...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Kaji, Shoma Kajimoto, Dai Noguchi, Tatsuya Takayama, Toshinori Bao, Siya Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan ZENRIN DataCom Tokyo Japan

ISBN: (纸本)9798331521165

Due to the dynamic traffic conditions, effective estimation of travel times is crucial for the real-world travel planning service. Although APIs provide accurate travel information, the high response time poses a limitation. This paper proposes a machine learning-based method using XGBoost to predict dynamic travel times between Points of Interests (POIs). By utilizing features including latitude and longitude of locations, route direction, and departure time, the model achieves high prediction accuracy. We conducted hyper-parameter tuning using Optuna and evaluated the proposed method with POIs in Kyoto. The proposed method achieved a low RMSPE of −3s. © 2025 IEEE.

关键词： Travel time

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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

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2025 International Conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Petricia, V. Angel Jacksy, M. Bhavadharani, R. St. Joseph's Institute of Technology Dept. of Computer Science and Engineering Chennai119 India

ISBN: (纸本)9798331521394

Autism Spectrum Disorder(ASD) is a complex neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Early detection is critical to enable timely intervention and improve outcomes for individuals with ASD. This research proposes an innovative framework that integrates Convolutional Neural Networks (CNNs) with advanced data preprocessing techniques, facial imaging, and behavioral analysis to enhance the accuracy and efficiency of ASD diagnosis. Leveraging data augmentation and transfer learning, the model achieves robust performance with high sensitivity and specificity. The novelty of this approach lies in its ability to identify subtle facial and behavioral patterns indicative of ASD, using a multidisciplinary strategy that combines neuroimaging, machine learning, and real-time diagnostic feedback. By advancing beyond traditional methods, this comprehensive framework holds the potential to revolutionize early ASD detection, making it more accessible, scalable, and precise. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Bhuvaneswari, S. Abirami, A. Suvetha Sri, R.P. Srinithy, R. Swathi, R. Easwari Engineering College Faculty of Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665492942

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues. © 2023 IEEE.

关键词： Packet networks

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction with a Tkinter-Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kasetty, Sai Bhargav Margret, Issac Neha Madhuri, K. Sai Ojha, Rajesh Kumar Sudha, Nuthalapati Rajakumar, K. V.I.T University SCOPE Tamilnadu Vellore India Data Science BVRIT Narsapur Dept. of CSE Telangana Hyderabad India Silicon University Dept. of Computer Science & Engineering Odisha Bhubaneswar India Mahindra University School of Engineering Telangana Hyderabad India

ISBN: (纸本)9798350369175

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience. © 2024 IEEE.

关键词： Prediction models

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Early Brain Tumour Detection in MRI Images Through Hybrid Deep Learning Model 3

Early Brain Tumour Detection in MRI Images Through Hybrid De...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Parida, Samrat Aditya Keshari Ashirbad, S.B. Maharana, Mangal Ghadai, Debasis Kumar De, Sumane C.V Raman Global University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331533205

Brain tumours are among the emerging significant health challenges that require early detection for complete management. Though the mainstay of diagnosis remains MRI, it can be applied in its interpretation with some challenges. This study investigates the potential of leveraging Convolutional Neural Networks, a form of artificial intelligence, to classify different brain tumours from MRI images with enhanced metrics. CNNs are said to provide the best currently available solution in achieving minimal preprocessing followed by feature engineering especially with respect to image analysis. Accuracy and efficiency in brain tumour diagnosis can be drastically improved by leveraging the power of AI. © 2025 IEEE.

关键词： Convolutional neural networks

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Empowering Android Malware Detection: A Deep Learning Ensemble with Optimal Features 26

Empowering Android Malware Detection: A Deep Learning Ensemb...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Shakil, Mehedi Hasan Hasan, Md. Mynul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798350359015

Because of the rapid expansion of mobile devices and the growing reliance on mobile applications, there has been an increase in Android malware threats, necessitating the development of sophisticated detection systems. This research proposes a technique for detecting Android malware that makes use of several deep learning models. The goal is to provide an accurate and dependable method for classifying mobile applications as malicious or benign. To assure data integrity, the Drebin dataset is preprocessed. RFECV was employed to obtain the optimal features for deep learning algorithms such as Bi-LSTM, Bi-GRU, and 1D CNN. The dataset was trained using the selected features for each deep learning algorithm based on the feature selection method. The ensemble technique, employing a weighted average method, was utilised to combine the predictions of the individual models. Extensive testing revealed that combining Bi-LSTM and CNN in the ensemble technique achieves the greatest accuracy of 98.99%. This combination highlights the efficiency of combining various models strengths for Android malware detection. © 2023 IEEE.

关键词： Bi-Directional Gated Recurrent Unit (Bi-GRU) Bi-Directional Long Short Term Memory (Bi-LSTM) Convolutional Neural Network (CNN) Recursive Feature Elimination with Cross-Validation (RFECV) weighted average ensemble

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Recent Advancements and Challenges in Bangla Textual Document Classification: A Review 26

Recent Advancements and Challenges in Bangla Textual Documen...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Nahar, Khairun Al Mahfuz, Mohammed Abdullah Haque Sweety, Mahmuda Comilla University Faculty of Engineering Dept. of Computer Science and Engineering Cumilla Bangladesh

ISBN: (纸本)9798350359015

Bengali is the world's sixth-most frequently used dialect. Natural language processing (NLP), which enables data scientists to extract useful information from the constantly growing quantity of text data available on the net, includes text categorization as a key component. It is the act of organizing already-created groups or classes of text documents. But compared to other languages, like English, Bengali does not have as many capabilities for classifying texts. This study offers an extensive analysis of the current categorization of Bengali documents. Also gives a comprehensive summary of recent advances in ML and DL, covering datasets, methods, performance, accuracy rate, classification category, strengths, and limits. The purpose of this discussion is to identify current challenges that must be addressed and recommend some guidelines for future studies that might be explored. © 2023 IEEE.

关键词： Bangla Language Bengali text classification Classification Document classification Natural Language processing Text categorization

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