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Evaluation of Solar Energy Generation and Radiation Prediction Using Machine Learning

Evaluation of Solar Energy Generation and Radiation Predicti...

2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, ICIITCEE 2023

作者： Gupta, Jagrati Shrivastava, Gourav Raghuwanshi, Santosh S Sage University Computer Science Engineering M.P. Bhopal India Medi-Caps University Electrical Engineering Department MP Indore India

ISBN: (纸本)9781665462631

Solar photo voltaic (PV) energy system backbone of the renewable energy system. Energy system is depended on weather conditions such as temperature and radiation intensity. The role of machine learning (ML) for solar energy generation and radiation forecasting. This paper presents ML algorithm or methods review for prediction of solar energy generation and radiation. This paper also presents the state of art on different ML methods and parameters for forecasting solar energy production and radiation. © 2023 IEEE.

关键词： Forecasting

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ProTEA: Programmable Transformer Encoder Acceleration on FPGA

ProTEA: Programmable Transformer Encoder Acceleration on FPG...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Ehsan Kabir Jason D. Bakos David Andrews Miaoqing Huang Department of Electrical Engineering and Computer Science University of Arkansas Fayetteville USA Department of Computer Science and Engineering University of South Carolina USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Transformer neural networks (TNN) have been widely utilized on a diverse range of applications, including natural language processing (NLP), machine translation, and computer vision (CV). Their widespread adoption has been primarily driven by the exceptional performance of their multi-head self-attention block used to extract key features from sequential data. The multi-head self-attention block is followed by feedforward neural networks, which play a crucial role in introducing non-linearity to assist the model in learning complex patterns. Despite the popularity of TNNs, there has been limited numbers of hardware accelerators targeting these two critical blocks. Most prior works have concentrated on sparse architectures that are not flexible for popular TNN variants. This paper introduces ProTEA, a runtime programmable accelerator tailored for the dense computations of most of state-of-the-art transformer encoders. ProTEA is designed to reduce latency by maximizing parallelism. We introduce an efficient tiling of large matrices that can distribute memory and computing resources across different hardware components within the FPGA. We provide run time evaluations of ProTEA on a Xilinx Alveo U55C high-performance data center accelerator card. Experimental results demonstrate that ProTEA can host a wide range of popular transformer networks and achieve near optimal performance with a tile size of 64 in the multi-head self-attention block and 6 in the feedforward networks block when configured with 8 parallel attention heads, 12 layers, and an embedding dimension of 768 on the U55C. Comparative results are provided showing ProTEA is 2.5× faster than an NVIDIA Titan XP GPU. Results also show that it achieves 1.3 – 2.8× speed up compared with current state-of-the-art custom designed FPGA accelerators.

关键词： Runtime High performance computing Memory management Parallel processing Transformers Feature extraction Magnetic heads Sparse matrices Machine translation Field programmable gate arrays

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A Meta Distribution-Based Fine-Grained Analysis for Contention-based WiFi Backscatter Networks 21

A Meta Distribution-Based Fine-Grained Analysis for Contenti...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Yulei Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Luo, Yu Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology 999078 China College of Electronics and Information Engineering South-Central Minzu University Wuhan430074 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

WiFi backscatter communication has gained many applications, but its performance characteristics remain to be analyzed. While existing research has investigated the success probability of backscatter tags in contention-based WiFi backscatter networks (CWBNs), it has focused solely on the first-order statistic of the signal-to-interference-plus-noise ratio (SINR). In this paper, we present a meta distribution-based fine-grained analysis that provides high-order statistics of SINR and characterizes the disparity among backscatter transmission links in CWBNs. Leveraging stochastic geometry, we, for the first time, derive mathematical expressions for the b-th moments of conditional success probability and its meta distribution. The extensive Monte-Carlo simulation results validate the accuracy of our proposed theoretical model and demonstrate its outstanding value to help us understand the overall performance of CWBNs. © 2024 IEEE.

关键词： Wi-Fi

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An Intelligent System for Furfural Estimation in the Power Transformers 2nd

An Intelligent System for Furfural Estimation in the Power T...

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2nd International Conference on Renewable Power, ICRP 2023

作者： Nezami, Md. Manzar Hashem, Hythem Equbal, Md. Danish Khan, Mohammad Junaid Ansari, Md. Fahim Mustafa, Elfatih Elmubarak Department of Electronics and Communication Engineering GLA University Mathura281406 India Department of Computer Science Faculty of Science and Arts at Belqarn University of Bisha Sabt-Al-Alaya61985 Saudi Arabia Department of Electrical Engineering Galgotias College of Engineering and Technology Greater Noida India Haryana Nuh India Department of Electrical Engineering Graphic Era University Dehradun India

ISBN: (纸本)9789819967483

Power transformers are supposed to be an expensive and critical component of a power system and so its schedule maintenance is an important aspect near the utilities. The cellulose paper used as the solid insulating material of the transformer deteriorates regularly due to progressive aging. As a result, it produces several degradation by-products of cellulose insulation into the transformer oil. Furfurals are among the major by-product of cellulose and are exploited to estimate the physical state of the transformer’s dielectric and the electrical insulation directly and noninvasively. In the present work, an intelligent system is proposed and developed that predicts the level of furfural in the transformer oil. The system makes predictions using easily quantifiable parameters, enabling utilities to avoid suffering financial losses. The proposed system employs the Adaptive Neuro Fuzzy Inference System (ANFIS) technique with temperature and moisture as the input and 2-Furfuraldehyde (2-FAL) as the output. An optimum arrangement of the developed model to find accurate outputs has been achieved by the suitable selection of number and types of the membership functions of the network. Also, an exhaustive study is made to optimize the training data points to achieve the accurate predictions at minimum iterations. The proposed system is validated with nine test conditions of temperature and moisture, and the output thus obtained is verified through existing life models in the literature. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power transformers

