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检索条件"机构=Program in electrical engineering and electronics"

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Battery Energy Storage System Supporting Frequency Regulation of Power System with High Renewable Penetration

Battery Energy Storage System Supporting Frequency Regulatio...

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Power, Energy and electrical engineering (CPEEE), International Conference on

作者： Nomvelo Nzimande Yasuhiro Noro Electrical Engineering and Electronics Program Kogakuin University Tokyo Japan Department of Electrical and Electronic Engineering Kogakuin University Tokyo Japan

The utilization of renewable energy (RE) has increased worldwide because it is one of the effective ways to minimize greenhouse gases. The power outputs of photovoltaic (PV) and wind farms depend on weather conditions. High penetration of intermittent renewable energy (IRE) will put pressure on frequency regulation, and frequency fluctuation will increase because IRE does not participate in frequency regulation. This study evaluated the role of battery energy storage systems (BESS) in the frequency regulation of a grid with large-scale IRE. The simulations were conducted in the MATLAB–Simulink environment, taking into consideration the operating conditions of the Republic of South Africa (RSA) power system, such as frequency deviation limits. Eskom’s (the RSA utility company) policy requires its operators to keep the system frequency between 49.85 Hz and 50.15 Hz 95% of the time. The BESS was integrated into the load frequency control (LFC) model to assist with frequency regulation and maintain the frequency between 49.85 Hz and 50.15 Hz.

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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PV and Battery Sizing and Cost Study for Small Remote Islands Considering Load Demand Peak Shift

PV and Battery Sizing and Cost Study for Small Remote Island...

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Power, Energy and electrical engineering (CPEEE), International Conference on

作者： Clarence Kitalong Yasuhiro Noro Electrical Engineering and Electronics Program Kogakuin University Tokyo Japan Department of Electrical and Electronic Engineering Kogakuin University Tokyo Japan

This paper discusses energy and cost comparison for 9 different combinations of Photovoltaic (PV) and Lithium-Ion Battery Energy Storage System (BESS) sizes with load demand peak changes for the Kayangel Power System (KPS). 9 PV and BESS combinations were modeled with the existing 90kW Diesel Generator (DG) and simulated while analyzing the kWh energy values of DG operation, PV curtailment and battery power flow energy values. A comparative cost analysis was then conducted on these 3 energies using Installation Costs (IC), diesel fuel costs and energy charge costs. Following this, the study was expanded to a 20-year time period for more complete system lifetime cost estimations. Simulated energy losses and system costs showed mostly favorable values during increased PV and BESS sizes and shifted load demand peak. Simulation results also identified combinations where increased sizing and shifted load demand peak made little difference.

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CARE HIVE: A Scalable IoT Platform for Collaborative Air Quality Monitoring and Research

CARE HIVE: A Scalable IoT Platform for Collaborative Air Qua...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： John Jairus D.P. Eslit Neil Astrologo Aldon Cris Galido Jomari Ganhinhin Percival Magpantay Marc D. Rosales John Richard E. Hizon Center for Air Research in Urban Environments (CARE) Program Electrical and Electronics Engineering Institute University of the Philippines Diliman

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

CARE HIVE is an Internet of Things (IoT) plat-form created to support efforts in air quality monitoring and research. It uses IoT technology to gather and analyze real-time air quality data. The platform's microservices architecture makes it modular, scalable, and flexible. It uses Docker and Kubernetes for containerization, ensuring consistency across different environments. CARE HIVE consists of data sources, an MQTT broker, data ingestion services, a data lake, a REST API service, data processing services, and user applications. It features an automated workflow and automatic scaling capabilities to efficiently manage resources and adapt to varying demands. By providing open access to air quality data, CARE HIVE aims to enhance public health, boost environmental awareness, and aid informed policy-making in the Philippines and beyond.

关键词： Soft sensors Scalability Microservice architectures Collaboration Water quality Air quality Data processing

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SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm 12

SoC-Oriented Implementation of Machine Learning Based Breast...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Saeed, Abdelrahman Tawfik, Ayman Mostafa, Hassan Khalil, Ahmed Hussein Cairo University Electronics and Communications Engineering Department Giza12613 Egypt Ajman University Department of Electrical and Computer Engineering United Arab Emirates University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza Egypt

ISBN: (纸本)9798350322910

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU). © 2023 IEEE.

