Alpha Shapes Algorithm Research Articles

A point cloud preprocessing method for complicated thin-walled ring parts with irregular section

Metallic W-ring is a typically complicated thin-walled ring part with irregular section (CTWRPIC). However, there is a lack of effective measurement methods to inspect its geometric quality in the forming process, which leads to the failure of finding the forming defects in time. Due to the smooth metal surface, complex section shape, small section size and annular closed overall structure, point cloud of the section profile obtained by the line laser scanners has serious noise, outliers and missing. To this problem, this article proposes a point cloud preprocessing method for CTWRPIC. In this method, historical measurement data based outliers statistics, initial-fine rigid registration and non-rigid registration of CAD model are introduced. Firstly, the optimal alignment of point cloud from the CAD model and measurement point cloud is realized by initial-fine rigid registration. Secondly, the point cloud of CAD model is transformed by non-rigid registration to approximate the measurement profile points, the transformed point cloud of CAD model is considered as the real profile. Thirdly, based on the transformed point cloud of CAD model and distance threshold, the adaptive removal of outliers and repair of missing are realized. Finally, the correction of profile point cloud noise is completed by combining the Alpha Shapes algorithm and the proposed repair method. Based on the proposed preprocessing method and a measurement platform equipped with line laser scanners, an on-machine measurement method for metallic W-ring is proposed, and experimental results demonstrate the advantage of the proposed method. Compared with the destructive inspection method, the mean value, standard deviation and root mean square of the distance deviation of the preprocessed point cloud are all within 11 μm.

Research on anti-interference and training set updating ability of fault diagnosis model of high voltage common rail system

In the fault diagnosis of high-pressure common rail diesel engine, the problem of low accuracy of fault diagnosis is caused by the inability to classify untrained state problems and training sets. To solve the above problems, an anti-interference classification model for fault diagnosis of high-voltage common rail system based on alpha shapes algorithm and K-means algorithm is proposed in this paper. This model can improve the anti-interference ability and training set updating ability of the fault diagnosis model. Using the anti-interference classification model composed of anti-jamming device and classifier, the samples for fault diagnosis are screened in advance, and the singular values of untrained state and trained state are classified. By constructing an anti-interference classification model composed of anti-interference device and classifier, the fault is diagnosed, and then the dependency of singular values is obtained through cluster analysis, which improve the anti-interference ability and training set updating ability of the fault diagnosis model. By comparing the diagnosis results of the fault diagnosis model with and without anti-interference classifier, we can found that the addition of anti-interference classifier makes the fault diagnosis model of high-voltage common rail system obtain the anti-interference ability to the untrained state and the ability to update the training set of the singular value of the trained state, and can slightly improve the accuracy of fault diagnosis.

High-fidelity modeling of 3D woven composites considering inhomogeneous intra-yarn fiber volume fractions

The accuracy of strength prediction of 3D woven composites is strongly dependent on the quality of the utilized mesoscale geometry model. This paper aims at presenting a novel high-fidelity modeling approach for 3D woven composites considering both realistic mesoscale architectures and inhomogeneous intra-yarn fiber volume fractions (iy-FVF) along the yarn path. The unit cell model is numerically reconstructed from simulated results of digital element analysis. An Alpha-shapes algorithm was utilized to extract the shape of yarn cross-sections, and a digital-element-chain tracing method was established to avoid artificial inaccurate torsion deformation in the yarn geometry. The inhomogeneity of iy-FVF along the yarn path was specially characterized, and its effect on the mechanical performance of the composite was analyzed based on the progressive damage model. Furthermore, the proposed numerical method was employed to investigate the influence of the weft tow size on the tensile properties of the composite. It is shown that the predicted results of the high-fidelity model agree well with experiments. Iy-FVF varies significantly in warp yarns of the investigated 3D woven composite. Considering the inhomogeneous iy-FVF, predicted warp stiffness and strength increase about 10%, leading to higher predictive accuracy.

