Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Cheng-Yu Ku"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Liquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seis
Externí odkaz:
https://doaj.org/article/c04902f0f5f94416bdb56b5e736e9f73
Publikováno v:
Mathematics, Vol 12, Iss 18, p 2940 (2024)
This study presents a novel approach for modeling unsaturated flow using deep neural networks (DNNs) integrated with spacetime radial basis functions (RBFs). Traditional methods for simulating unsaturated flow often face challenges in computational e
Externí odkaz:
https://doaj.org/article/fb45536a858f47caab024a1da6fc2310
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-20 (2023)
Abstract Land subsidence, a complex geophysical phenomenon, necessitates comprehensive time-varying data to understand regional subsidence patterns over time. This article focuses on the crucial task of reconstructing missing time-varying land subsid
Externí odkaz:
https://doaj.org/article/dae5cde55cd8466d852d8f3afbbcd19e
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 7202 (2024)
Soil classification is essential for understanding soil properties and their suitability for conveying the characteristics of soil types. In this study, we present a prediction of soil classification using fewer soil variables by employing the random
Externí odkaz:
https://doaj.org/article/8a6dbb1eaf244465b6b27acc84d29319
Autor:
Cheng-Yu Ku, Chih-Yu Liu
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-17 (2023)
Abstract In this study, the land subsidence in Yunlin County, Taiwan, was modeled using an artificial neural network (ANN). Maps of the fine-grained soil percentage, average maximum drainage path length, agricultural land use percentage, electricity
Externí odkaz:
https://doaj.org/article/71506db05da34e21aef882138dc26d51
Publikováno v:
Mathematics, Vol 12, Iss 9, p 1407 (2024)
This study introduces a deep neural network approach that utilizes radial basis functions (RBFs) to solve forward and inverse problems in the process of diffusion. The input layer incorporates multiquadric (MQ) RBFs, symbolizing the radial distance b
Externí odkaz:
https://doaj.org/article/0260b70063b94db188a81b59fe350f8e
Autor:
Cheng-Yu Ku, Chih-Yu Liu
Publikováno v:
Fire, Vol 7, Iss 4, p 136 (2024)
To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to devel
Externí odkaz:
https://doaj.org/article/4a73526d3dab4fbb9799fe06631bddc8
Publikováno v:
Applied Sciences, Vol 14, Iss 5, p 1930 (2024)
Fires resulting from human activities, encompassing arson, electrical problems, smoking, cooking mishaps, and industrial accidents, necessitate understanding to facilitate effective prevention. This study investigates human-caused fires in Keelung Ci
Externí odkaz:
https://doaj.org/article/86a71047a15249898e3dd6b2c74c07dc
Autor:
Cheng-Yu Ku, Chih-Yu Liu
Publikováno v:
Mathematics, Vol 11, Iss 21, p 4497 (2023)
This article introduces a new boundary-type meshless method designed for solving axisymmetric transient groundwater flow problems, specifically for aquifer tests and estimating hydraulic properties. The method approximates solutions for axisymmetric
Externí odkaz:
https://doaj.org/article/faa60a1aa2084704b31fb7279f2f9fc7
Autor:
Chih-Yu Liu, Cheng-Yu Ku
Publikováno v:
Mathematics, Vol 11, Iss 18, p 3935 (2023)
Elliptic boundary value problems (BVPs) are widely used in various scientific and engineering disciplines that involve finding solutions to elliptic partial differential equations subject to certain boundary conditions. This article introduces a nove
Externí odkaz:
https://doaj.org/article/711fab1bd5ce4414b1ff3a48df6bb4a1