关键词： Convolutional neural networks

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Darknet Traffic Classification with Machine Learning Algorithms and SMOTE Method 7

Darknet Traffic Classification with Machine Learning Algorit...

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7th International Conference on Computer Science and engineering, UBMK 2022

作者： Karagol, Hasan Erdem, Oguzhan Akbas, Barkin Soylu, Tuncay Trakya University Department of Electrical and Electronics Engineering Edirne Turkey University of Health Sciences Department of Occupational Health and Safety Program Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

The Darknet is a network that can be accessed with certain privileges and runs a non-standard communication protocol. The Darknet traffic that consists of data from several known networks such as Tor and the P2P is often used for criminal activities due to its anonymity. It is so critical to correctly classify Darknet traffic to differentiate the individual flows for security purposes. In this paper, we proposed three different machine learning (ML) based traffic classification approaches;the binary classification of Darknet and Benign traffic classes (Case 1);the quadruple classification of classes Tor, NonTor, VPN, and NonVpn (Case 2);an traffic classification of eight sub-traffic classes (Case 3). We further applied the SMOTE method for balancing the sizes of the classes in the traffic dataset and feature selection (FS) algorithms to identify the most effective attributes where the number of features in the original dataset were reduced from 63 to 8, 8 and 6 for Case 1, 2 and 3 respectively. For all three cases, classification was performed with six different machine learning algorithms with and without SMOTE, and the highest accuracy values were obtained with SMOTE method. The highest accuracy values were obtained with the Random Forest Algorithm as 97.22%, 97.16% and 85.99% for Case 1, 2 and 3, respectively. © 2022 IEEE.

关键词： Decision trees

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Internet of Things-Based Early Forest Fire Detection System with *** Platform

Internet of Things-Based Early Forest Fire Detection System ...

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International Conference on electrical engineering, Computer Science and Informatics (EECSI)

作者： Tole Sutikno Son Ali Akbar Tosan Aji Bayusuta Liya Yusrina Sabila P Hendril Satrian Master Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Undergraduate Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Embedded System and Power Electronics Research Group (ESPERG) Yogyakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Forest and land fires in Indonesia are a major concern for the government when entering the dry season. The researchers have widely developed early detection technology, including satellite and airborne-based technology, but it faces several limitations due to the influence of weather and other unavoidable factors. Furthermore, fire detection technology based on wireless sensor networks (WSNs) and the Internet of Things (loT) is one of the most effective solutions. This study developed a sensor node that includes an Arduino UNO, a smoke sensor, a fire sensor, and temperature and humidity sensors and code program using C++, to detect potential forest fires. We place these nodes at fire-prone points in the forest. Additionally, the gateway node receive data from the sensor node, process it using the NodeMCU ESP8266, and send it to the *** database for real-time report. The developed system's test results demonstrate that it can display and send data in real-time to the *** platform via an internet connection. Additionally, the base station's physical 12C 20x4 LCD screen displays the information. The forest fire early warning system functions well, and the test results show good detection and data transmission capabilities.

关键词： Temperature sensors Wireless communication Wireless sensor networks Temperature Transmitters Forestry Logic gates Real-time systems Data communication Internet of Things

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RFID-Based Smart Secure E-Bike Parking Station

RFID-Based Smart Secure E-Bike Parking Station

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International Conference on electrical engineering, Computer Science and Informatics (EECSI)

作者： Tole Sutikno Tri Wahono Haris Imam Karim Fathurrahman Watra Arsadiando Hendril Satrian P Master Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Undergraduate Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Embedded System and Power Electronics Research Group (ESPERG) Yogyakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

This research discusses the safety challenges faced by bicycle smart parking systems, which are often irregular and unsafe, especially in Indonesia. The purpose of this research is to develop an RFID-based smart parking system for electric bicycles that improves user safety and accessibility. The system is designed using components such as the NodeMCU 8266 microcontroller, RFID module, and solenoid lock, and is tested under real-world conditions. Using the $\mathrm{C}++$ programming language to be embedded in a microcontroller. Quantitative results show that the system achieved an 80% success rate in RFID detection on various materials with optimal performance through acrylic barriers. The system demonstrates better safety compared to traditional bicycle parking solutions, which contributes to increased public confidence in the safety of electric bicycles.

