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Comparative Study of ML Algorithms for Load Redistribution Attack Detection 1

Comparative Study of ML Algorithms for Load Redistribution A...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Verma, Praveen Dasgupta, Pallab Chakraborty, Chandan School of Energy Science & Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Dept. of Computer Science & Engineering Kharagpur India Indian Institute of Technology Dept. of Electrical Engineering Kharagpur India

ISBN: (纸本)9798350398069

A Load Redistribution (LR) attack is a special type of False Data Injection (FDI) attack on the power grid in which the attacker manipulates the load request. This manipulation results in false dispatch decisions by the grid operator, which causes congestion/overflow in the lines and uneconomic operation of the grid. The drawbacks of conventional detection mechanisms have motivated researchers to explore data-driven mechanisms employing Machine Learning (ML) techniques. This paper presents a comparative study of the performance of 6 different ML algorithms for detecting LR attacks on the IEEE 30 bus test system. © 2022 IEEE.

关键词： Cybersecurity

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Critical Load Identification for Load Redistribution Attacks 11

Critical Load Identification for Load Redistribution Attacks

引用

11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Verma, Praveen Gupta, Sagar Dasgupta, Pallab Chakraborty, Chandan School of Energy Science & Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Dept. of Computer Science & Engineering Kharagpur India Indian Institute of Technology Dept. of Electrical Engineering Kharagpur India

ISBN: (纸本)9798350399660

The smart integration of communication and information technology into electrical power grids has significantly benefited the monitoring and operations of the grid, but it has also made it vulnerable to cyber attacks. A Load Redistribution (LR) attack is a type of attack in which the attacker manipulates one or more load requests to create an overflow (congestion) in a transmission line. The cost and limitations of conventional detectors have motivated researchers to explore Machine Learning (ML) and data driven mechanisms for detecting potential LR attacks. The expanding nature of the grid and the rapidly increasing number of loads on a grid necessitates the monitoring of a huge number of points and creates a data deluge for the ML-based detectors, thereby adversely affecting their performance. This paper provides an approach for identifying a subset of loads, namely critical loads, that needs to be monitored for detecting LR attacks. This helps the grid operator to monitor fewer loads thereby reducing the cost of monitoring the loads, the detection time, and the computational burden. The proposed approach is implemented on the IEEE 30 bus system. The results validate the fact that LR attacks can be detected by monitoring only the identified critical loads, without adversely affecting the performance of the ML detectors. © 2022 IEEE.

关键词： Machine learning

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Medium-Term Load Forecasting for The City of Benghazi Using an Artificial Neural Network Based Time Series Approach 3

Medium-Term Load Forecasting for The City of Benghazi Using ...

引用

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2023

作者： Farkash, Hend M. Sahly, Eiman M. El-Werfalli, Aya A. El-Agori, Rabia A. The College of Electrical & Electronic Technology Benghazi Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (纸本)9798350319897

In recent years, an increase in the electricity load at the General Electricity Company of Libya (GECOL) has led to power shortages during the peak summer season. And sometimes there was an acute shortage in the distribution of electricity throughout the country. Due to the interruption of large generation units or transmission lines of the electric power grid. this paper aims to address the issue of electrical load demand forecasting by using the NARX tool on the MATLAB platform to train artificial neural networks (ANN). The forecasting of Benghazi's electricity consumption is the main topic of this paper. In this work, deep learning is used for the first time to forecast overall energy demand and consumption in Benghazi. The real data acquired by the National Control Center GECOL supports the results that were achieved. © 2023 IEEE.

