Analyzing point cloud of coal mining process in much dust environment based on dynamic graph convolution neural network.

Environmental perception is an important research direction of coal mine sustainable development. There is much dust in the underground working environment of coal mine. This study is to identify the marker (ball) in the coal mine, which provides a basic to convert the coordinate of large-scale fully mechanized mining face point cloud to the geodetic coordinate. Firstly, in the face of the phenomenon that the uneven distribution of underground point cloud is more serious, this study further has studied on the basis of complete and incomplete geometry point cloud and generated multi-density geometry point cloud for the first time. Secondly, aiming at the problem that the geometric features of underground point cloud are not obvious enough, this study has increased the weight of point cloud normal vector in the training process of network model, so that the network model is more sensitive to different geometric features. Finally, this study has used a variety of advanced deep neural networks to directly analyze point clouds to verify the proposed method. The results show that the method proposed in this study has been combined with the dynamic graph convolution neural network (DGCNN) established earlier, which can more accurately identify the ball in tens of millions of the point clouds of coal mining process. Most importantly, this work is not only of great significance to improve the production efficiency and safety in fully mechanized mining face but also lays a foundation for realizing intelligence in the mining field and avoiding the harm of dust explosion and other accidents to workers.

Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks.

The intersection line information of the point cloud between the coal wall and the roof can not only accurately reflect the direction information of the scraper conveyor but also provide a preliminary basis for realizing the intelligent coal mine. However, the indirect method of using deep learning to segment the point cloud of coal mine working face cannot make full use of the rich information provided by the point cloud data. The direct method of using deep learning to segment the point cloud ignores the local feature relationship between points. Therefore, we propose to use dynamic graph convolution neural networks (DGCNNs) to segment the point cloud of the coal wall and roof so as to obtain the intersection line between them. First, in view of the characteristics of heavy dust and strong electromagnetic interference in the environment of the coal mine working face, we have installed an underground inspection robot so that we use light detection and ranging to obtain the point cloud of the coal mine working face. At the same time, we put forward a fast labeling method of the point cloud of the coal mine working face and an efficient training method of the depth neural network. Second, on the basis of edge convolution, being the greatest innovation of DGCNNs, we analyze the influence of the number of layers, K value, and output feature dimension of edge convolution on the effect of DGCNNs segmenting the point cloud of the coal mine working face and obtaining the intersection line of the coal wall and roof. Finally, we compare DGCNNs with PointNet and PointNet++. The results show that the DGCNN exhibits the best performance. What is more, the results provide a research foundation for the application of DGCNNs in the field of energy. Last but not least, the research results provide a direct and key basis for the adjustment of the scraper conveyor, which is of great significance for an intelligent coal mine working face and accurate construction of a geological information model.

Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques.

This article investigates global asymptotic stability for neural networks (NNs) with time-varying delay, which is differentiable and uniformly bounded, and the delay derivative exists and is upper-bounded. First, we propose the extended secondary delay partitioning technique to construct the novel Lyapunov-Krasovskii functional, where both single-integral and double-integral state variables are considered, while the single-integral ones are only solved by the traditional secondary delay partitioning. Second, a novel free-weight matrix equality (FWME) is presented to resolve the reciprocal convex combination problem equivalently and directly without Schur complement, which eliminates the need of positive definite matrices, and is less conservative and restrictive compared with various improved reciprocal convex inequalities. Furthermore, by the present extended secondary delay partitioning, equivalent reciprocal convex combination technique, and Bessel-Legendre inequality, two different relaxed sufficient conditions ensuring global asymptotic stability for NNs are obtained, for time-varying delays, respectively, with unknown and known lower bounds of the delay derivative. Finally, two examples are given to illustrate the superiority and effectiveness of the presented method.

Quantized Sampled-Data Control for T-S Fuzzy System Using Discontinuous LKF Approach.

In this study, the stability for a class of sampled-data Takagi-Sugeno (T-S) fuzzy systems with state quantization was investigated. Using the discontinuous Lyapunov-Krasoskii functional (LKF) approach and the free-matrix-based integral inequality bounds processing technique, a stability condition with less conservativeness has been obtained, and the controller of the sampled-data T-S fuzzy system with the quantized state has been designed. Furthermore, based on the results, the sampled-data T-S fuzzy system without the state quantization was also discussed, and the required controller constructed. The results of two simulation examples show that both the maximum sampling intervals, with and without the quantized state for T-S fuzzy systems, are actually superior to the existing results.

A consensus-based multi-agent approach for estimation in robust fault detection.

This paper is devoted to distributed estimation in robust fault detection for sensor networks with networked-induced delays and packet dropouts by using a consensus-based multi-agent approach. Utilizing the information interaction and coordination among the neighboring networks based on multi-agent theory, we design novel and multiple agent-based robust fault detection filters (RFDFs) subject to only partial estimated and measured information. Asymptotically stable sufficient conditions for the innovative constructed filters are derived in the form of linear matrix inequality (LMI) and the threshold fit for each agent-based RFDF is determined. An illustrative example is given to demonstrate the effectiveness of the consensus-based multi-agent approach for the estimation in robust fault detection.