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24th International Middle East Power System Conference, MEPCON 2023

作者： El-Faraskoury, Adel Ward, Sayed A. Mansour, Diaa-Eldin A. Ibrahim, Shimaa A. Badawi, Mohamed Extra High Voltage Research Centre Egyptian Electricity Holding Company Egypt Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt Delta University for Science and Technology Faculty of Engineering Department of Electrical Engineering Egypt Egypt-Japan University of Science and Technology School of Electronics Communications and Computer Engineering Electrical Power Engineering Department Alexandria Egypt Tanta University Faculty of Engineering Electrical Power and Machines Engineering Department Tanta Egypt

ISBN: (纸本)9798350358469

One of the indispensable assets of the electrical systems and its most expensive components are power transformers. Condition assessment of power transformers is crucial and must be performed precisely to detect any abnormal conditions and prevent failures. Most transformer condition assessment relies on evaluating the insulating oil quality that achieves the cooling inside the transformer and the electrical insulation role. Hence, insulating oil performance is an important term for the reliable operation of power transformers. Under field aging, there is critical degradation in the chemical properties of the transformer oil, which may cause the occurrence of arcing, thermal, and partial discharge fault types. One of these chemical degradations caused by transformer aging is the generation of dissolved gases. Hence, dissolved gas analysis (DGA) is considered an in-service common and effective technique for detecting oil condition assessment under field aging. In addition, this technique can diagnose fault types. This paper studies the effect of oil aging on dissolved gas generation. For this purpose, it presents an analysis of the generation of dissolved gases for four old transformers of the same oil type (Diala B) and different ratings that exist in the Egyptian Electricity Transmission Company (EETC). This analysis of transformer oils was studied from 2010 to 2021. © 2023 IEEE.

关键词： Insulating oil

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Patient-Centric Skill Evaluation in IoT-Based Surgical Training with Naive Bayes Algorithm for Personalized Feedback 2

Patient-Centric Skill Evaluation in IoT-Based Surgical Train...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gopi, B. Ashfaqul Hasan, M.D. Kumar, G. Sivakumar, S. Raju, M. Janardhana Malathi, N. Department of Electronics and Communication Engineering Rasipuram Tamil Nadu India Narsampet Telangana Warangal India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chenagalpattu Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Siddartha Institute of Science and Technology Narayanavanam Andhra Pradesh Puttur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Internet of Things (IoT) technology has established essential in advancing surgical training, enabling more individualized and patient-centric methods. This paper proposes a unique framework for skill assessment in surgical training, concentrating on individualized feedback using the Naive Bayes algorithm. With the use of IoT technology, surgical simulators can now collect real-time data on trainee performance while also communicating patient-centric factors into account. The data is analyzed using the suggested Naive Bayes Algorithm, which places an emphasis on providing personalized feedback to individuals. The objective of this method is to improve surgical outcomes while simultaneously increasing surgeons' technical competence. The trainee's abilities are evaluated in a variety of ways by the algorithm, which takes into account accuracy, recall, and other metrics. It provides to the continuing attempts to establish more efficient and individualized training procedures using IoT-based surgical training combined with machine learning approaches. These results demonstrate the promise of the Naive Bayes algorithm for improving surgical outcomes via the delivery of actionable, personalized feedback to surgeons. © 2024 IEEE.

关键词： Internet of things

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A Machine Learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0 4

A Machine Learning Approach in Predictive Maintenance in the...

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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Shankar, B. Prabhu Yadav, Rakesh Kumar Jayalakshmi, K.P. Solution Architect Pilvi Systems Inc LewisvilleTX75057 United States Alliance University Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka562106 India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India St Joseph Engineering College Department of Electronics and Communication Engineering Karnataka Mangaluru India

ISBN: (纸本)9798350300888

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness. © 2023 IEEE.