关键词： Microcontrollers Urban areas Metals Bicycles Materials reliability Automated parking Safety Solenoids Informatics Radiofrequency identification

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Novel Hybrid Algorithm for Investigation of Dissolved Gas Analysis for Alternate Dielectric Fluids of Power Transformer

Novel Hybrid Algorithm for Investigation of Dissolved Gas An...

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Smart and Sustainable Developments in electrical engineering (SSDEE), IEEE International Conference on

作者： Rahul Soni Raymon Antony Raj Bishal Silwal Rahman Azis Prasojo M.M.F. Darwish Norazhar Abu Bakar Electrical and Electronics Engineering Program Navrachana University Vadodara Gujarat India Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai Erode Tamil Nadu India Department of Electrical Engineering Tribhuvan University Institute of Engineering (Pulchowk Campus) Pulchowk Lalitpur Nepal Department of Electrical Engineering Politeknik Negeri Malang Malang Indonesia Department of Electrical Engineering Faculty of Engineering Shoubra Benha University Cairo Egypt Department of Electrical Engineering Faculty of Engineering Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The power transformer is the most important element of equipment in an electrical transmission and distribution network. The power and voltage levels that these transformers can manage determine their size, and as the transformer's volume increases, internal isolation and cooling become more challenging. The most popular insulating fluid used in transformers for insulation and cooling is mineral-based oil, which has demonstrated exceptional dielectric and thermal qualities. However, alternative oil technologies have been sought to replace mineral-based oils in recent years because to concerns about safety, cost, and environmental impact. In this context, extensive research efforts have emerged on biological oils for use in transformers. This paper examines the sustainability, safety, and electrical performance of bio-based ester oil in transformer applications with respect to dissolved gas analysis (DGA). AI-based Fuzzy logic controller (FLC) with Duval's triangle (DT) and Duval's pentagon (DP), IEC, CIGRE reports are used in a hybrid manner to identify the possible incipient faults inside the oil-immersed transformers. The graphical analysis of various fuzzy logic plots along with DT and DP are utilized to critically analyze the possible incipient faults arrayed due to DGA. The proposed work proficient compared to previously published results. Highest efficiency of DT and DP based FLC (92%), followed by furan analysis (82.3%). Lastly, recommendations are made on potential fixes for the problems raised and the future course of study for alternate dielectric fluids-based power transformers. The suggested techniques in this research offer a higher diagnostic accuracy than the present approach.

关键词： Fuzzy logic Oils Cellulose Oil insulation Dissolved gas analysis Dielectrics Safety Dielectric liquids Sustainable development Power transformer insulation

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Scene Text Recognition Models Explainability Using Local Features

Scene Text Recognition Models Explainability Using Local Fea...

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IEEE International Conference on Image Processing

作者： Mark Vincent Ty Rowel Atienza Electrical and Electronics Engineering Institute University of the Philippines AI Graduate Program University of the Philippines

Explainable AI (XAI) is the study on how humans can be able to understand the cause of a model’s prediction. In this work, the problem of interest is Scene Text Recognition (STR) Explainability, using XAI to understand the cause of an STR model’s prediction. Recent XAI literatures on STR only provide a simple analysis and do not fully explore other XAI methods. In this study, we specifically work on data explainability frameworks, called attribution-based methods, that explains the important parts of an input data in deep learning models. However, integrating them into STR produces inconsistent and ineffective explanations, because they only explain the model in the global context. To solve this problem, we propose a new method, STRExp, to take into consideration the local explanations, i.e. the individual character prediction explanations. This is then benchmarked across different attribution-based methods on different STR datasets and evaluated across different STR models.

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