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Now showing items 1 - 10 of 10

  • Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

    Han, Liang   Yang, Guijun   Dai, Huayang   Xu, Bo   Yang, Hao   Feng, Haikuan   Li, Zhenhai   Yang, Xiaodong  

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    Double-channel processing method and apparatus for a picture-outside-picture (POP) or picture-in-picture (PIP) type television receiver. In the updating time period, the present broadcasting program signal is displayed on the secondary channel region continuously as a moving picture, and at the beginning of the next time period, the last frame of the present broadcasting program signal is displayed on the secondary channel region as a still picture, and the secondary channel is tuned to next channel so that the tuned present broadcasting program signal is displayed on the next secondary channel region as a moving picture. Once receiving user's channel selection, it stops picture updating of secondary channel region and superimposes markers of the broadcasting program signal onto the secondary channel regions respectively. When user selects a marker of the broadcasting program signal in the secondary channel region, the main channel region will be switched to display the broadcasting program signal corresponding to the selected channnel. It is convenient for users to browse all the broadcasting programs. For the POP or PIP type television receiver, user only need double keystrokes to switch the main channel to the desired television channel.
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  • Mechanism and calculation of surface discontinuous deformation (in Chinese) : Dai Huayang, Journal — China Coal Society, 20(6), 1995, pp 614–618

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  • Effects and Correction of Atmospheric Pressure Loading Deformation on GNSS Reference Stations in Mainland China

    Yue, Caiya   Dang, Yamin   Xu, Changhui   Gu, Shouzhou   Dai, Huayang  

    Atmospheric pressure loading (APL) deformation is one component of nontectonic deformation for Global Navigation Satellite System (GNSS) time series and is a kind of deformation response caused by a redistribution of atmospheric pressure. In this paper, we present an atmospheric data processing strategy to compute the APL based on a spherical harmonic expansion of the global atmosphere pressure changes. We also provide a sample model to describe the relativity between the global atmosphere pressure changes and APL vertical deformation. The results show that the variation of air mass has a major impact on the north-eastern area of East China, the eastern area of North China, and Northeast China, and the vertical crustal displacement caused by the atmosphere changes in these regions can reach about 20 mm. The correction of APL for vertical time series of GNSS reference stations in different regions indicates that the arid area of the Northwest China, Northeast China, Central China, and North China are greatly affected by APL. While for the station located in Sichuan-Yunnan region, the amplitude and period change are small after correction of APL for vertical time series of GNSS reference stations, which reveals that the area is seriously affected by tectonic movement and water migration loading. The correlation between atmospheric pressure changes and crustal deformation is analyzed, which shows that APL has a serious impact on the north-eastern area of North China, the Northeast China, and the eastern area of Central China when the variations in atmospheric pressure in mainland China are the same. The research results of this paper will provide some reference value for the study of crustal structural deformation and the establishment of geodetic datum.
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  • Crustal deformation characteristics of Sichuan-Yunnan region in China on the constraint of multi-periods of GPS velocity fields

    Yue, Caiya   Dang, Yamin   Dai, Huayang   Yang, Qiang   Wang, Xiankai  

    In order to obtain deformation parameters in each block of Sichuan-Yunnan Region (SYG) in China by stages and establish a dynamic model about the variation of the strain rate fields and the surface expansion in this area, we taken the Global Positioning System (GPS) sites velocity in the region as constrained condition and taken advantage of the block strain calculation model based on spherical surface. We also analyzed the deformation of the active blocks in the whole SYG before and after the Wenchuan earthquake, and analyzed the deformation of active blocks near the epicenter of the Wenchuan earthquake in detail. The results show that, (1) Under the effects of the carving from India plate and the crimping from the potential energy of Tibetan Plateau for a long time, there is a certain periodicity in crustal deformation in SYG. And the period change and the earthquake occurrence have a good agreement. (2) The differences in GPS velocity fields relative Eurasian reference frame shows that the Wenchuan earthquake and the Ya'an earthquake mainly affect the crustal movement in the central and southern part of SYG, and the average velocity difference is about 4-8 mm/a for the Wenchuan earthquake and 2-4 mm/a for the Ya'an earthquake. (3) For the Wenchuan earthquake, the average strain changed from 10 to 20 nanostrian/a before earthquake to 40-50 nanostrian/a after the earthquake, but before and after the Ya'an earthquake, the strain value increased from about 15 nanostrian/a to about 30 nanostrian/a. (4) The Wenchuan earthquake has changed the strain parameter of each active block more or less. Especially, the Longman block and Chengdu block near the epicenter. The research provides fundamental material for the study of the dynamic mechanism of the push extrusion from the north-east of the India plate and the crimp from Qinghai Tibet Plateau, and it also provides support for the study of crustal stress variation and earthquake prediction in Sichuan Yunnan region. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
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  • Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform