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Text Extraction with Optimal Bi-LSTM

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computers, Materials & Continua 2023年第9期76卷 3549-3567页

作者： Bahera H.Nayef Siti Norul Huda Sheikh Abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed Computer Techniques Engineering Department Ibn Khaldun University CollegeBaghdad10011Iraq Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University MalaysiaSepang43900Malaysia

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the *** Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant *** study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain *** pass them to deep learning layers to extract feature maps and reform the predicted *** Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training *** Connectionist Temporal Classification(CTC)function calcifies the training and validation loss *** addition to decoding the extracted feature to reform characters again and linking them according to their time *** proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)*** result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and *** the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

关键词： Deep neural network text features dual max-pooling concatenating convolution neural networks bidirectional long short memory text connector characteristics

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Current Injection Based Quasi-Static Time Series Load Flow Algorithm for Distribution System with Multiple Distributed Energy Resources

Current Injection Based Quasi-Static Time Series Load Flow A...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Suresh, Arun Murari, Krishna Kamalasadan, Sukumar University of North Carolina at Charlotte Department of Electrical Engineering CharlotteNC28223 United States The University of Toledo Electrical Engineering and Computer Science Department ToledoOH43606 United States

ISBN: (纸本)9781665471640

In this paper, a current injection-based QSTS load flow approach has been developed to simulate and analyze the interactions between Distributed Energy Resources (DERs) and voltage-regulating devices over a given time. The novelty is that the paper proposes a comprehensive modeling approach that takes into account the intricacies of both voltage-regulating devices and DERs. By integrating these models into the QSTS simulation, the researchers can perform detailed analyses and simulations to evaluate the effects of DERs on control devices and assess the overall impact on the grid's stability and operation. The process involves modeling the voltage-regulating devices, such as the load tap changers and voltage regulators, in a way that accurately represents their physical characteristics and control strategies. This enables the simulation to mimic the real-world behavior of these devices under various conditions. The time series analysis has been successfully validated using a state-of-the-art QSTS solver, demonstrating that the developed approach is both efficient and accurate. © 2023 IEEE.

关键词： Distributed Energy Resources Grid Modernization Injected Current Sensitivity (ICS) Load Tap Changer (LTC) Multi-Phase Load Flow Unbalanced Distribution System Voltage Regulator

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Improved SINR m-Coverage Probability in 6G Networks: Unveiling the Sample Point Saturation Phenomenon in Urban and Suburban Environments

Improved SINR m-Coverage Probability in 6G Networks: Unveili...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajaegbu, Chigozirim Adebiyi, Abayomi A. Kanu, R.U. Babcock University Department of Computer Science Ogun State Ilishan-Remo Nigeria Durban University of Technology Department of Electrical Power Engineering Durban South Africa Babcock University Department of Basic Science Ogun State Ilishan-Remo Nigeria

ISBN: (纸本)9798350358155

This study looks at the influence of sample points on SINR-based m-coverage probability in Urban and Suburban environments within the context of 6G networks. SINR is a crucial factor impacting network coverage and reliability. While it is commonly assumed that increasing sample points enhances SINR coverage probability, the relationship between sample points and coverage probability remains intricate and not well-understood. The main objective of this study was to determine the saturation point at which additional sample points only marginally improve network performance. To achieve this, advanced simulations were conducted using the Poisson point process to spatially distribute nodes in single-tier and multi-tier networking scenarios. Existing mathematical models of path-loss, interference, and coverage probability were extended, taking into account other wireless phenomena present in urban and suburban environments. The analysis indicates a positive correlation between coverage probability and increasing sample points, as expected for improved network performance. However, intriguingly, both Urban and Suburban environments exhibit a saturation point, challenging the conventional belief of continuous improvements with more sample points. The optimization of sample point numbers becomes crucial to strike the right balance between accuracy and computational efficiency in designing 6G networks. These findings offer valuable insights for network planning, ultimately enhancing the overall performance and reliability of 6G networks in dynamic environments. © 2024 IEEE.

关键词： Signal to noise ratio

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Digital Twin-Assisted Space-Air-Ground Integrated Networks for Vehicular Edge Computing

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IEEE Journal on Selected Topics in Signal Processing 2024年第1期18卷 66-82页

作者： Paul, Anal Singh, Keshav Nguyen, Minh-Hien T. Pan, Cunhua Li, Chih-Peng National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT71NN United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211111 China

In this paper, we present a framework that integrates digital twin (DT) technology into space-air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our objective is to efficiently offload tasks in ultra-reliable low-latency communications (URLLC)-enabled vehicular networks, focusing on minimizing overall latency for requested tasks by reducing transmission time for task offloading and edge processing requirements. The proposed framework leverages DT-assisted SAGINs to minimize task offloading latency, expand network coverage, and reduce energy consumption. Key components of our framework include partial task offloading, distributed edge computing, latency modeling, energy consumption analysis, mobility, and channel modeling. We formulate a non-convex optimization problem considering various network constraints to achieve the system objective. To solve this optimization problem, we develop a novel multi-agent deep reinforcement learning (DRL) algorithm, enabling intelligent decision-making by individual agents. Through extensive simulations, we validate the effectiveness of our proposed system in advancing VEC by integrating DT technology into SAGINs. © 2007-2012 IEEE.

关键词： Energy utilization

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

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computer Modeling in engineering & sciences 2024年第9期140卷 2689-2706页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia School of Science and Technology Hong Kong Metropolitan University(HKMU)Hong KongChina

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall *** balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification *** proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly *** model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and *** study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

关键词： LSTM neural networks anomaly detection smart home health-care elderly fall prevention

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