Search

Abstract Rock burst disasters severely restrict the safe and efficient mining of coal. The fundamental cause of their occurrence is the concentration of stress within the coal mass. Stress monitoring in coal seam drilling is widely used as an effective method for rock burst monitoring. However, how to scientifically and reasonably set the critical values of early warning indicators that match the conditions of each mine has always been a key issue restricting the accurate prediction of rock burst by the drilling stress method. This paper adopts a method combining theoretical analysis and field practice to conduct research on the critical values of drilling stress early warning indicators. Based on perturbation response instability theory, a mechanical model for the occurrence of impact ground pressure has been established. Based on the instability theory of disturbance response, a mechanical model for the occurrence of impact ground pressure has been established, leading to the derivation of the expression for the near-field critical stress of impact ground pressure events. The theoretical formula for the critical value of drilling stress early warning indicators was obtained based on the difference between the critical stress of rock burst occurrence and the actual stress in the roadway. This formula includes the mechanical parameters of the coal mass and its propensity for rock burst, roadway support stress, mining depth, stress concentration coefficient, and the initial installation pressure of the stress gauge. They can be determined by the geological and mining technical conditions of each mine. This theoretical formula breaks the uniformity of the critical values for stress warning indicators in various mine drill holes, allowing each mine to scientifically determine its critical value based on its own conditions. This theoretical method has been applied to a high-stress mine in Shanxi, China, and the critical values of drilling stress early warning indicators were obtained. When the monitored stress exceeded the critical value, dynamic phenomena of anchor rod and cable fractures occurred in the roadway roof. The distribution of microseismic events also shifted towards the warning area, and the microseismic monitoring indicators reached the warning values. This confirmed the engineering feasibility of the critical values for drilling stress early warning indicators determined by the theoretical method.

Abstract Water from hydraulic technology affects the desorption of gas from coal seams. Gas desorption behavior is critical information for gas control in coal mines. In this study, a designed coal seam water injection simulation experimental device was utilized to conduct dynamic water injection experiments on coal samples at different adsorption equilibrium pressures, analyzing the gas desorption law under dynamic water injection, as well as the role of water replacement, water imbibition and water blockage in gas desorption. The results showed that water altered the gas desorption rate in coal, causing fluctuating attenuation of the desorption rate of a water-injected coal sample (WCS). Under the same adsorption equilibrium pressure, the relationship between the desorption rate of the WCS and the non-water-injected coal samples (NCS) underwent a transition in desorption time. In contrast to the NCS desorption curves, the WCS desorption curves lacked a rapid growth phase and exhibited only a slow growth phase and a stopping phase. Water imbibition and water replacement promoted the desorption of gas in the non-wet area during the water injection process, while it inhibited the desorption of gas in the wet area. Under the effects of water imbibition, water blockage, and water replacement, the discharge rate of WCS is greater than the desorption rate of NCS, indicating that water injection increases the total amount of gas desorption. The study results have significant implications for gas extraction and the prevention and control of coal and gas outbursts.

Abstract Indoor testing are performed to explore the charge induction law during the uniaxial compression fracture process of coal samples, and the charge time and frequency domain signals of coal samples with different primary fissures are analyzed in the paper. On-site monitoring of charge in different fissures distribution areas of underground coal tunnels, and the charge signals of different drillingdepths in coal seams are analyzed. The results show that the uniaxial compressive strength and elastic modulus of multi-fissured coal samples are less than those of less fissured coal samples, and the Poisson’s ratio is greater than those of less fissured coal samples. The charge induction signal intensity during the fracture process of multi-fissured coal samples is relatively low, but it is concentrated at the low frequency of 0–50 Hz in the compacting elasticity stage. The charge signal intensity during the fracture process of coal samples with less fissure is relatively high, and the charge frequency during the reinforcement damage stage is concentrated at a low frequency of 0–50 Hz. Therefore, the sudden appearance of low-frequency charge signals is more suitable as effective precursor information for the instability and failure of less fissured coal bodies. The average charge intensity is small in the multi-fissured area with a drilling depth of 1–4 m in the coal seam, and the average charge intensity of the coal body with less fissures is larger in the 5–12 m region. The on-site charge monitoring results have good consistency with the indoor test results. This study has guiding significance in setting up a charge monitoring warning index of instability failure in different coal body fissures regions.

