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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Dang, Nhat Keller, Jason Bajtos, Marek Gurhan, Hakki Barnes, Frank Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder BoulderCO United States Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781946815200

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide (O2-) and calcium ions (Ca2+) concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a 10 μT amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a 45 μT static background field. Effects due to changes in amplitude from 10 to 30 μT at selected frequencies are also investigated with the same superimposed static field. © 2025 USNC-URSI.

关键词： Electromagnetic field effects

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Impact Assessment and Mitigation Strategies for Plug-in Electric Vehicles on Distribution Networks: A Probabilistic Approach 7

Impact Assessment and Mitigation Strategies for Plug-in Elec...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Raj, Vijilius Helena Reddy, M. Siva Swetha Thethi, H. Pal Ramesh, G. Mohammad, Q. Bhavana, Munugapati New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad50043 India Lovely Professional University Phagwara India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The rapid depletion of fossil fuel reserves coupled with the ever-increasing demand for electrical power has prompted the search for new energy sources. Electric vehicles (EVs) are one potential answer because of the technology that allows for more efficient transportation while simultaneously reducing fossil fuel usage and emissions of greenhouse chemicals. Depending on the power system's capability, structure, and architecture, several studies have shown that the distribution network may be negatively affected by the widespread interconnection of electric vehicles. Consequently, it is important to determine the effects of this additional load on the network so that measures can be taken to prevent threats to the system's stability, dependability, and security, especially because the Indian federal and state governments have introduced incentives to increase sales of this type of vehicle. © 2024 IEEE.

关键词： Power distribution networks

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Adoption of Precision Agriculture Process with Wireless Environment Plant Disease Monitoring System using Smartphone 7

Adoption of Precision Agriculture Process with Wireless Envi...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rajabalayanan, S.R. Barkunan, S.R. Nirmaliswarran, S.R. Hindusthan Institute of Technology Departmentt of Mechanical Engineering Coimbatore India K.Ramakrishnan College of Technology Department of Ece Tiruchirappalli India Psg Institute of Technology and Applied Research Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350341485

Agriculture is a backbone of India. Precision agriculture focuses on delivering tools for harvest data, task coordination, and growth updates, thereby enhancing agricultural productivity through the incorporation of sensor network technologies to monitor various farming activities. This studyr introduces a solar-powered wireless system for detecting plant diseases in the environment. A comprehensive wireless sensing system is showcased, comprising sensors for temperature, humidity, and soil moisture, along with a microcontroller, IR transmitter and receiver, camera, and GSM module, enabling extended periods of environmental monitoring ARM processor used for ADC and sends the collected data to mobile via GSM. A camera is periodically enabled for capturing the plant picture for diseases monitoring and IR unit used for the purpose of plant growth monitoring. It use soil energy to power the temperature sensor to develop plant growth monitoring system and plant disease monitoring, also develop android application to monitor such conditions by using this technology to achieve a green electronics system. © 2023 IEEE.

关键词： Temperature sensors

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Rock-Paper-Scissors Image Classification Using Transfer Learning 2

Rock-Paper-Scissors Image Classification Using Transfer Lear...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ahmed, Fahad Khan, Wasim Ahmad Iqbal, Muhammad Ahmad Abazeed, Ashraf Riad Alrababah, Hamza Khan, Muhammad Farhan National College of Business Administration & Economics Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Department of Computer Science Islamabad Pakistan Abu Dhabi Vocational Education and Training Institute ADVETI United Arab Emirates University City Sharjah School of Information Technology Skyline University College Sharjah1797 United Arab Emirates University of Health Sciences Department of Forensic Sciences Lahore Pakistan

ISBN: (纸本)9798350335644

Games are a genre of deliberate mental or physical activities which can be done alone or in groups for the purpose of enjoyment and recreation. Every game has its unique collection of participants, movements, and tactics, in addition to its own set of principles and guidelines. Rock-Paper-Scissors is probably the most well-known game for determining a winner in the world. The rules are pretty straightforward. Rock, paper, or scissors are displayed by each player simultaneously. The rock defeats the scissors but is defeated by the paper. Scissors defeat paper but lose to rock and paper defeats rock but loses to scissors. Using hand images, this study proposed identifying images of hands playing rock-paper-scissors. Transfer learning is used in the proposed model to recognize and classify images of rock-paper-scissors. The dataset for this study was acquired from the Kaggle. There are 2188 images in the dataset, including 712 rock images, 726 paper images, and 750 scissor images. The dataset is divided into two parts: 80% is utilized for training the model, and 20% is utilized for model validation. The RPSICTL model evaluation results good validation accuracy of 99.54%. © 2023 IEEE.