Grid Partition Variable Step Alpha Shapes Algorithm

On the basis of Alpha Shapes boundary extraction algorithm for discrete point set, a grid partition variable step Alpha Shapes algorithm is proposed to deal with the shortcomings of the original Alpha Shapes algorithm in the processing of nonuniform distributed point set and multiconcave point set. Firstly, the grid partition and row-column index table are established for the point set, and the point set of boundary grid partition is quickly extracted. Then, the average distance of the k -nearest neighbors of the point is calculated as the value of α . For the point set of boundary grid partition extracted in the previous step, Alpha Shapes algorithm is used to quickly construct the point set boundary. The proposed algorithm is verified by experiments of simulated point set and measured point set, and it has high execution efficiency. Compared with similar algorithms, the larger the number of point sets is, the more obvious the execution efficiency is.

A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground-Based LiDAR Data

Earthquake disasters can have a serious impact on people’s lives and property, with damage to buildings being one of the main causes of death and injury. A rapid assessment of the extent of building damage is essential for emergency response management, rescue operations, and reconstruction. Terrestrial laser scanning technology can obtain high precision light detection and ranging (LiDAR) point cloud data of the target. The technology is widely used in various fields; however, the quantitative analysis of building seismic information is the focus and difficulty of ground-based LiDAR data analysis processing. This paper takes full advantage of the high-precision characteristics of ground-based LiDAR data. A triangular network vector model (TIN-shaped model) was created in conjunction with the alpha shapes algorithm, solving the problem of small, nonvisually identifiable postearthquake building damage feature extraction bias. The model measures the length, width, and depth of building cracks, extracts the amount of wall tilt deformation, and labels the deformation zone. The creation of this model can provide scientific basis and technical support for postearthquake emergency relief, assessment of damage to buildings, extraction of deformation characteristics of other structures (bridges, tunnels, dams, etc.), and seismic reinforcement of buildings. The research data in this paper were collected by the author’s research team in the first time after the 2013 Lushan earthquake and is one of the few sets of foundation of LiDAR data covering the full range of postearthquake building types in the region, with the data information mainly including different damage levels of different structural types of buildings. The modeling analysis of this data provides a scientific basis for establishing the earthquake damage matrix of buildings in the region.

A Coarse-to-Fine Strip Mosaicing Model for Airborne Bathymetric LiDAR Data

The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is an extension of the ubiquitous topographic LiDAR mapping system and has been most simply characterized as adding a green laser to the infrared laser of topo systems. Due to the low point cloud density and monotonous objects in the scene, it is difficult to mosaicing the ALB strips. Therefore, the existing airborne laser scanning strip stitching algorithm has poor performance for ALB strips. In this article, a coarse-to-fine strip mosaicing model for ALB is proposed. The framework is fast and efficient and can handle large ALB data. An improved alpha shapes algorithm can fast and accurately determine the overlap region of strip is applied. Due to different data accuracy and spatial characteristics, the water area and land area are processed separately. A weight distribution-based coarse-to-fine registration model is designed for underwater areas. The topological constraint term is added to the nonrigid iterative closest point (ICP) cost function to prevent excessive deformation caused by outliers. The implicit B-spline surface fitting algorithm using the 3L algorithm and the least-squares trend surface fitting algorithm are applied separately to assign weights for overlapping strips to solve the limitation of no control or less control. Moreover, a random sample consensus (RANSAC)-ICP registration model characterized by the normal vector and curvature is constructed for land area. Finally, the comparisons with ICP highlight the superiority of the proposed approach in flexibility and accuracy. The root-mean-square error (RMSE) is 0.12 m and the maximum error is 0.36 m.