    Han, Liang   Yang, Guijun   Dai, Huayang   Yang, Hao   Xu, Bo   Li, Heli   Long, Huiling   Li, Zhenhai   Yang, Xiaodong   Zhao, Chunjiang  

    BackgroundWith environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding fornew varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations.ResultsIn the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits).ConclusionsThis framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.
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  • Surface crack and sand inrush disaster induced by high-strength mining:example from the Shendong coal field,China

    Yan, Weitao   Dai, Huayang   Chen, Junjie  

    Sand inrush disaster and ground destruction induced by high-strength mining in the Shendong coal field seriously threaten the normal operation of the mine and cause significant property losses and environmental disruption. The physical simulation experiment demonstrate that the roof of high-strength mining working face can be regarded as a "step beam" structure and broken by sliding instability. The vertical damage state of overlying strata is summarized into three types: slightly, severely and very severely damage. On the basis of in situ data of the working face with the mining height greater than 3 m, the prediction formulas of the caved and fractured zone heights are given. The vertical damage types of working faces 22407 and 22402 are analyzed. Owing to the sliding instability of the roof and the thin bedrock, the surface stepped crack has become widely distributed above the high-strength mining working face. The sand inrush of working face 22402 can be interpreted by the very severely damaged of overburden and the thick aeolian sand aquifer. This work can be used to improve the understanding of mining-induced disaster and establish a disaster prediction model.
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  • Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

    Han, Liang   Yang, Guijun   Dai, Huayang   Xu, Bo   Yang, Hao   Feng, Haikuan   Li, Zhenhai   Yang, Xiaodong  

    BackgroundAbove-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.ResultsIn this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.ConclusionsThese results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.
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  • Assessment of a house affected by ground movement using terrestrial laser scanning and numerical modeling

    Lian, Xugang   Dai, Huayang   Ge, Linlin   Cai, Yinfei  

    This study analyzed the internal mechanical state of houses disturbed by underground coal mining, especially those that are free from damage in subsidence areas. Traditional surveying and terrestrial laser scanning (TLS) were utilized as the external monitoring methods to obtain the outside displacement of the disturbed houses. The results were then used to estimate the internal mechanical state of houses using simulation software. Three high-quality houses in Paifang Village, China were scanned using TLS in their pre- and post-mining stages. The magnitude of movement and deformation was highly related to the location of the building in the panel, which was enlarged near the center of the subsidence basin. ANSYS was used to compute the internal state of the houses with a focus on the structural columns, ring beams, and walls of the houses under changing tensile conditions. Results showed that the structural column and ring beam can resist tensile deformation, whereas the window corner was the principal point of stress concentration with potential risk of cracking. Mastering the internal stress state of undamaged buildings in mining-affected areas can help to strengthen the premaintenance of houses and guide the construction of new anti-deformation houses. The results of this work may benefit the construction of anti-deformation buildings in mining regions.
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  • Fuzzy Clustering of Maize Plant-Height Patterns Using Time Series of UAV Remote-Sensing Images and Variety Traits

    Han, Liang   Yang, Guijun   Dai, Huayang   Yang, Hao   Xu, Bo   Feng, Haikuan   Li, Zhenhai   Yang, Xiaodong  

    The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.
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