关键词： Rocks

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On the periodic motions of a one-degree-of-freedom oscillator

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SeMA Journal 2024年第3期81卷 479-494页

作者： Kooij, Robert Zegeling, André Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Unit ICT Strategy and Policy TNO (Netherlands Organisation for Applied Scientific Research) The Hague Netherlands College of Mathematics and Statistics Guangxi Normal University Guilin China

We present a mechanical model for an oscillator with one degree of freedom under the influence of a flowing medium. Under fairly general conditions we show that the ensuing differential equation has at most two limit cycles and we give examples where exactly two limit cycles will occur. The implications of this result are that it is possible for a system of this kind to exhibit galloping even when the so-called Den Hartog criterion of local instability is not satisfied. © 2023, The Author(s).

关键词： 34C15 34C25 34C29 73K70 Galloping Limit cycles Liénard system Wind-induced vibrations

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QSVG: Quaternion Vision Graph Neural Network with Structure Inference for Texture Classification 16

QSVG: Quaternion Vision Graph Neural Network with Structure ...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Wen, Haiyuan Huang, Guoheng Cheng, Lianglun Liao, Xianglian Wu, Shiting Yuan, Xiaochen Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Faculty of Applied Sciences Macao Polytechnic University China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

In the realm of texture classification, while traditional approaches like Bag of Words (BoW) and Convolutional Neural Networks (CNNs) are commonly used, recent research highlights the remarkable flexibility offered by Graph Neural Networks (GNNs). Specifically, GNNs can effectively represent image blocks as one-dimensional multi-channel node features from three-dimensional data, interconnecting node feature channels. However, existing methods tend to process channels separately during graph convolution, overlooking critical inter-channel connections that are integral to capture intricate relationships between texture features, potentially limiting the effectiveness of GNNs in texture classification. Moreover, textures contain vital global information essential for classification performance, but GNNs tend to focus on information from similar neighboring nodes, which can restrict their capability to capture the global feature of the entire image during data processing. To address the aforementioned challenges, we propose a novel GNN architecture called Quaternion Structure inference Vision Graph neural network (QSVG) for texture classification, which combines quaternions and structure inference. Unlike previous approaches, QSVG comprises two key components: first, Quaternion Max-related convolution (Qmax), which uses the Hamilton transform to enhance node characterization by exploiting inter-channel connections;second, Structure Inference Block (SIF Block), which enriches node information through Bidirectional Gated Recurrent Unit (Bi-GRU)-based structure inference, fusing graph neural network node features with global features. Experimental results on three standard texture datasets demonstrate the competitive classification performance of our method. © 2025 SPIE.

关键词： Graph neural networks

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Resource Consumption of Federated Learning Approach applied on Edge IoT Devices in the AGV Environment 1

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23rd International Conference on Computational science, ICCS 2023

作者： Shubyn, Bohdan Grzesik, Piotr Maksymyuk, Taras Kostrzewa, Daniel Benecki, Pawel Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Computer Science Emory University AtlantaGA30322 United States Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

Federated learning is a distributed machine learning method that is well-suited for the Industrial Internet of Things (IIoT) as it enables the training of machine learning models on distributed datasets. One of the most important advantages of using Federated Learning for Automated Guided Vehicles (AGVs) is its capability to optimize resource consumption. AGVs are typically resource-constrained systems and must operate within tight power and computational limits. By using Federated Learning, AGVs can perform model training and updating on-board, which reduces the amount of data that needs to be transmitted. This paper presents experiments to assess the consumption of resources of the Jetson Nano edge IoT device while training the Federated Learning model, and compares it with referential machine learning approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recurrent neural networks