With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51.

Because of the lack of comprehensive evaluation on the control design reliability and their control ability of rockburst in current rockburst mines, the rockburst still occurs in some mines although the rockburst control measures have been taken. Based on the method of evaluating the safety degree by safety factor in geotechnical engineering, the basic idea of rockburst mine safety is put forward. Through site investigations, it is found that the damage area caused by rockburst is limited in proportion to the total working area of the working face. The stress concentration in the surrounding rock of mine roadway is limited, ranging from 1 to 6. The energy released by rockburst accidents is no more than 109 J level, that is, the energy released by rockburst is limited. The safety evaluation of rockburst mine can be transformed into roadway safety evaluation, that is, the safety of roadway can ensure the safety of the whole mine. After rock burst occurs, controlling the shrinkage rate of roadway section within 20% can basically avoid casualties. Therefore, it is pointed out that rockburst can be prevented and controlled. Therefore, the conditions of non-occurrence of rockburst and occurrence of no casualties are put forward, that is, the actual load stress of roadway surrounding rock deformation system is lower than the critical stress of rockburst when roadway instability occurs and the energy released by the instability of the surrounding rock deformation system is lower than that absorbed by the surrounding rock support system. Safety evaluation should be carried out after impact risk evaluation, hazard classification and control design of rockburst, which includes critical condition, calculation of safety factor and classification of safety grade. Based on the instability theory of rockburst disturbance response, two aspects that need to be considered in safety evaluation are given: stress safety and energy safety. The stress safety is determined by the ratio of the actual stress under load to the critical stress of buckling in the surrounding rock deformation system (i.e. the stress safety factor). The energy safety is determined by the ratio of energy released from the instability of surrounding rock deformation system to the energy absorbed by surrounding rock supporting system (i.e. energy safety factor). Furthermore, the mine safety is divided into three levels: A, B and C, corresponding to safety, basic safety and unsafe respectively. Among them, if the stress safety factor is greater than 1.5 and the energy safety factor is greater than 1.0, it is a Grade A safe mine. The mine has control ability of rockburst and can be mined normally. If the stress safety factor is greater than 1.0 and the energy safety factor is greater than 1.5, it is a Grade B basic safety mine. The mine has control ability of rockburst and can be mined normally. If the safety factor is not Grade A or Grade B, it is classified as Grade C unsafe mine. The mine does not have control capability of rockburst or mining activities should be suspended. Taking a mine as an example, the complete flow of mine safety calculation and evaluation results are given. Finally, the stress safety factor and energy safety factor of some coal mines in China are given. Combined with the actual occurrence of rockburst in these mines, the reference of the calculation results of safety factor is verified, which shows that the safety evaluation method has certain reliability and practicability.

In recent years, the research of autonomous driving and mobile robot technology is a hot research direction. The ability of simultaneous positioning and mapping is an important prerequisite for unmanned systems. Lidar is widely used as the main sensor in SLAM (Simultaneous Localization and Mapping) technology because of its high precision and all-weather operation. The combination of Lidar and IMU (Inertial Measurement Unit) is an effective method to improve overall accuracy. In this paper, multi-line Lidar is used as the main data acquisition sensor, and the data provided by IMU is integrated to study robot positioning and environment modeling. On the one hand, this paper proposes an optimization method of tight coupling of lidar and IMU using factor mapping to optimize the mapping effect. Use the sliding window to limit the number of frames optimized in the factor graph. The edge method is used to ensure that the optimization accuracy is not reduced. The results show that the point plane matching mapping method based on factor graph optimization has a better mapping effect and smaller error. After using sliding window optimization, the speed is improved, which is an important basis for the realization of unmanned systems. On the other hand, on the basis of improving the method of optimizing the mapping using factor mapping, the scanning context loopback detection method is integrated to improve the mapping accuracy. Experiments show that the mapping accuracy is improved and the matching speed between two frames is reduced under loopback mapping. However, it does not affect real-time positioning and mapping, and can meet the requirements of real-time positioning and mapping in practical applications.

