Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Chengzhi DENG"'
Publikováno v:
暴雨灾害, Vol 43, Iss 6, Pp 627-636 (2024)
To gain an in-depth understanding of the asymmetric structure of the Southwest Vortex producing rainstorms, the rainstorm process in the Sichuan Basin on 30 June 2013 was investigated in this study. This rainstorm was caused by the slowly moving Sout
Externí odkaz:
https://doaj.org/article/3a8f6bf586ca47dbaa2ec28bf9989c99
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 15, Iss 1 (2024)
The study analyzed hourly extreme precipitation events in Chongqing and its six sub-regions from 2011 to 2021 during the warm season. Results showed uneven distribution of the 99.7th percentile hourly extreme precipitation threshold, with lower thres
Externí odkaz:
https://doaj.org/article/a404724c11d1468c93c02d230d82889d
Publikováno v:
暴雨灾害, Vol 43, Iss 2, Pp 204-213 (2024)
This paper proposes an assessment model of the regional rainstorm process based on the return period method, which is used to comprehensively assess the frequency and intensity of rainstorm processes. Based on the hourly rainfall data and rainstorm d
Externí odkaz:
https://doaj.org/article/a6885b0c5eda418795f5e57dd5724d4d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11583-11597 (2024)
Integrating a low-spatial-resolution hyperspectral image with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based m
Externí odkaz:
https://doaj.org/article/1e513126019e4bda80b48bcffbcd5b26
Autor:
Ke Wang, Lei Zhong, Jiajun Zheng, Shaoquan Zhang, Fan Li, Chengzhi Deng, Jingjing Cao, Dingli Su
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1269-1285 (2024)
With the aid of endmember spectral libraries, sparse unmixing plays a critical role in interpreting hyperspectral remote sensing data. Integrating spatial clues from hyperspectral data into sparse unmixing frameworks is pivotal for enhancing unmixing
Externí odkaz:
https://doaj.org/article/f2ddfe67c58542e4ad65c1ea0987b2cc
Autor:
Chengzhi DENG, Yan ZHANG, Qiang LI, Juan LUO, Zhiyi LIAO, Zhengqian WU, Chunmei HU, Tingting LIU, Yingying ZHOU
Publikováno v:
暴雨灾害, Vol 42, Iss 1, Pp 24-36 (2023)
Torrential rain with the greatest social impact in Chongqing in the year of 2021 occurred in the central and eastern parts of Sichuan basin from August 7 to August 8. Using multi-source observation and ERA5 reanalysis data, the characteristics of mes
Externí odkaz:
https://doaj.org/article/0f9860659e484e0eacb831a6f507e823
Publikováno v:
Tehnički Vjesnik, Vol 29, Iss 4, Pp 1202-1209 (2022)
Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effect
Externí odkaz:
https://doaj.org/article/ff6b0d6f70c54f31b1f7eedcc895d05b
Publikováno v:
World Electric Vehicle Journal, Vol 14, Iss 9, p 252 (2023)
To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in or
Externí odkaz:
https://doaj.org/article/9295fd1b5bdd4b0286cb4ed75bf7e1ee
Autor:
Chengzhi Deng, Yonggang Chen, Shaoquan Zhang, Fan Li, Pengfei Lai, Dingli Su, Min Hu, Shengqian Wang
Publikováno v:
Remote Sensing, Vol 15, Iss 16, p 4056 (2023)
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial informati
Externí odkaz:
https://doaj.org/article/311b1c27662043609d3d9cf3e35243ca
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6119-6130 (2021)
Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image. Spars
Externí odkaz:
https://doaj.org/article/c160b71bcea44298b14c6aa6fbb272da