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AI Simulation, IoT, and Digital Twin for Smart Campus 5

AI Simulation, IoT, and Digital Twin for Smart Campus

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Pexyean, Tanapeak Saraubon, Kobkiat Nilsook, Prachyanun Bangkok Suvarnabhumi University Department of Information Technology and Digital Innovation Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer Science and Information Bangkok Thailand King Mongkut's University of Technology North Bangkok Division of Information and Communication Technology for Education Faculty of Technical Education Bangkok Thailand

ISBN: (纸本)9798331505998

The objective of this research is to Present the use of Artificial intelligence Simulation(AI Simulation), Internet of Things(IoT) and Digital Twin technology to Intelligently To develop the energy management potential of the campus intelligently by using technology to improve the environment, making the system smarter and more connected. This helps to make living in an urban community or university more comfortable. The adoption of various technologies has led to development in many fields, such as smart cities, smart homes, smart robots, smart machines Smart cars, smart agriculture, smart healthcare, and smart traffic control systems have the challenge of developing systems that allow devices to connect to the system quickly. One of the interesting ideas is Smart Campus, which is an educational institution that creates learning to develop individuals to be able to improve the world. The use of Internet of Things (IoT) technology, artificial intelligence, simulation, and the creation of digital twins. The use of IoT helps collect and exchange data from various devices in the network, enabling effective monitoring and control of the operation of various systems. Simulation allows for the creation of different scenario models to experiment and improve the system before it is actually implemented. It is a virtual model of a system or device to be used to track electricity consumption and optimize electrical energy management in real time. The application of these technologies in smart campuses will help to manage resources more efficiently, such as electrical energy management. Improvement of the transportation system within the university Security, and enhancing modern learning experiences. Developing the university into a smart campus not only improves operational efficiency but also helps to use energy efficiently. Reduce energy costs Reduce carbon dioxide emissions and create a sustainable and environmentally friendly learning environment both now and in the futur

关键词： Smart agriculture

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A Comprehensive Review Analysis of Distinct Tools Image Plagiarism Detection

A Comprehensive Review Analysis of Distinct Tools Image Plag...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Naudiyal, Anjali Singh, Vineeta Joshi, Kapil Roka, Sanjay Mamoria, Pushpa Shakya, Rajeev Kumar School of Applied and Life Sciences Uttaranchal University Premnagar Dehradun India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Uttaranchal University Department of Computer Science & Engineering Dehradun India Graphic Era deemed to be university dehradun India School of Electrical Engineering and Computing Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia

ISBN: (纸本)9798350379716

In the era of artificial intelligence, academic activities tend to use Chat GPT more frequently. A Chabot called Chat GPT is used for brainstorming, writing, and learning. Although most academicians use Chat GPT to their advantage when taking notes, it is detrimental to research. A few authors wrote the papers swiftly by using AI techniques. These days, chat GPT is employed in a wide range of tasks. It prepares any type of content for the internet. The likelihood of plagiarism is rising as a result of its use. At present, plagiarism is the biggest challenge in the field of research. Plagiarism occurs when an author uses this technology to modify another person's work and transmit it off as their own without providing a reference or credit. Plagiarism is a term used to describe when someone copies something from another source without providing credit to the original author. Google is working quickly in the image field, but at that point, it will be used to detect plagiarism that is generated with the aid of artificial intelligence. The less papers that are utilized in favor of online resources, the more academics are working to make the globe more environmentally friendly. In the presented paper, a few software applications are discussed and compare a number of different techniques to detect the plagiarism in academics field like scientific equations or cross-lingual or multilingual texts. Various applications are developed in research to identify instances of plagiarism in research papers, assignments, and source code. After reviewing the papers pertaining to various tools for detecting plagiarism, not much more work is done in the area of detecting plagiarism in images or in image text. The article that is being presented comes to the conclusion that no software tool has been developed to identify plagiarism in images or in the text associated with images. © 2024 IEEE.

关键词： Application programs

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