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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of on-line detection of cylinder wall temperature in the drying process of tobacco dryer, this paper proposed a novel method named deep gated recurrent unit(DGRU)network to predict cylinder wall temperature. In order to improve sample quality, data preprocessing technologies including wavelet denoising and normalization were utilized. Then, mutual information was used to determine the optimal features as model input, which had the largest correlation with the cylinder wall temperature. Next, stacked gate recurrent unit network was introduced to extract the deep hidden nonlinear dynamic representation from thermal data, and then sent into a fully connect network to estimate the cylinder wall temperature. Finally, based on the industrial data of dryer drying process in a tobacco factory, the experiment results showed that the median and mean value of prediction error by DGRU algorithm was very close to the zero scale line, and caused few abnormal points. The method exhibited higher prediction accuracy, and could accurately predict the cylinder wall temperature. [ABSTRACT FROM AUTHOR]

To overcome the problem of parameters calculation for optimal segmented Knothe time function, two calculation methods are presented. Firstly, based on the surface monitoring data of a mining area, or mining areas with similar geological and mining conditions, the back-calculation and comparison method of parameter determination is proposed. It gives a detailed parameter calculation process, which is intuitive, easy to operate, and versatile, and can also be used to obtain the parameters for other time functions. Secondly, based on the surface subsidence law when the goaf reaches its full mining size, and the corresponding probability integration parameters, a"direct calculation method"of parameter determination is proposed. The parameters of the method are clear in meaning, and the process of parameter calculation is convenient and can be directly applied to program calculation. Through the dynamic prediction practice, it is shown that the maximum relative error of the dynamic prediction can be controlled within 9% using the method described in the paper. With the increase of mining time, the dynamic prediction accuracy will gradually increase and it will eventually be maintained at around 5%. According to the statistics, the relative prediction error of the maximum surface subsidence can be maintained at around 6%. Application practice proves the practicability and reliability of the method proposed in the paper. [ABSTRACT FROM AUTHOR]

Copyright of Mountain Research (10082786) is the property of Mountain Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On the basis of the water inrush theory of coal mines and analysis of field measured data, this paper established water influx indicators for water inrush from coal seam floor. Through the feature selection of the Wrapper evaluation strategy, the main control factors affecting the water inrush of coal mine were finally selected. After training and establishing a dynamic gated recurrent unit (GRU) neural network prediction model of water inrush in coal seam, this model was compared with other static prediction models. The accuracy predicted by gate recurrent unit neural network model during the validation, training, and test phases was higher than that obtained with other static models, indicating that gate recurrent unit model can well predict the water inrush from coal seam floor and improve coal mine production safety.

For the purpose of achieving more accurate and reliable gas emission dynamic prediction through effective analysis of gas measuring data in mines, this paper put forward a method that use ant colony clustering to optimize El-man neural network. Ant colony clustering algorithm was merged with Elman neural network to optimize weight and threshold. The model of gas emission quantity prediction was established by ACC-ENN algorithm, with the historical data of mine actual monitoring to experiment and analysis. The results show that the Elman neural network model optimized by ant colony clustering than other prediction model has better generalization ability and higher precision of prediction, to realize the dynamic forecast of gas emission effectively. [ABSTRACT FROM AUTHOR]

Based on the systematic investigation, this paper summarizes the four important development stages of the prediction technology of mine gas emission in China from 1950s to now. It focuses on the basic principles, application scope, advantages and disadvantages of the source based prediction method, mine statistical method, gas geological mathematical model method, grey prediction method, neural network prediction method and combination prediction method. The main problems of each prediction method are analyzed in detail with the field application status. Finally, centering on the development goals of mine mechanization and intelligentization, three technological breakthroughs are proposed: improving the scientific selection of predictive parameters of source separation, promoting the modularization and integration of gas geological model prediction technology, and establishing information-based high-end fusion model, which points out the direction for future research on gas emission prediction technology.