Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points

Cheng, Ruhui; Yin, Xiang; Chen, Leilei

doi:10.32604/cmes.2022.018519

Cited by 3 publications

(2 citation statements)

References 37 publications

(37 reference statements)

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“…The tests are validated with a new dataset after trained of the proper model. The tested model check for various performance parameters that helps in choosing the best model out of all trained models [18]. This step is important as it gives an idea about the effectiveness model.…”

Section: Data Synthesis and Preparationmentioning

confidence: 99%

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Kumar¹,

Pal²

2023

Computers, Materials &Amp; Continua

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Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management.

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Section: Data Synthesis and Preparationmentioning

confidence: 99%

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Kumar¹,

Pal²

2023

Computers, Materials &Amp; Continua

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“…In recent years, with the improvement of computing power, the research of deep learning (DL) has developed rapidly [18,19]. Since DL models can extract latent representation information from raw hyperspectral data by automatically learning feature representations, the application of DL models in HSI classification tasks has gradually become a research hotspot [20].…”

Section: Introductionmentioning

confidence: 99%

Spectral-spatial inverted residual attention network for hyperspectral image classification with pre-activation

Yang,

Wang

2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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In recent years, hyperspectral image (HSI) classification technology has received more attention. Deep learning methods have been gradually applied to the classification of HSIs. Convolutional neural networks-based model, especially the residual networks (ResNets), have shown its excellent performance. In HSI samples, there are usually some noise pixels near the center pixel, which are not conductive for the extraction of spectral-spatial features and will have a negative impact on the classification performance. In our previous study, a spectral similarity-based spatial attention module integrated with 3D Resnet was designed to highlight the effect of center pixels on spatial attention. However, during the generation of the spatial attention, the characteristics of different similarities are ignored. Meanwhile, the employment of 3D ResNet may generate a large amount of redundancy to waste the computing resources. Therefore, an improved spectral-similarity-based spatial attention module with pre-activation and a 3D inverted residual attention network, is proposed. The pre-activation strategy is designed to follow the feature of each similarity measurement, in which the Canberra distance is invoked to simplify the computational complexity of similarity. A 3D inverted residual module is integrated with squeeze-and-excite attention modules to handle spatial and spectral-spatial features more efficiently. Experimental results on three public HSI data sets demonstrate a better classification performance of the proposal comparing with some of the state of the arts.

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Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

Bera¹,

Shrivastava²,

Satapathy³

2022

Computer Modeling in Engineering &Amp; Sciences

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Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points

Cited by 3 publications

References 37 publications

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Spectral-spatial inverted residual attention network for hyperspectral image classification with pre-activation

Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

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