Single Ping Filtering of Multi-Beam Sounding Data Based on Alpha Shapes

AbstractFrom the standpoint of complex marine environments relative to underwater acoustic signal propagation, the application of multi-beam systems for the filtering and processing of multi-beam sounding data is critical. However, currently existing automatic filtering methods estimate data, which involve several calculations and require the respective computer to possess significant processing capabilities. In this study, raw data are measured using an interferometer multi-beam echo sounder, and the characteristics of the noise data are analyzed. The noise data are discontinuous; however, accurate data can be approximated as a continuous distribution. Under the assumption of continuous underwater terrain, in consideration of a circle of the appropriate radius rolling on the terrain profile, the continuous underwater terrain data can be extracted from the raw data by means of the alpha-shapes algorithm. Finally, on the basis of the measured data in a sail trial, the effectiveness of the proposed algorithm is verified.

Entropy Evaluation Method for Sandstone Uranium Reservoir Characteristics Based on Convex Hull Search

The continuity and homogeneity of sandstone uranium deposits are inherent characteristics that affect in situ leaching performance indexes and have important guiding effects on the optimization of mining design. In this work, we propose a spatial information entropy calculation method based on a convex hull search strategy to quantitatively evaluate the geological characteristics of uranium deposits. We use the 3-D block model of sandstone-hosted uranium deposits as hard data. First, we use the Alpha-Shapes algorithm to calculate the convex hull of the space area where various types of unit blocks are located. Second, we formulate a suitable multi-point configuration based on the convex hull size. Then, we use the multi-point density function. Scanning the convex hull point cloud establishes a probability density function to calculate the local information entropy of various sub-regions. Finally, the local spatial information entropy is aggregated using addition and multiplication to obtain the joint spatial information entropy of the entire evaluation area. A series of parameter sensitivity and performance analysis experiments prove the stability of the method and the accuracy of the quantitative and homogeneous evaluations of the complex geological conditions. In order to solve the problem of the difference between the designed production capacity and actual production capacity of a uranium deposit, we use a new method to evaluate the homogeneity and continuity of the lithology and grade of the deposit. The new method and evaluation conclusions can be used for the automation and understanding of future engineering optimization processes.

Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests

Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

Automated Detection of 3D Roof Planes from Lidar Data

The purpose of this study is to derive vectoral 3D roof planes from the LIDAR point cloud of the detected buildings. For segmentation of the LIDAR point cloud, the RANSAC algorithm has been used. Because the RANSAC algorithm is sensitive to the used parameters, and results in over- or under-segmentation of the clusters, a refinement method has been proposed. The detection of roof planes has been improved with use of the refinement method. Therefore, similar plane surfaces have been combined, followed by the region-growing algorithm, to split the under-segmented plane surfaces. The digitization of the roof boundaries is performed using the alpha-shapes algorithm, followed by line fitting to generalize the roof edges. The quality assessment has been done using the reference vector dataset with comparison using four different criteria.

Computing concave hull with closed curve smoothing: performance, concaveness measure and applications

Presentation of large set of points as clusters is a common problem in geoinformatics applications. One of the approach that can be used for this task is Alpha Shapes algorithm. In this study the Alpha Shapes algorithm was implemented with two modifications. The first one is creation of a new data-adaptive parameter for regularization of shape’s concavenessety. The second one is splining for reduction of shapes complexity. Also performance of implementation is considered in this study.

EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

Abstract. Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

Automated extraction of ground surface along urban roads from mobile laser scanning point clouds

ABSTRACTExtracting ground surface from high-density point clouds collected by Mobile Laser Scanning (MLS) systems is of vital importance in urban planning and digital city mapping. This article proposes a novel approach for automated extraction of ground surface along urban roads from MLS point clouds. The approach, which was designed to handle both ordered and unordered MLS point clouds, consists of three key steps: constructing vertical profile from MLS point clouds along the vehicle trajectory; extracting candidate ground points using an adaptive alpha shapes algorithm; refining the candidate ground points with an elevation variance filter. To evaluate the performance of the proposed method, experiments were conducted using two types of urban street-scene point clouds. The results reveal that the ground points can be detected with an error rate of as low as 1.9%, proving that our proposed method offers a promising solution for automated extraction of ground surface from MLS point clouds.