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김유철,김광수,황승현,연성모,Kim, Yoo-Chul,Kim, Kwang-Soo,Hwang, Seung-Hyun,Yeon, Seong Mo 대한조선학회 2022 大韓造船學會 論文集 Vol.59 No.4
In the design stage of hull forms, a fast prediction method of resistance performance is needed. In these days, large test matrix of candidate hull forms is tested using Computational Fluid Dynamics (CFD) in order to choose the best hull form before the model test. This process requires large computing times and resources. If there is a fast and reliable prediction method for hull form performance, it can be used as the first filter before applying CFD. In this paper, we suggest the offset-based performance prediction method. The hull form geometry information is applied in the form of 2D offset (non-dimensionalized by breadth and draft), and it is studied using Convolutional Neural Network (CNN) and adapted to the model test results (Residual Resistance Coefficient; C<sub>R</sub>). Some additional variables which are not included in the offset data such as main dimensions are merged with the offset data in the process. The present model shows better performance comparing with the simple regression models.
김유철(Yoo Chul Kim),정금동(Keum-Dong Jung),김병주(Byeong-Ju Kim),이종덕(Jong Duk Lee),박병국(Byung-Gook Park) 대한전자공학회 2007 대한전자공학회 학술대회 Vol.2007 No.11
A new threshold voltage and mobility extraction method for organic thin film transistors (OTFTs) are suggested. As mobility of OTFTs is known to have dependence on gate bias, conventional threshold voltage extraction methods cannot be applied. By dividing saturation regime current with linear regime current, a linear curve can be obtained. Then, from x-axis intercept of the curve, threshold voltage can be extracted. Substituting extracted threshold voltage to drain current equation, gate voltage dependent mobility can be obtained. This simple procedure can be applied to the devices which has gate bias dependent mobility as OTFTs.