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Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system

Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 8035-8044页

作者： Singh, Upma Rizwan, M. Department of Electrical Engineering Delhi Technological University Delhi110042 India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology Delhi110058 India

In this paper, Machine Learning (ML) based techniques known as Support Vector Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for improving the forecast accuracy of short-term wind power production. In addition, data analysis framework is also prepared to visualize the data acquired from the Supervisory Control and Data Acquisition (SCADA) system. Dataset analysis has been carried out in Polar and Cartesian coordinates to identify the produced power and wind relationship. Based upon different statistical indices, the performance of the developed model has been evaluated. In addition, the comparative analysis reveals that GBRT model performance is superior than that of the SVR model with Mean-Square Error (MSE) value of 0.026, Root Mean-Square Error (RMSE) 0.301, Mean Absolute Percentage Error (MAPE) 0.063, Mean Absolute Error (MAE) 0.004 and Coefficient of determination (R2) 0.969. The results obtained illustrate that the proposed model would be applicable in real-life scenarios and help the management team to handle the produced power from wind turbines. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： SCADA systems

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年第13期12卷 25020-25032页

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Crowd Size Estimation: Smart Gathering Management 2nd

Crowd Size Estimation: Smart Gathering Management

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2nd International Conference on Emerging Trends in engineering and Technology, EGTET 2022

作者： Swami, Ishita Das, Nimish Sunil Department of Computer Engineering LJ Institute of Engineering and Technology Ahmedabad India Department of Electronics and Communication Engineering LJ Institute of Engineering and Technology LJK University Ahmedabad India

ISBN: (纸本)9789819943616

Linear increase in population which results in overcrowding has become an unavoidable element in any public gathering. Public safety under such condition has become a very vital problem in areas like streets, malls and railway stations during weekends, festive seasons, holidays, concerts, etc., normally or in any pandemic situation. The massive disasters that can occur includes numerous instances of fatality where people gather in form of throng. In present time, surveillance cameras are deployed to maintain peace, security and manage crowd, as surveillance videos for proper analysis of crowd activities is an important issue for communal harmony and security;however, some major limitations in video surveillance system are that includes picture getting blurred, peculiarities among person cannot be identified automatically with respect to surroundings during live video streaming, along with that to save the information a lot of storage spaces is also required and hence it becomes costly to run and maintain. The present study proposes a method that is based on principle of Histogram of Oriented Gradients (HOG) and OpenCV that efficiently keeps in track count of the people in the scene which helps in efficient crowd management. OpenCV-based method used for crowd estimation written in Python used in this study in order to count the number of heads in live streaming and helps in crowd management according to requirement in an economical way. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Python

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Nanofabricated high turn-density spiral coils for on-chip electromagneto-optical conversion

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Microsystems & Nanoengineering 2024年第2期10卷 181-192页

作者： Ilhan Bok Alireza Ashtiani Yash Gokhale Jack Phillips Tianxiang Zhu Aviad Hai Department of Biomedical Engineering University of Wisconsin–MadisonMadisonWIUSA Department of Electrical and Computer Engineering University of Wisconsin–MadisonMadisonWIUSA Wisconsin Institute for Translational Neuroengineering(WITNe) University of Wisconsin–MadisonMadisonWIUSA

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction,modulation,and tunability,with specific applications in environmental and biomedical micromagnetometry.A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial *** efforts have exploited highly involved three-dimensional synthesis,advanced insulation,and exotic material ***,we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils;these coils achieve efficient on-chip electromagnetic-to-optical signal *** fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate)as a basis for the metal lift-off *** with 70 micrometer overall diameters and 49–470 nm interturn spacings with corresponding inductances of 12.3–12.8 nH are *** utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling *** designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation.

关键词： utilize retaining high

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A new technology perspective of the Metaverse:Its essence,framework and challenges

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Digital communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

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Annals of Data Science 2024年第1期11卷 103-135页

作者： Choithani, Tamanna Chowdhury, Asmita Patel, Shriya Patel, Poojan Patel, Daxal Shah, Manan Department of Computer Science and Engineering Indus University Gujarat Ahmedabad India Department of Electrical Engineering Nirma University Gujarat Ahmedabad India Department of Electronics and Communication Engineering Nirma University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India

In recent years cryptocurrencies are emerging as a prime digital currency as an important asset and financial system is also emerging as an important aspect. To reduce the risk of investment and to predict price, trend, portfolio construction, and fraud detection some Artificial Intelligence techniques are required. The Paper discusses recent research in the field of AI techniques for cryptocurrency and Bitcoin which is the most popular cryptocurrency. AI and ML techniques such as SVM, ANN, LSTM, GRU, and much other related research work with cryptocurrency and Bitcoin have been reviewed and most relevant studies are discussed in the paper. Also highlighted some possible research opportunities and areas for better efficiency of the results. Recently in the past few years, artificial intelligence (AI) and cybersecurity have advanced expeditiously. Its implementation has been extensively useful in finance as well as has a crucial impact on markets, institutions, and legislation. It is making the world a better place. AI is responsible for the simulation of machines that are replicas of human beings and are intelligent enough. AI in finance is changing the way we communicate with money. It helps the financial industry streamline and optimize processes from credit judgments to quantitative analysis marketing and economic risk management. The main goal of this research has been investigating certain impacts of artificial intelligence in this contemporary world. It's centered on the appeal of artificial intelligence, confrontation, chances, and its influence on professions and careers. The research paper uses AI to enable banks to generate financial resources and to provide valuable customer services. The application of the growing Indian banking sector is part of everyday life made up of several banks like RBI, SBI, HDFC, etc. and these banks have digitally implemented using chat-bots that have brought benefits to the customers. © The Author(s), under exclusive licence t

关键词： Risk assessment

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Enhanced Health Monitoring System for Internet of Medical Things using Machine Learning 15

Enhanced Health Monitoring System for Internet of Medical Th...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Swedheetha, C. Manikandan, P. Rajalakshmi, J. Muthuraja, T. Kavin Kumar, K. Chandrasekaran, Gokul Sriram Srivathsan, N. Velammal College of Engineering and Technology Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhnagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350370249

This brief looks at mobile operation, speech recognition, and voice robotization. Patients with physical limitations, such as Parkinson's disease and cerebral palsy, as well as those with speech impairments, can express their requirements with this application. The initiative enhances the general well-being and independence of people with impairments. Increasing the automation of current technologies is the main objective of this design. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

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SN computer Science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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