关键词： Electric lines

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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Platform Construction to Protect Portraits of Others

Platform Construction to Protect Portraits of Others

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Cheng, Hui-Jun Zeng, Sheng-Fu Lin, Chun-Yuan Minth University of Science and Technology Dept. of Artificial Intelligence Hsinchu County Taiwan Chang Gung University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan Asia University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350302561

As the times change, online video activities and video sharing have become commonplace. However, publicly sharing the images of others can involve risks of violating privacy and portrait rights, especially in today's era of widespread high-resolution cameras that can capture detailed facial features. Therefore, protecting the portrait rights of others has become crucial. In order to balance the protection of portrait and the preservation of image quality, we propose a deep learning-based person tracking system. This system can record and track the time and location of people in videos or video streams. Through image synthesis techniques, we can either place the necessary people in virtual environments or hide people in real videos. In this way, we ensure the portrait and privacy of others while maintaining the authenticity and quality of the image. This approach is not only applicable for individuals protecting their own images but can also be applied to respect the privacy of others, making online video activities safer and more respectful of people rights. © 2023 IEEE.

关键词： Deep learning

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Classification of Plant Diseases using DenseNet 121 Transfer Learning Model 2

Classification of Plant Diseases using DenseNet 121 Transfer...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Pillai, Rudresh Sharma, Neha Gupta, Rupesh Sharma, Avinash Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Dept. of Cse Punjab India

ISBN: (纸本)9798350315905

A significant risk to agricultural output and food security is posed by plant diseases. Plant diseases may be identified and diagnosed using traditional methods, but these procedures can be laborious and complicated. As the process is complicated, it is easy for a plant disease to be misdiagnosed which can lead to further damage to the plant. Thus, to overcome the problems faced by traditional methods, diagnosing a plant disease in early stages, and increase the reliability of the diagnosis obtained, deep learning can be used for classification of plant diseases. This paper aims to increase agricultural productivity and ensure food security by preventing crops from being affected by plant diseases through timely diagnosis and accurate classification. This would further develop the goal of sustainable food production in the world. In this paper, the deep transfer learning model DenseNet121 is used to detect and classify plant diseases for pepper bell, potato, and tomato plants with multiple fine-tuning layers for improved and refined results. An accuracy of 97.38% was achieved with the model proving that deep learning methods can be used for the effective diagnosis and classification of plant diseases. © 2023 IEEE.

关键词： Agriculture

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ChatGPT: Information Retrieval from Image using Robotic Process Automation and OCR 7

ChatGPT: Information Retrieval from Image using Robotic Proc...

引用

7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Shidaganti, Ganeshayya Sanjana, R. Shubeeksh, K. Monish Raman, V.R. Thakshith, Valmeeki M.S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350397253

ChatGPT hit 1M users in just 5 days once it went public, the leading companies such as Facebook, Instagram, Spotify, Netflix took several months to years to achieve this mark. As a result of the conversational AI Chabot being hailed as a technological revolution, ChatGPT has emerged as one of the year's most popular buzzwords. This research study intends to initiate interaction with ChatGPT more convenient to users by using user sent WhatsApp images containing text as input by converting it into text-based prompt which is accepted by ChatGPT and provide the user with the response generated by ChatGPT. This is achieved by integrating RPA to automate an in-dept. examination of images for information discovery using OCR to extract text and ChatGpt to retrieve information. This research study also focuses on providing a quick response to the user by comparing images with previous users' inputs and sending the same response in case there is repetition of images which increases efficiency in terms of computation resources too. The proposed solution is designed to provide a quick suitable response to the user inscribed text in the image. © 2023 IEEE.

关键词： Robotics

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A Multimodal Approach to Detect Driver Drowsiness 5

A Multimodal Approach to Detect Driver Drowsiness

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Gowda, Mayur S Talasila, Viswanath Raafay Umar, S.A. Alva, Rashmi Carnegie Mellon University Dept. of Electrical and Computer Engineering Pittsburgh United States Ramaiah Institute of Technology Dept. of Electronics and TeleCommunication Engg. Bangalore India Centre for Imaging Technologies Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9798331528539

