Gas disaster has always been a major safety problem in the coal mine field. The prediction of gas concentration in fully mechanized mining face is of great significance to ensure the safety of mine production and the safety of underground personnel. A Long short-term Memory (LSTM) neural network model based on time series is proposed for the prediction of gas concentration. Since there are many factors affecting the gas emission and there is a complex nonlinear relationship between them, a method of data preprocessing is proposed to weaken the data volatility, combined with the powerful GPU function of the computer, to build an LSTM neural network in the Tensorflow environment Gas Emission Prediction Model, using root mean square error (RMSE) and running time, for evaluating forecast performance. The prediction results are compared with the SVR network, and the results show that the LSTM model has higher prediction accuracy and prediction stability.
Aiming at the problems of uneven illumination, detail loss and color distortion of video images caused by low illumination and contrast of mine environment, a mine image enhancement algorithm based on bilateral filtering function is proposed. The algorithm first converts the image from RGB space to HSV space to avoid destroying the color space, extracts the illuminance layer and reflection layer of the image according to the classical Retinex theory combined with bilateral filtering with edge retention, adaptively adjusts the lighting by gamma function, and uses the CLAHE algorithm to enhance the overall contrast. Experimental results show that the proposed algorithm is superior to other commonly used algorithms in terms of visual effects, information entropy, average gradient, standard deviation, etc., which effectively improves the overall brightness and contrast of the image and improves the problems of detail loss and color distortion and realizes the effective enhancement of mine images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.