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Journal of Mountain Science 2014年第3期11卷 578-591页

作者： Hosung KIM Seung Woo LEE Chan-Young YUNE Gihong KIM Department of Civil Engineering Gangneung-Wonju National University

This paper describes a geographic information system(GIS)-based method for observing changes in topography caused by the initiation, transport, and deposition of debris flows using highresolution light detection and ranging(LiDAR) digital elevation models(DEMs) obtained before and after the debris flow events. The paper also describes a method for estimating the volume of debris flows using the differences between the LiDAR DEMs. The relative and absolute positioning accuracies of the LiDAR DEMs were evaluated using a real-time precise global navigation satellite system(GNSS) positioning method. In addition, longitudinal and cross-sectional profiles of the study area were constructed to determine the topographic changes caused by the debris flows. The volume of the debris flows was estimated based on the difference between the LiDAR DEMs. The accuracies of the relative and absolute positioning of the two LiDAR DEMs were determined to be ±10 cm and ±11 cm RMSE, respectively, which demonstrates the efficiency of the method for determining topographic changes at an scale equivalent to that of field investigations. Based on the topographic changes, the volume of the debris flows in the study area was estimated to be 3747 m3, which is comparable with the volume estimated based on the data from field investigations.

关键词： Debris flow Topographic change LiDAR DEM volume estimation Global navigation satellitesystem (GNSS)

Coal/Gangue volume estimation with Convolutional Neural Network and Separation Based on Predicted volume and Weight

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Computers, Materials & Continua 2024年第4期79卷 279-306页

作者： Zenglun Guan Murad S.Alfarzaeai Eryi Hu Taqiaden Alshmeri Wang Peng School of Mechatronic Engineering China University of Mining and TechnologyXuzhou221116China China National Coal Group Corp. Beijing100029China Information Institute Ministry of Emergency Management of the ChinaBeijing100029China School of Mechanical Engineering Sana’a UniversitySana’a31220Yemen

In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and ***,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for *** such method exploits the difference in substancedensity,leading to excellent coal/gangue ***,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the *** training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the *** prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the *** classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,*** test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was *** useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained ***,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),*** obtaining a classification with hi

关键词： Coal coal gangue convolutional neural network CNN object classification volume estimation separation system

Fast estimation of Loader’s Shovel Load volume by 3D Reconstruction of Material Piles

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Chinese Journal of Mechanical Engineering 2023年第5期36卷 187-205页

作者： Binyun Wu Shaojie Wang Haojing Lin Shijiang Li Liang Hou Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361102China Shenzhen Research Institute of Xiamen University Shenzhen 518057China

Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

关键词： Loader volume estimation Binocular stereo vision 3D terrain reconstruction Point cloud registration and segmentation

estimation OF ROCK-AGGREGATE volume BASED ON PCA AND LM-OPTIMIZED NEURAL NETWORK

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Journal of Electronics(China) 2009年第6期26卷 825-830页

作者： Zhao Pan Chen Ken Wang Yicong Zhang Yun College of Information Science and Engineering Ningbo University Ningbo 315211 China

In granule processing industries, acquisition of particle size and shape parameters is a common procedure, and volumetric measurement is of great importance in dealing with particle sizing and gradation. To eradicate the major drawbacks with manual gauge, this paper proposes an optical approach using Back Propagation （BP） neural network to estimate the particle volume based on the two-Dimensional （2D） image information. To achieve the better network efficiency and structure simplicity, Principal Component Analysis （PCA） is adopted to reduce the dimensions of network inputs To overcome the shortcomings of generic BP network for being slow to converge and vulnerable to being trapped in local minimum, Levenberg-Marquardt （LM） algorithm is applied to achieve a higher speed and a lower error rate. The real particle data is utilized in training and testing the presented network. The experimental result suggests that the proposed neural network is capable of estimating aggregate volume with satisfactory precision and superior to the generic BP network in terms of perforxnance capacity.

关键词： Particle image Particle parameters Principal Component Analysis (PCA) Neuralnetwork volume estimation

What diameter?What height?Influence of measures of average tree size on area-based allometric volume relationships

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Forest Ecosystems 2024年第1期11卷 100-109页

作者： Yilin Wang John A.Kershaw Mark J.Ducey Yuan Sun James B.McCarter Integrated Forest Mensuration and Systems Analysis Laboratory Faculty of Forestry and Environmental ManagementUniversity of New BrunswickFrederictonNBCanada Department of Natural Resources and the Environment University of New HampshireDurhamNHUSA College of Forestry Nanjing Forestry UniversityNanjing210037China Scientific Programmer RayonierYuleeFLUSA

volume is an important attribute used in many forest management *** from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume *** density was included in the models(volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height *** density was not included(volume=f(Diameter,Height)),the opposite was *** models with density included,moment-based estimators of stand diameter and height performed better than all other *** models without density,largest tree estimators of stand diameter and height performed better than other *** overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

关键词： Allometric relationships Stand structure volume estimation Stand-level attributes

Optimizing the dataset size of a topo-bathymetric survey for Hammam Debagh Dam, Algeria

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International Journal of Sediment Research 2018年第4期33卷 518-524页

作者： Djamel Bengora Lotfi Khiari Jacques Gallichand Noureddine Dechemi Silvio José Gumiere Department of Hydraulics Polytechnic National School Department of Soil and Agrifood Engineering Laval University

The capacity of water reservoirs may be considerably reduced by sediment transport and *** phenomenon may occur quickly in regions prone to erosion, such as Hammam Debagh Dam region in Algeria. For better management of water resources, it is important to estimate the volume of a reservoir by regular and frequent topo-bathymetric surveys. Topo-bathymetric surveys with a high point measurement density are expensive and time-consuming. To reduce this expense and to increase the frequency of topo-bathymetric surveys, it is required to optimize their size and density. The complete survey of the reservoir created by the Hammam Debagh Dam in Algeria was examined in comparison with 19 reduction subsets using geostatistics. The study showed that the complete dataset of 32,164 points, corresponding to a density of 42 points ha-1could be reduced to 10,000 points, i.e. 13 points ha-1,without affecting the estimation of the water volume in the reservoir. This reduction could considerably simplify the work load, reduce the expenses to do these surveys, and, therefore, allow for an increased frequency of topo-bathymetric surveys.

关键词： Dam Reservoir Silting volume estimation Point measurement density volume/elevation curve