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Robust optimization of power system stabilizer parameters based on pole placement 18

Robust optimization of power system stabilizer parameters ba...

18th International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Panyarat, Thanon Uatrongjit, Sermsak Chiang Mai University Faculty of Engineering Master Degree Program in Electrical Engineering Thailand Electrical Generating Authority of Thailand Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Thailand

ISBN: (纸本)9780738111278

This paper presents a new approach based on pole-placement technique for robust optimization of power system stabilizer (PSS) parameters. The parameters design is formulated as an optimization problem aimed for adjusting all system poles to be in the specific region of s-plane. It is then solved by using the genetic algorithm. The proposed method has been implemented in MATLAB environment and applied to the single machine infinite bus (SMIB) model. The preliminary results from simulation show that the proposed parameter optimization technique can further enhance the power oscillation damping compared to the conventional approach. © 2021 IEEE.

关键词： Genetic algorithms

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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Identification of User's Interests by Deep-Learning Based Pose Estimation with Angle Relationship from Keypoints for Smart Advertising Displays

Identification of User's Interests by Deep-Learning Based Po...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yu-En Lee Chih-Peng Fan Master Program in Semiconductor and Green Technology National Chung Hsing University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

In this paper, a deep-learning based pose estimation technology for recognizing user's interests on interactive advertising displays is developed by leveraging the YOLO-Pose architecture. The primary goal is to predict user's attention areas by analyzing angular relationships with human body poses. The methodology uses the YOLO-Pose model to identify 17 keypoints on the human body and the angles among the selected body keypoints are calculated. The proposed design incorporates the left and right hand identification and the height determination to address accuracy issues from variations in user's height or body size. By integrating the estimation data for angles, user's height, and handedness, the horizontal and vertical SVM classifiers will be pre-trained. After the SVM classifiers are well trained, the combination results from both SVM classifiers enables the prediction of the user's area of interests. Experimental results with four, six, and eight grid divisions on the display reveals that the average identifying accuracies for horizontal and vertical grid classification can be up to 94% and 83%, respectively. By height adjusted information, the horizontal and vertical classification accuracies of the SVM classifiers can improve by 11% and 20% respectively within corresponding height ranges. For the embedded device implementation, the YOLO model and SVM classifier are deployed on the Jetson AGX Orin platform to achieve an average processing speed of 15 frames per second.

关键词： Support vector machines YOLO Accuracy Feature detection Pose estimation Classification algorithms Advertising

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study

TechRxiv

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TechRxiv 2024年

作者： Muharram, Arief Purnama Taufikulhakim, Farhan Hilmi Purwarianti, Ayu Faculty of Medicine Universitas Indonesia Central Jakarta Indonesia Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development. © 2024, CC BY.

关键词： COVID-19

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A Data Encryption System Utilizing Physical Unclonable Functions, Pseudorandom Functions, and Ascon

A Data Encryption System Utilizing Physical Unclonable Funct...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yian Chen Yichiao Chen Hongzhe Chen Yeong-Kang Lai Master Program in Semiconductor and Green Technology National Chung Hsing University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a data encryption transmission system designed to achieve high security, integrating Physical Unclonable Functions (PUF), Pseudo-Random Functions (PRF), and the Ascon encryption standard. The system utilizes Weak PUF for key generation, based on the unique physical characteristics of devices to produce unpredictable and device-specific responses. Furthermore, the system employs a PRF designed based on PUF characteristics to generate Nonce, further enhancing the security of the communication process. The choice of Ascon encryption mode is based on its high efficiency and low resource consumption, making it an ideal option for maintaining high security while being suitable for resource-constrained devices.

