Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Guohua Gou"'
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 5048 (2023)
Considering the challenges associated with accurately identifying building shape features and distinguishing between building and non-building features during the extraction of buildings from remote sensing images using deep learning, we propose a no
Externí odkaz:
https://doaj.org/article/8d4c0e4c04d449c493167c1815602828
Publikováno v:
Drones, Vol 7, Iss 9, p 544 (2023)
Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive captu
Externí odkaz:
https://doaj.org/article/5f631616b2a1470f9651dab053f2e9fe
Publikováno v:
Drones, Vol 7, Iss 6, p 358 (2023)
Real-time 3D reconstruction combined with MAVs has garnered significant attention in a variety of fields, including building maintenance, geological exploration, emergency rescue, and cultural heritage protection. While MAVs possess the advantages of
Externí odkaz:
https://doaj.org/article/5e0799c2c8a4427fa4df6badf62be023
Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5879 (2022)
Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual geo-localization is seriously impaired by illumination variation,
Externí odkaz:
https://doaj.org/article/a0b75018d05747c099b1b2eec649f6b7
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 979 (2022)
Unmanned Aerial Vehicles (UAVs) require the ability to robustly perceive surrounding scenes for autonomous navigation. The semantic reconstruction of the scene is a truly functional understanding of the environment. However, high-performance computin
Externí odkaz:
https://doaj.org/article/2ed54499e36e43a190d2a3b439d838fe
Publikováno v:
Remote Sensing, Vol 14, Iss 2, p 289 (2022)
Manifold mesh, a triangular network for representing 3D objects, is widely used to reconstruct accurate 3D models of objects structure. The complexity of these objects and self-occlusion, however, can cause cameras to miss some areas, creating holes
Externí odkaz:
https://doaj.org/article/1e434a03184e499ca2e02dbf270f6495
Autor:
Guohua GOU
Publikováno v:
MATEC Web of Conferences, Vol 175, p 03015 (2018)
This paper studied the coal demand in the prediction accuracy problems. The traditional gray GM(1,1)model has the theoretical prediction problem of poor accuracy which leaded to less accurate prediction. A modified gray BP Neural Network forecasting
Externí odkaz:
https://doaj.org/article/f52da5dc785f4330a6c8fae2ca044fc8
Autor:
Guohua Gou
Publikováno v:
International Journal of Information and Education Technology. 7:870-875
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::157820179eb6888530840075db3576ee
https://doi.org/10.18178/ijiet.2017.7.11.987
https://doi.org/10.18178/ijiet.2017.7.11.987
Autor:
Guohua Gou
Publikováno v:
Journal of Service Science and Management. 10:166-176
After introducing the inflation expectation, this paper uses the co-integration test and VAR model to analyze the price fluctuation of agricultural products and the paper analyzes the relationship between inflation and inflation expectation. The resu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8325960b53264e8175afa9eaec631e0b
https://doi.org/10.4236/jssm.2017.102015
https://doi.org/10.4236/jssm.2017.102015
Autor:
Guohua Gou
Publikováno v:
IOP Conference Series: Earth and Environmental Science. 113:012052
This paper analyzes the advantage of support vector regression (SVR) in the prediction of carbon emission and establishes the SVR-based carbon emission prediction model. The model is established using the data of Henan's carbon emissions and influenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6201af722f89dadce0bacd6e480d6792
https://doi.org/10.1088/1755-1315/113/1/012052
https://doi.org/10.1088/1755-1315/113/1/012052