In regions characterized with great mining depths, complex topography, and intense geological activities, solely relying on lateral pressure coefficients or linear boundary conditions for predicting the in situ stress...
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In regions characterized with great mining depths, complex topography, and intense geological activities, solely relying on lateral pressure coefficients or linear boundary conditions for predicting the in situ stress field of rock bodies can induce substantial deviations and limitations. This study focuses on a typical karst area in Southwest Guizhou, China as its research background. It employs a hybrid approach integrating machine learning, numerical simulations, and field experiments to develop an optimization algorithm for nonlinear prediction of the complex three-dimensional (3D) in situ stress fields. Through collecting and fitting analysis of in situ stress measurement data from the karst region, the distributions of in situ stresses with depth were identified with nonlinear boundary conditions. A prediction model for in situ stress was then established based on artificial neural network (ANN) and genetic algorithm (GA) approach, validated in the typical karst landscape mine, Jinfeng Gold Mine. The results demonstrate that the model's predictions align well with actual measurements, showcasing consistency and regularity. Specifically, the error between the predicted and actual values of the maximum horizontal principal stress was the smallest, with an absolute error 0.01-3 MPa and a relative error of 0.04-15.31%. This model accurately and effectively predicts in situ stresses in complex geological areas.
We designed a broadband quarter wave plate in the visible range using a twisted nematic liquid crystal film sandwiched between two compensation films. The quarter wave plate exhibits much wider bandwidth than the comm...
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We designed a broadband quarter wave plate in the visible range using a twisted nematic liquid crystal film sandwiched between two compensation films. The quarter wave plate exhibits much wider bandwidth than the commercial product, which is composed of a half wave plate and a quarter wave plate.
ABSTRACTSports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an eff...
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ABSTRACTSports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an efficient key frame extraction method and hybrid Wavelet Convolution Neural Network (WCNN) framework with optimization scheme to classify sports videos. Initially, input videos are converted into number of frames, and keyframes are extracted using Enhanced threshold with Discrete Wavelet Transform (ETDWT) method. Then, Cross Guided Bilateral Filter (CGBF) method eliminates the noise from the keyframe. After that, segmentation process is performed by the Fuzzy Equilibrium Optimizer (FEO) algorithm, and then motions are detected using the Farneback optical flow (OF) method. Finally, classification process is performed using Hybrid Wavelet Convolutional Manta Ray Foraging optimization (HWCMRFO) algorithm to categorize different sports videos. The overall work is implemented using Python language. Simulation results proved that the proposed work achieved the highest accuracy (93.17%) compared to existing approaches.
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