关键词： Resistance Nonce Side-channel attacks Physical unclonable function Encryption Security System analysis and design

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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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A Reconfigurable Compute-In-Memory Design with Adaptive Bit-Precision and Power Adjustment

A Reconfigurable Compute-In-Memory Design with Adaptive Bit-...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： I-Chieh Hsu Chun-Fu Chen Dao-Long Huang Yun-Kai Yeh Wei-Chieh Lee Qi-Hua Lin Jinjia Zhou I-Chyn Wey Electrical Engineering Department College of Engineering Chang-Gung University Taiwan School of Electrical Engineering National Taiwan University of Science and Technology Taiwan Master Program in Nano-Electronic Engineering and Design Chang Gung University Taiwan Department of Applied Informatics Faculty of Science and Engineering Hosei University Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

With the successful development of artificial intelligence (AI), Convolutional Neural Networks (CNNs) occupy a large amount of computing time and power consumption in the entire AI computing process. However, in traditional von Neumann architectures, the separation of the computing element and memory leads to the memory-wall problem of memory data transmission bandwidth from memory to processing element when executing multiply-accumulate-based CNN operations. The data transmission time and power consumption all are much higher than the CNN computing part. Therefore, this paper proposes a memory-in-computation design that can flexibly adjust energy usage, achieve high energy efficiency, and support multiple operation frequencies. By controlling the switches of each memory row, unnecessary energy consumption is avoided, leading to a reduction in total power consumption by 15% to 60%. Additionally, by adjusting the pulse width, the charging power consumption of capacitors is reduced by 65% per charge cycle.

关键词： Energy consumption Power demand Capacitors Bandwidth In-memory computing Control systems Energy efficiency Convolutional neural networks Data communication Artificial intelligence

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys Computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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Security and Privacy Relate Future Aspects of Artificial Intelligence-Powered Electric Vehicle Technology

Security and Privacy Relate Future Aspects of Artificial Int...

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

作者： Tole Sutikno Anggit Pamungkas Budi Santosa Riky Dwi Puriyanto Ahmad Raditya Cahya Baswara Master Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Embedded System and Power Electrinics Research Group (ESPERG) Yogyakarta Indonesia Automotive Technology of Vocational Education Universitas Ahmad Dahlan Yogyakarta Indonesia Undergraduate Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Artificial Intelligence (AI) and electric vehicles EVs are revolutionizing the future of transportation, automation, and connectivity. The integration of AI in EV technology heralds a transformative era in transportation, characterized by enhanced efficiency, autonomy, and user experience. However, as AI-powered EVs become increasingly prevalent, critical security and privacy challenges emerge that necessitate comprehensive scrutiny. This paper explores the future aspects of security and privacy in AI-powered EVs, highlighting potential vulnerabilities and the implications of data exploitation. Key areas of focus include the cybersecurity threats posed by interconnected vehicle networks, the safeguarding of personal data from unauthorized access, and the development of robust encryption methods to protect vehicular communication systems. Additionally, the paper examines regulatory frameworks and industry standards essential for ensuring the secure deployment of AI in EVs. By addressing these concerns, the research aims to provide a holistic understanding of the measures required to fortify the security and privacy of AI-driven electric vehicles, ultimately contributing to developing a safe and trustworthy transportation ecosystem.

关键词： Data privacy Privacy Protocols Ecosystems Transportation Electric vehicles Encryption Security Autonomous vehicles Information integrity

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Brainwave Analysis of the Correlation Between Listening to the Rain/Water Sounds and Reducing Human-Perceived Temperature 18th

Brainwave Analysis of the Correlation Between Listening to t...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Tey, Fu Jie Wu, Tin-Yu Liu, Mei-Yin Chen, Yi-Kai Department of Electrical Engineering National Taiwan University of Science and Technology Taipei City Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Neipu Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan City Taiwan

ISBN: (纸本)9789819901043

This study focuses on the brainwave analysis of the correlation between listening to the rain and water sounds and reducing human-perceived temperature. Brainwave changes are monitored when experimental subjects listen to rain and water sounds at various frequencies. The experiment result shows that listening to the sounds of rain and water in an environment of high temperature and high humidity can boost α waves to induce feelings of calm and relaxed mental state. In order to investigate subjects’ psychological and physiological changes induced by brainwave changes while listening to rain and water sounds for different time durations in a high temperature and humidity environment, this study uses the Fourier transform to obtain the spectra of natural rain/water sounds and analyzes the sound frequencies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fourier analysis

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