Dynamic evaluation of support quality of hydraulic support in space-time region

The support process of hydraulic support is a dynamic change process in time and space regions. At present, the evaluation of support quality of hydraulic support mostly focuses on the static characteristics of support. There is little research on the dynamic change of support column pressure. In order to solve the above problems, a dynamic evaluation model of support quality of hydraulic support in the space-time region based on improved LeNet-5 network is established by using the deep learning method. Firstly, the column pressure data of hydraulic support in working face is preprocessed (missing value filling, abnormal value processing, screening, sorting, etc.) to obtain more complete pressure data hydraulic support. Secondly, the preprocessed column pressure data of the hydraulic support is arranged according to time and space. The important characteristics are extracted such as initial setting force, circulating end-resistance, time-weighted resistance, and spatial distribution condition of resistance, which reflect the support condition of the hydraulic support in intelligent mining working face. The pressure time-sequence and the pressure space-sequence are combined into the total space-time pressure matrix in 2D space-time region. Thirdly, according to the support requirements of the working face, the support quality of the space-time region into seven types, namely, support quality preliminary deterioration, support quality continuous deterioration, support quality deep deterioration, support quality general maintenance, support quality preliminary optimization, support quality continuous optimization and support quality good maintenance. On the total space-time pressure matrix, the sliding window is used to intercept the sub-matrix with the given size at a certain interval. The sub-matrix is one-to-one corresponding to the seven support quality types in space-time regions to form samples and labels. Finally, the samples and the labels are input into the improved LeNet-5 network for training. The dynamic evaluation model of support quality of hydraulic support in space-time region is constructed, which evalues the support condition of hydraulic support in the region in real-time. The experimental results show that the model based on improved LeNet-5 network can be used to identify the dynamic effect of support quality in the working face, and provide the basis for the field operators to adjust the support state of the hydraulic support pertinently. The classification accuracy is 85.25%, which is 12% higher than that of the model based on LeNet-5 network. At the same time, the improved LeNet-5 network can converge to the optimal solution quickly in the training process, which accelerates the training speed of the network. The result verifies the advantages of the improved LeNet-5 network in evaluation of the support quality of hydraulic support in space-time region of intelligent working face

Analysis of transcripts and splice isoforms in red clover (Trifolium pratense L.) by single-molecule long-read sequencing

Abstract Background Red clover (Trifolium pratense L.) is an important cool-season legume plant, which is the most widely planted forage legume after alfalfa. Although a draft genome sequence was published already, the sequences and completed structure of mRNA transcripts remain unclear, which limit further explore on red clover. Results In this study, the red clover transcriptome was sequenced using single-molecule long-read sequencing to identify full-length splice isoforms, and 29,730 novel isoforms from known genes and 2194 novel isoforms from novel genes were identified. A total of 5492 alternative splicing events was identified and the majority of alter spliced events in red clover was corrected as intron retention. In addition, of the 15,229 genes detected by SMRT, 8719 including 186,517 transcripts have at least one poly(A) site. Furthermore, we identified 4333 long non-coding RNAs and 3762 fusion transcripts. Conclusions We analyzed full-length transcriptome of red clover with PacBio SMRT. Those new findings provided important information for improving red clover draft genome annotation and fully characterization of red clover transcriptome