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Schematic diagram of the distribution of cables and the surrounding environment (Z max and Z min representing the maximum and minimum values of the elevation).
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In order to maintain a ski resort efficiently, regular inspections of the cableways are essential. However, there are some difficulties in discovering and observing the cable car cableways in the ski resort. This paper proposes a high-precision segmentation and extraction method based on the 3D laser point cloud data collected by airborne lidar to...
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Objective
To characterize the discs and vertebrae in detail over time in a group of adolescent individuals with varying training doses using magnetic resonance imaging (MRI).
Method
Thirty-five students were recruited from regular high schools (n=13) as well as schools with athlete competitive skiing programmes (n = 22). The thoraco-lumbar spine o...
Citations
Lidar (Light Detection and Ranging), utilizes laser based remote sensing data to measure distances and properties of objects by analysing reflected light, serving diverse applications. Lidar signals in the middle atmosphere face challenges like noise and atmospheric uncertainties. Lidar signal denoising and temperature retrievals are crucial for accurate measurements, improving data reliability in atmospheric research. Lidar signal denoising is the process of reducing unwanted noise or interference from Lidar data, enhancing accuracy and reliability. To address the issues Shuffle Attention with Encoder and Decoder based Deep Convolutional Neural Network (SAED-DCNN) is proposed. The Lidar signal undergoes preprocessing, including normalization, followed by convolution and pooling encoding in the autoencoding layer of SAED-DCNN. Leveraging Deep Convolutional Neural Network (DCNN) and encoder-decoder Framework, with shuffle attention, enhances spatial interactions for reducing Lidar signal noise. the introduction of enhanced spider wasp optimization addresses computational complexity, optimizing SAED-DCNN parameters, presenting an innovative Lidar signal enhancement approach. The suggested framework exhibits outstanding performance, achieving signal-to-noise ratio and root mean squared error of 28.135 dB and 68.113, respectively. These metrics underscores the mode's efficacy in overcoming denoising challenges, establishing it as a robust denoising solution.
Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.