Driver fatigue, manifesting as drowsiness and loss of concentration, poses a significant risk to road safety. We present a novel real-time approach for detecting driver fatigue by combining feature recognition, head pose estimation, and eye gaze tracking. Utilizing 3D landmarks from the MediaPipe framework, our method provides a comprehensive assessment of driver alertness in both real-world and controlled environments. Our system leverages the YOLOv5 algorithm for feature recognition to extract crucial driver cues and employs advanced 3D facial landmarks from MediaPipe for precise head and eye position estimation. This multimodal approach effectively evaluates driver concentration and alertness, focusing on critical fatigue indicators such as drowsiness and yawning. The face mesh, with 468 facial landmarks, calculates head position, eye position, and blink patterns to accurately identify signs of sleepiness. A total of 17 subjects participated in the data collection, driving the car under various lighting conditions and 13 subjects for in lab data collection under different lighting conditions. This diverse dataset was instrumental in training and testing the model, ensuring its robustness and adaptability to real-world scenarios. By integrating these techniques, our approach offers robust detection of driver fatigue and concentration levels, enhancing road safety. © 2024 IEEE.

关键词： Fatigue testing

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Mechanical Evaluation of Bone Scaffolds' Behavior under Compression 46

Mechanical Evaluation of Bone Scaffolds' Behavior under Comp...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ntousi, Ourania A. Pleouras, Dimitrios S. Siogkas, Panagiotis K. Roumpi, Maria Kontaxis, Lykourgos C. Bourantas, George Deligianni, Despoina Kostopoulos, Vassilis Pakos, Emilios E. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR45110 Greece University of Patras Laboratory of Biomechanics and Biomedical Engineering Dept. of Mechanical Engineering and Aeronautics RionGR26500 Greece University of Patras Dept of Agriculture MessolonghiGR30200 Greece University of Patras Dept. of Mechanical Engineering and Aeronautics Rio Achaia26504 Greece University of Ioannina School of Medicine Department of Orthopaedic Surgery and Traumatology Epirus Ioannina45221 Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Biomedical Research Institute-FORTH GR45110 Greece

ISBN: (纸本)9798350371499

This study aims to comprehensively evaluate the mechanical performance of bone scaffolds, focusing on a compression test displacement rate 0.5mm/s and 2mm/min. The research employs the Yeoh 3rd order model for the hyperelastic PLA scaffolds to simulate the compression tests, which are validated using experimental results. More specifically, the stress- strain curves derived from the in-silico analysis exhibit strong agreement with the experimental results, highlighting the reliability of the computational approach. Two distinct scaffold geometries were employed and a comparative analysis of their responses to specific loading conditions had been performed. The investigation reveals that the PCL-50 scaffold demonstrates promising mechanical behavior in terms of better endurance in such loading conditions. © 2024 IEEE.

关键词： Stress-strain curves

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Adaptive and Automatic Illuminance Control of Semi-Open Historic Underground Sites Using Smart Glass Based on Digital Twins and 3D Reconstruction

Adaptive and Automatic Illuminance Control of Semi-Open Hist...

引用

2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Zhang, Jiaying Fu Chan, Jeff Chak Kwok, Helen H.L. Cheng, Jack C.P. Dept. of Civil and Environmental Engineering Hong Kong Univ. of Science and Technology Hong Kong Institute for the Environment Hong Kong Univ. of Science and Technology Hong Kong

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

The entrance of the semi-open underground site is susceptible to sunlight, resulting in a significant green biological infestation that compromises the aesthetics and structural integrity of the historical site. The glass shed at the entrance of site provides light transmission and rain preservation. Traditional glass cannot simultaneously transmit and reflect light. To address this issue, a novel intelligent glass control system based on digital twin technology was developed. This system effectively regulates the illuminance within the prescribed range, ensuring the preservation of semi-open underground monuments. However, previous reconstruction methods struggle with non-homogeneous and complex morphologies. An improved 3D Gaussian Splatting algorithm-based method was proposed for fast and accurate 3D reconstruction, enhancing mesh completeness for numerical simulations. Furthermore, the digital twin-based smart glass system offers natural lighting while inhibiting plant growth, facilitating automatic, adaptive lighting control for heritage sites. The system's effectiveness was illustrated in the former Sham Shui Po Service Reservoir. © ASCE.

关键词： Light transmission

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