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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Tactile Displays Driven by Projected Light

arXiv

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arXiv 2024年

作者： Linnander, Max Goetz, Dustin Reardon, Gregory Kumar, Vijay Hawkes, Elliot Visell, Yon Department of Mechanical Engineering United States Department of Bioengineering United States Media Arts & Technology Program United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Tactile displays that lend tangible form to digital content could transform computing interactions. However, achieving the resolution, speed, and dynamic range needed for perceptual fidelity remains challenging. We present a tactile display that directly converts projected light into visible tactile patterns via a photomechanical surface populated with millimeter-scale optotactile pixels. The pixels transduce incident light into mechanical displacements through photostimulated thermal gas expansion, yielding millimeter scale displacements with response times of 2 to 100 milliseconds. Employing projected light for power transmission and addressing renders these displays highly scalable. We demonstrate optically driven displays with up to 1,511 addressable pixels – several times more pixels than any prior tactile display attaining comparable performance. Perceptual studies confirm that these displays can reproduce diverse spatiotemporal tactile patterns with high fidelity. This research establishes a foundation for practical, versatile high-resolution tactile displays driven by light. Copyright © 2024, The Authors. All rights reserved.

关键词： Light transmission

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students 38

Ideation of Engineering Solutions for Sustainable Developmen...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Krutphong, Kodchakorn Leelawat, Natt Tang, Jing Ota, Eri Suzuki, Rie Murakami Taoka, Yuki Punyabukkana, Proadpran Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Thailand Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand The Learning Innovation Center Chulalongkorn University Thailand

ISBN: (纸本)9798350302196

A collaborative course called Global Awareness for technology Implementation was conducted by the Faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation. © 2023 IEEE.

关键词： Students

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Analysis of Partial Discharges in Oil-Impregnated Transformer Paper Insulation and PET-G Insulation

IEEE Open Journal of Instrumentation and Measurement

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IEEE Open Journal of Instrumentation and Measurement 2025年 4卷

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz AlShaikh Saleh, Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Olesz, Marek Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering HatfieldAL10 9EU United Kingdom Hamad Bin Khalifa University Qatar Foundation Electrical and Computer Engineering Program in College of Science and Engineering Doha Qatar Gdańsk University of Technology Department of Electrical Power Engineering Gdańsk80-233 Poland

Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteristics of those damaging, PD-causing cavities must be well understood, to accelerate the development of advanced PD detection techniques. Thus, the impact of varying the radius and position of spherical air voids on the characteristics of PDs in PTs is examined using a 3-D finite element analysis (FEA) model designed in COMSOL Multiphysics. The spherical voids are positioned between two windings of a 512 kV, three-phase (3φ ) PT. The peak electric field (EF) and aggregate energy in the FEA model are used in conjunction with laboratory measurements of the apparent discharge magnitude, for detailed analysis of Polyethylene Terephthalate Glycol (PET-G)-based cylindrical voids with different heights. Simulations demonstrate that the inception of PD activity in the PT model occurs for spherical voids with a radius exceeding 1 mm. Furthermore, the most severe PDs occur within the press-board insulation, adjacent to the uppermost part of the innermost windings. Experiments demonstrate that a significant increase in PD activity was observed for PET-G-based cylindrical voids with heights exceeding 1 mm. © 2022 IEEE.

关键词： Partial discharges

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Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

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computer Networks 2024年 253卷

作者： Tu, Yi-Hao Lin, En-Cheng Ke, Chih-Heng Ma, Yi-Wei Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Master Program of Information Technology and Applications National Quemoy University Kinmen892009 Taiwan Department of Computer Science of Information Engineering National Quemoy University Kinmen892009 Taiwan

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions. © 2024

关键词： Wireless local area networks (WLAN)

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Integration of Artificial Intelligence in Software Defined Networking technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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Development of a Phase-Locked Loop Circuit in Running Frequency Mode for Capacitance-to-Voltage Conversion

Development of a Phase-Locked Loop Circuit in Running Freque...

引用

International Electrical engineering Congress (iEECON)

作者： Tada On-khong Noppadon Sisuk Paramote Wardkein Ratchanoo Katman Kriangsak Prompak Department of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program in Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand Department of Telecommunication School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Program of Physics Faculty of Science and Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Capacitance-to-voltage conversion is essential for capacitive sensors in various industries, including touch interfaces, medical devices, and moisture measurement. However, circuit design faces challenges like non-linearity, noise, and small capacitance variations affecting voltage signals. This article proposes a phase-locked loop in free-running oscillator mode with a frequency-to-voltage converter to enhance accuracy and stability. Test results show a 97.9% measurement accuracy, demonstrating reduced noise and improved stability. The proposed circuit is ideal for high-precision applications in diverse environments, overcoming limitations of traditional methods.

关键词： Accuracy Sensitivity Moisture measurement Noise Moisture Capacitance Frequency conversion Circuit stability Phase locked loops Standards

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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