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Kang, Seung-Kyun,Kim, Ju-Young,Kang, Ingeun,Kwon, Dongil Cambridge University Press (Materials Research Soc 2009 Journal of materials research Vol.24 No.9
<P>We introduce a novel method to correct for imperfect indenter geometry and frame compliance in instrumented indentation testing with a spherical indenter. Effective radii were measured directly from residual indentation marks at various contact depths (ratio of contact depth to indenter radius between 0.1 and 0.9) and were determined as a function of contact depth. Frame compliance was found to depend on contact depth especially at small indentation depths, which is successfully explained using the concept of an extended frame boundary. Improved representative stress-strain values as well as hardness and elastic modulus were obtained over the entire contact depth.</P>
강환일(Hwan Il Kang),박강(Kang Park),신동일(Dongil Shin),박우성(Woo Seong Park),주기돈(Gee Don Joo) 한국정보과학회 2012 한국정보과학회 학술발표논문집 Vol.39 No.1B
기존의 탄도방정식[2]에서 여러조건을 제시하여 간략화된 대공화기 탄도방정식을 얻는다. 대공화기의 탄도궤적이므로 양력계수가 들어간 항의 값이 충분히 작다는 가정을 하였다. 또한 속도의 크기를 시간불변이라는 가정을 하였다. 이 탄도방정식은 기존의 방정식[1]에 비하여 밀도, 풍속, 항력계수 및 탄도계수가 식에 나타나 있어 일반적인 탄도방정식으로 이용가능하고 또한 미분방정식의 해를 구할 필요가 없다. 모의실험을 통하여 제시된 탄도방정식을 이용하여 풍속이 들어간 탄도궤적을 구한다.
Efficient Feature Selection-Based on Random Forward Search for Virtual Metrology Modeling
Kang, Seokho,Kim, Dongil,Cho, Sungzoon Institute of Electrical and Electronics Engineers 2016 IEEE transactions on semiconductor manufacturing Vol.29 No.4
<P>Virtual metrology (VM) has been successfully applied to semiconductor manufacturing as an efficient way of achieving wafer-to-wafer quality control. VM involves the estimation of metrology variables of wafer inspection using a prediction model trained with process parameters and measurements prior to the actual implementation of metrology. VM modeling should incorporate a number of process parameters and measurements collected from each piece of process equipment, which results in a greater number of input variables. Therefore, it is necessary to resolve the problem of high dimensionality through feature selection. A suitable feature selection method for VM modeling should effectively address the high dimensionality by lowering the computational cost, while also achieving high prediction accuracy as an essential requirement for the practical deployment of VM. In this paper, a feature selection method based on random forward search is proposed for efficient VM modeling. This method selects relevant variables sequentially from disjoint random subsets of candidate variables by incorporating randomness. Our preliminary experimental results obtained with real-world semiconductor manufacturing data demonstrate that the proposed feature selection method achieves comparable prediction accuracy yet has the advantages of being computationally more efficient, thus merits further investigation.</P>
Kang, Pilsung,Kim, Dongil,Cho, Sungzoon Elsevier 2016 expert systems with applications Vol.51 No.-
<P><B>Abstract</B></P> <P>Dataset size continues to increase and data are being collected from numerous applications. Because collecting labeled data is expensive and time consuming, the amount of unlabeled data is increasing. Semi-supervised learning (SSL) has been proposed to improve conventional supervised learning methods by training from both unlabeled and labeled data. In contrast to classification problems, the estimation of labels for unlabeled data presents added uncertainty for regression problems. In this paper, a semi-supervised support vector regression (SS-SVR) method based on self-training is proposed. The proposed method addresses the uncertainty of the estimated labels for unlabeled data. To measure labeling uncertainty, the label distribution of the unlabeled data is estimated with two probabilistic local reconstruction (PLR) models. Then, the training data are generated by oversampling from the unlabeled data and their estimated label distribution. The sampling rate is different based on uncertainty. Finally, expected margin-based pattern selection (EMPS) is employed to reduce training complexity. We verify the proposed method with 30 regression datasets and a real-world problem: virtual metrology (VM) in semiconductor manufacturing. The experiment results show that the proposed method improves the accuracy by 8% compared with conventional supervised SVR, and the training time for the proposed method is 20% shorter than that of the benchmark methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new semi-supervised support vector regression method is proposed. </LI> <LI> Label distribution is estimated by probabilistic local reconstruction algorithm. </LI> <LI> Different oversampling rate is used based on uncertainty information. </LI> <LI> Expected margin based pattern selection is used to reduce the training complexity. </LI> <LI> The proposed method improves the prediction performance with lower time complexity. </LI> </UL> </P>
Electrostatic Energy Harvester Using Magnetically Actuated Liquid Dielectric Layers
Dongil Kim,Seonuk Yu,Byeong-Geun Kang,Kwang-Seok Yun Institute of Electrical and Electronics Engineers 2015 Journal of microelectromechanical systems Vol. No.
<P>We propose a fully liquid-based energy harvester that uses ferrofluid droplets as a movable dielectric material. The proposed device consists of top and bottom plates with conducting electrodes coated with a thin solid dielectric layer, a conducting liquid, and oil-based ferrofluid droplets as movable dielectric layers. The rotational motion of the ferrofluid droplets is actuated by a magnetic field that causes a capacitance variation that is used to generate electric power. An average output power of 19.3 μW is generated when eight ferrofluid droplets are used at a rotational speed of 180 r/min.</P>
Korean EFL Learners’ Acquisition of Voiceless Interdental Fricative
Dongil Shin,Gina Kang 현대문법학회 2011 현대문법연구 Vol.65 No.-
The purpose of this study is to investigate the production of English interdental fricative [θ] by Korean EFL learners. This study also examines participants’ tendency to substitute [θ] with four possible variants. Data is gathered from 17 EFL learners who are asked to demonstrate four oral production tasks. The speech styles are collected by interview, reading aloud a passage, a word list with minimal pairs, and a picture story telling. In addition, participants are asked to fill their preferred variants for (th) on the questionnaire given. After using the Praat program to analyze data, the study shows that phonological variation in English (th) is systematically affected by (1) internal factors (phono- logical factors): following front vowel and syllable positions; (2) external factor: speech style. With regard to phonological factors, high frequency plays a crucial role and high frequency words are more quickly to go through the progress of the acquisition of (th)-sound. As for the effect of external context, word-list reading and passage reading strongly promotes accurate (th), while story telling inhibits it. The study also suggests that the more attention is paid to speech, the more accurate production will be produced. Finally, the language attitude questionnaire revealed that /s/ is not only the acceptable substitute but also the students’ preferred variant for (th)-sound.
Constitutive equations optimized for determining strengths of metallic alloys
Kang, Seung-Kyun,Kim, Young-Cheon,Kim, Kug-Hwan,Kwon, Dongil,Kim, Ju-Young Elsevier 2014 Mechanics of materials Vol.73 No.-
We investigate compatibilities of three constitutive equations, the Hollomon, the Swift, and the Voce equations for determination of yield and ultimate tensile strengths based on tensile true stress-strain curves of 27 metal alloys including those with power-law type and linear-type strain-hardening. We analyze each constitutive equation in terms of yield strength determined by the intercept of the linear elastic loading curve and plastic flow curve and ultimate tensile strength evaluated by the concept of instability in tension. We found that the describing plastic flow is very sensitive in determination of the yield strength and tensile strength from parameters of constitutive equation. Voce equation gives estimate yield strength and tensile strength better than Hollomon and Swift equations. (C) 2014 Elsevier Ltd. All rights reserved.