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Your search keyword '"Tsai, Chia-Cheng"' showing total 188 results
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188 results on '"Tsai, Chia-Cheng"'

23. Hindcast of oil spill pollution in the East China Sea in January 2018.

Author

Nguyen, Thi-Hong-Hanh, Hou, Tien-Hung, Pham, Hai-An, and Tsai, Chia-Cheng

Abstract

In this study, an integrated model is developed for studying the Sanchi oil spill event, which occurred in the East China Sea in January 2018. The results of the Advanced Research Weather Research and Forecasting model (WRF-ARW) as well as the Princeton Ocean Model (POM) are used for meteorological forecasting and the hydrodynamic simulations, respectively. These data are adopted as inputs for the OpenOil, a sub-module of OpenDrift, for the oil spill model. Some reference experiments are examined for short-term hindcast. The satellite image is used to validate the numerical result. The oil slicks of the satellite image and the numerical result are of similar shapes. Quantitatively, the simulated oil slick and that from the satellite image are located closely and have similar dimensions of 56 km by 34 km and 54 km by 29 km, respectively. It is found that the accurate results can be obtained by the proposed integrated model with the high-frequency (hourly) and high-spatial-resolution data as inputs, and the wind drift factor has to be added. The long-term 1-month simulation showed that most of the oil particles would move to the northeast of the sinking location and be trapped by the Kuroshio current. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Rafflesia Optimization Algorithm Applied in the Logistics Distribution Centers Location Problem

Author

Jeng-Shyang Pan Jeng-Shyang Pan, Zonglin Fu Jeng-Shyang Pan, Chia-Cheng Hu Zonglin Fu, Pei-Wei Tsai Chia-Cheng Hu, and Shu-Chuan Chu Pei-Wei Tsai

Subjects
Computer Networks and Communications, Software
Abstract

Intelligent evolutionary algorithm is an important method to solve optimization problems. Most of their inspiration comes from the laws of nature and biology. This paper proposes a new intelligent evolutionary algorithm based on the life habits of Rafflesia, which is called Rafflesia Optimization Algorithm. It mainly consists of three stages: attracting insects, swallowing insects, and spreading seeds. In the first stage, the ROA algorithm performs the local search to find the optimal solution. In the second stage, it improves execution efficiency and solution accuracy by reducing the number of individuals. In the third stage, it performs the global search to jump out of the local optimal position. In the experimental part, this paper uses numerical functions (the CEC2013 benchmark function set) and practical application problems (the logistics distribution centers location problem) to test the performance of the ROA algorithm, and compares it with seven meta-heuristics algorithms. The experimental results prove the effectiveness and practicability of the ROA algorithm. &nbsp

Published
2022

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