关键词： Industry 4.0

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Fusion-based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and Image Enhancement in Low-Light IoT Environments 2

Fusion-based Digital Twin Framework with Convolutional Neura...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, K. Ashok Barmola, Pareshwar Prasad Velmurugan, S.P. Manjunath, A.C. Soni, Umang Maranan, Ramya Department of Physics B V Raju Institute of Technology Medak Narsapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

Image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PSNR) of 32.45 dB and a structural similarity index (SSIM) of 0.87, reflecting an 18% improvement in PSNR and a 12% gain in SSIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction. © 2024 IEEE.

关键词： Wavelet decomposition

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Efficient Wideband Diode Mixer for k-Band Applications 2

Efficient Wideband Diode Mixer for k-Band Applications

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2nd International Conference on Advanced Electrical engineering, ICAEE 2022

作者： Sellidj, A. Gaoua, Said Yagoub, M.C.E. University of Science and Technology Houari Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

In this work, an efficient k-band diode mixer was designed in TSMC CMOS 180 nm technology. It works in the frequency range of 17-28 GHz with a conversion loss of 7.5 dB for an IF frequency of 3 GHz and a local oscillator power of 15 dBm. It exhibits an isolation better than 30 dB between the input LO and RF ports and the IF output port. Furthermore, the proposed mixer shows low conversion loss particularly for high IF frequencies. © 2022 IEEE.

关键词： CMOS integrated circuits

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

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Exploring the Benefits of Deep Learning for Forecasting Time Series Data 3

Exploring the Benefits of Deep Learning for Forecasting Time...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Daivagna, Umesh.M. Vinay Kumar, S.B. Agarwal, Ankita Jha, Gaurav Sharmila, P. Dodia, Shubham J. Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Deep present day is swiftly emerging as an effective device for forecasting time series records. Current advances in deep mastering fashions permit computerized characteristic extraction and trendy contemporary complex nonlinear dynamic relationships among inputs and outputs. The ability to symbolize high-level temporal and temporal-spatial systems of modern-day multivariate time collection facts gives extended accuracy and reliability in forecasting. Moreover, recent strategies skip entering facts via temporal convolutional neural networks to forecast destiny values in present-day time-collection statistics. This article delves into the use of deep learning for forecasting time series data. Deep learning methods, specifically recurrent neural networks, are found to outperform traditional forecasting techniques in accuracy and efficiency. The ability of deep learning to automatically extract patterns and relationships from large amounts of data makes it a valuable tool for forecasting in various industries such as finance, healthcare, and energy. Additionally, the integration of deep learning with other techniques, such as feature selection and data preprocessing, can further improve its forecasting capabilities. Overall, the benefits of using deep learning for time series forecasting make it a promising approach for decision-making and strategic planning in a rapidly changing world. It has enabled the improvement of brand-new state-of-the-art deep ultra-modern models that could seize the dynamics of contemporary statistics sequences without counting on any prior expertise, resulting in correct forecasts. This paper evaluation the modern deep, brand-new techniques for time collection forecasting and discusses the recent traits in the field. Capability-demanding situations and opportunities within the application of modern deep modern day for time series forecasting are addressed in detail in the paper. © 2023 IEEE.

关键词： convolutional developments forecasting learning techniques

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Using Machine Learning Tools to Analyze Large Real-Time Data Sets 1

Using Machine Learning Tools to Analyze Large Real-Time Data...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Hannah Jessie Rani, R. Yadav, Rakesh Kumar Garg, Avni Gayathri, C. Gaikwad, Vidya S. Vishnoi, Manish Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electronics and Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350359756

Using engine getting-to-know-the-gear to investigate big real-time statistics sets has become increasingly commonplace because of its efficiency, scalability, and advanced abilities. Devices getting to know equipment permit for speedy facts evaluation and sample reputation, allowing quicker and more accurate decision making. This gear may make actual-time data sets more beneficial via detecting traits, figuring out correlations, and developing models for predictive analysis. Moreover, machine mastering can personalize products and services to fulfill consumer desires. By utilizing the system and gaining knowledge of algorithms, groups can better understand user behavior and preferences, leading to extra valuable product and provider offerings. Ultimately, the use of system getting-to-know equipment to release real-time facts helps corporations to make greater informed selections and deliver higher consumer studies. © 2023 IEEE.

关键词： Decision making

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