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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.
Kang, Seung-Kyun,Kim, Ju-Young,Park, Chan-Pyoung,Kim, Hyun-Uk,Kwon, Dongil Cambridge University Press (Materials Research Soc 2010 Journal of materials research Vol.25 No.2
<P>We evaluate Vickers hardness and true instrumented indentation test (IIT) hardness of 24 metals over a wide range of mechanical properties using just IIT parameters by taking into account the real contact morphology beneath the Vickers indenter. Correlating the conventional Vickers hardness, indentation contact morphology, and IIT parameters for the 24 metals reveals relationships between contact depths and apparent material properties. We report the conventional Vickers and true IIT hardnesses measured only from IIT contact depths; these agree well with directly measured hardnesses within ±6% for Vickers hardness and ±10% for true IIT hardness.</P>
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.
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>