A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

Bp-miR408a participates in osmotic and salt stress responses by regulating BpBCP1 in Betula platyphylla.

The microRNAs, which are small RNAs of 18-25 nt in length, act as key regulatory factors in posttranscriptional gene expression during plant growth and development. However, little is known about their regulatory roles in response to stressful environments in birch (Betula platyphylla). Here, we characterized and further explored miRNAs from osmotic- and salt-stressed birch. Our analysis revealed a total of 190 microRNA (miRNA) sequences, which were classified into 180 conserved miRNAs and 10 predicted novel miRNAs based on sequence homology. Furthermore, we identified Bp-miR408a under osmotic and salt stress and elucidated its role in osmotic and salt stress responses in birch. Notably, under osmotic and salt stress, Bp-miR408a contributed to osmotic and salt tolerance sensitivity by mediating various physiological changes, such as increases in reactive oxygen species accumulation, osmoregulatory substance contents and Na+ accumulation. Additionally, molecular analysis provided evidence of the in vivo targeting of BpBCP1 (blue copper protein) transcripts by Bp-miR408a. The overexpression of BpBCP1 in birch enhanced osmotic and salt tolerance by increasing the antioxidant enzyme activity, maintaining cellular ion homeostasis and decreasing lipid peroxidation and cell death. Thus, we reveal a Bp-miR408a-BpBCP1 regulatory module that mediates osmotic and salt stress responses in birch.

LMFD: lightweight multi-feature descriptors for image stitching.

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

Channel power equalization based on joint optimization of EDFA and ROADM configuration in open optical network.

Deterioration of the signal-to-noise ratio (SNR) is an important challenge in ultra-long multi optical line system (OLS) optical transmission systems. The non-uniform gain and cascading of the Erbium-doped fiber amplifier (EDFA) lead to SNR deterioration in transmission systems. In this paper, we propose two channel power equalization methods based on joint optimization of EDFA and Reconfigurable optical add-drop multiplexer (ROADM) configurations: 1) reinforcement learning (RL)-based channel power equalization (RL-PE) and 2) covariance matrix adaptive evolution strategy (CMA-ES) channel power equalization (CMA-PE). The simulation results indicate that the power equalization effect was improved by 1.9 dB through the CMA-PE method, while the RL-PE method led to a 1.5 dB improvement in an ultra-long 80-channel 7-OLS transmission system.