In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of ...tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust ...concentrations (200 ug/m3, 500 ug/m3 and 800 ug/m3), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing, this article proposed a convolution neural network (CNN) coal and rock identification ...method based on hyperspectral data. First, coal and rock spectrum data were collected by a near-infrared spectrometer, and then four methods were used to filter 120 sets of collected data: first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing. The coal and rock reflectance spectrum data were pre-processed to enhance the intensity of spectral reflectance and absorption characteristics, as well as effectively remove the spectral curve noise generated by instrument performance and environmental factors. A CNN model was constructed, and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations (i.e., the learning rate, the number of feature extraction layers, and the dropout rate) to generate the best CNN classifier for the hyperspectral data for rock recognition. The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%. Verification of the advantages and effectiveness of the method were proposed in this article.
A coal mine roadway is a longitudinally limited space with curves and branches, low illumination and high humidity, a large amount of dust, and an unstructured terrain environment. Traditional ICP ...algorithms have the defects of slow convergence speed and it is easy to fall into local optimums. While the NDT algorithm in the NDT + ICP algorithm has high registration efficiency, poor stability, and low registration accuracy, which are not suitable for point clouds with noise and a large amount of data. By calculating the FPFH value, the detailed description of the point cloud will be greatly increased to increase the robustness and accuracy Therefore, a feature registration method based on the FPFH + ICP algorithm is proposed to reduce the modeling error of excavation roadways and meet the requirements of intelligent excavation. First, outliers caused by dust are treated by the Euclidean clustering point cloud segmentation method, and then the calculation of the normal vector in the FPFH feature descriptor is optimized based on extracting key points from the roadway structure. The surface normal vector of each key point and its neighborhood point is estimated according to the measured point and its neighborhood point. The initial coordinate transformation matrix of a point cloud of an excavated roadway is obtained by the SAC-IA algorithm and transferred to the ICP algorithm. Finally, KD-tree is introduced into the ICP algorithm to accelerate the search speed of corresponding point pairs, and the Gauss–Newton method is used to optimize the solution of the nonlinear objective function of the algorithm to complete accurate registration of point clouds in an excavation roadway.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was ...proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
The method of laneway environment modeling and roadheader positioning based on Self-coupling and HectorSLAM was proposed to solve the problems of difficult extraction of environmental information of ...coal mine laneway and difficult determination of the position of roadheader and realization of autonomous mobilization of mine roadheader. The optimality of HectorSLAM was verified by experiments, and the deficiencies were pointed out. Then the self - coupling HectorSLAM algorithm was proposed. Finally, the self-coupling and Hector SLAM algorithms were run in ROS system. Environmental modeling of coal mine laneway was completed. The shaft bottom visualization positioning function of roadheader was realized. The comparative experiment proves that: Compared with the original algorithm, the self-coupling and Hector SLAM algorithms were more adaptive and more accurate in the simulated laneway environment.
Multi-interlayer composed of Ni and microcracked Cr (MC-Cr), formed by electrochemical deposition, was applied to induce the formation of wavy microstructure enhancing bonding strength of titanium to ...carbon steel brazed joints with BAg45CuZn filler. Effect of microcrack density in Cr layer on microstructure evolution and mechanical properties of joints has been studied. Phase identification and formation mechanism of joints were analyzed systematically. Introducing multilayer with microcracks avoided the formation of brittle Ti-C and Fe-Ti phases near carbon steel substrate. During bonding, the diffusion of Ti atoms was hindered by compact Cr while promoted by Ni at microcracks. The inhomogeneous diffusion of Ti atoms led to the generation of a wavy microstructure unit that is crater-shaped structure, accompanied by the enrichment of β-CuZn, Fe0.2Ni4.8Ti5 and Cu2TiZn phases. The fracture results showed that these structures improved the shear strength of joints up to 242 MPa by altering the direction of crack propagation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Properties of Ti/Steel DED-LB joints were enhanced by using BNi-1+Cr interlayer.•Reaction between Ni and Ti atoms was restricted by Cr addition.•Original deposition cracks were alleviated by Cr ...addition and preheating treatment.•Distribution of Ti-Ni-based IMCs affected failure mode of Ti/Steel DED-LB joints.
For searching alternative strategies to improve reliability of titanium and steel dissimilar bimetallic joints manufactured by directed energy deposition with laser beam (DED-LB), pure titanium was considered as cladding deposited on carbon steel substrate with Ni-based alloy interlayers in this work. Effect of different interlayer modification methods on the microstructure evolution and mechanical properties of joints was analyzed systematically. The distribution of intermetallic compounds (IMCs) such as β-Ti, Ti2Ni, TiNiFe0.2, Ti2Ni3Si and TiB2 in joints was revealed. The results showed that original deposition cracks caused by residual stress during processing could be alleviated by substrate preheating treatment while suppressed by the modified interlayer with Cr completely. Notably, additional Cr could reduce reaction activity between Ti and Ni atoms by raising laser molten pool liquidus, leading to fewer IMCs in joints. As a result, both bonding strength and toughness of joints were remarkably improved. The findings emphasize more significance of optimizing Ni-based interlayer composition with Cr than preheating method to improve the mechanical performance of DED-LB joints.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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