Aiming at the problem that the model of YOLOv4 algorithm has too many parameters and the detection effect of small targets is poor, this paper proposes an improved helmet fitting detection model based on YOLOv4 algorithm. Firstly, this model improves the detection accuracy of small targets by adding multi-scale prediction and improving the structure of PANet network. Then, the improved depth-separable convolution was used to replace the standard 3 × 3 convolution, which greatly reduced the model parameters without reducing the detection ability of the model. Finally, the k_means clustering algorithm is used to optimize the prior box. The model was tested on the self-made helmet dataset helmet_dataset. Experimental results show that compared with the safety helmet detection model based on Faster RCNN algorithm, the improved YOLOv4 algorithm has faster detection speed, higher detection accuracy and smaller number of model parameters. Compared with the original YOLOv4 model, the mAP of the improved YOLOv4 algorithm is increased by 0.49%, reaching 93.05%. The number of model parameters was reduced by about 58%, to about 105 MB. The model reasoning speed is 35 FPS. The improved YOLOv4 algorithm can meet the requirements of helmet wearing detection in multiple scenarios.

Visual attention mechanism has been widely used in computer vision and plays a vital role in the research of human action recognition. In this paper, we explore a novel moving target detection mechanism for human action recognition and propose a new 3D CNN (3D Convolutional Neural Network) model, dubbed LaM-2SRN (Local Features Enhanced and Moving target detected 2Stream-ResNet) for extracting and learning attention-enhanced spatiotemporal features. The contributions of this paper are as follow: First, the traditional CAM (Class Activation Map) based visual attention algorithm is used to obtain the optical flow information of the human region, thus eliminating the influence of irrelevant optical flow information (such as background clutter). Second, the ViBe algorithm is used to identify the moving target in the continuous frame, retain the optical flow information of the moving target, and make it complement with the optical flow information of the human region, to obtain a complete motion descriptor. After the motion information is marked in the video frame, we put the marked video frames and the original video frames into the 3D CNN and 2D CNN models respectively, to acquired mixed descriptor, which serves as the basis for video classification. Different from most of the previous target detection algorithms, this paper not only detects the human target, but also detects the moving foreground targets in the video, so that the pre-trained CNN model can obtain more complete motion information from videos. We use RGB-only video data for evaluation on two benchmarks: UCF101 and HMDB51, the experimental results demonstrate that our LaM-2SRN is comparable to the previous state-of-the-art algorithms.

It is of great significance to study the pattern of displacing methane gas with water in the coal seam water injection process for the prevention and control of gas disasters. A two-phase displacement test device was used to conduct experiments on the water injection of coal samples to displace methane gas under different water injection pressures, and the influence of the water injection pressure on the displacement was analyzed. The test results show that with the same displacement time, the ratio of displaced methane increases with the increase in the water injection pressure. The displacement rate increases with the increase in the water injection pressure at the stage without water discharge, while the water displacement rate under different water injection pressures differed slightly; in the later displacement stage, a higher water injection pressure of the coal sample results in a faster attenuation of the displacement rate and water displacement rate due to the decrease in the methane content in the coal sample. There is a critical water injection pressure to displace methane gas. When the critical water injection pressure is exceeded, the displacement time does not change appreciably. Flowing water can displace a large amount of methane gas in coal, and the methane displacement rate increases with the increase in the water injection pressure. The high solubility of methane in water is the reason for the high methane displacement rate. The results of this study can provide theoretical guidance for coal seam water injection to control gas disasters.