Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Chih Chiang Wei"'
Autor:
Chih-Chiang Wei, Cheng-Shu Chiang
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 2, p 283 (2024)
In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world’s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximi
Externí odkaz:
https://doaj.org/article/9cdde0dbf5ce4dc193827cc268d59995
Autor:
Chih-Chiang Wei, Wei-Jen Kao
Publikováno v:
Atmosphere, Vol 14, Iss 12, p 1817 (2023)
With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter
Externí odkaz:
https://doaj.org/article/5974cbf8004a4885b879e3763f30c073
Autor:
Chih-Chiang Wei, Yen-Chen Yang
Publikováno v:
Energies, Vol 16, Iss 23, p 7693 (2023)
One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustain
Externí odkaz:
https://doaj.org/article/7f208c79629147f39fc10b4582832b05
Autor:
Chih-Chiang Wei
Publikováno v:
Journal of Civil Engineering and Management, Vol 27, Iss 4 (2021)
Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether
Externí odkaz:
https://doaj.org/article/59076cce24af4ffbba54a4fb27e64db6
Autor:
Chih-Chiang Wei
Publikováno v:
Advances in Meteorology, Vol 2020 (2020)
Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish a
Externí odkaz:
https://doaj.org/article/7545570a9f5f42e392e08b23110864cb
Autor:
Chih-Chiang Wei
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 11, p 1257 (2021)
Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave for
Externí odkaz:
https://doaj.org/article/f3c44feebcd04ca89510bd90c50842c8
Autor:
Chih-Chiang Wei, Hao-Chun Chang
Publikováno v:
Sensors, Vol 21, Iss 15, p 5234 (2021)
Taiwan is an island, and its economic activities are primarily dependent on maritime transport and international trade. However, Taiwan is also located in the region of typhoon development in the Northwestern Pacific Basin. Thus, it frequently receiv
Externí odkaz:
https://doaj.org/article/3dde90402680475aaad4157adf4d997e
Autor:
Chih-Chiang Wei, Tzu-Heng Huang
Publikováno v:
Sensors, Vol 21, Iss 12, p 4200 (2021)
Taiwan is located at the edge of the northwestern Pacific Ocean and within a typhoon zone. After typhoons are generated, strong winds and heavy rains come to Taiwan and cause major natural disasters. This study employed fully convolutional networks (
Externí odkaz:
https://doaj.org/article/b121bcb803ba4766b6b98cde515fd058
Autor:
Chih-Chiang Wei, Chen-Chia Hsu
Publikováno v:
Sensors, Vol 21, Iss 4, p 1421 (2021)
This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar
Externí odkaz:
https://doaj.org/article/a071d72c991a4cdca9c7fb2c199cd86f
Autor:
Chih-Chiang Wei
Publikováno v:
Remote Sensing, Vol 12, Iss 24, p 4172 (2020)
To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input fu
Externí odkaz:
https://doaj.org/article/7c5b98f7b56c4c69a25f3364d18a38db