• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.320-322
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• 2009

Support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. In this pater, we design the controller using support vector regression which has good properties in comparison with multi-layer perceptron or radial basis function. The applicability of the presented method is illustrated via an example simulation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.47-52
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• 2002

There are many factors in machine tool error. These are thermal deformation, geometric error, machine's part assembly error, error caused by tool bending. Among them thermal error is 70% of total error of machine tool . Prediction of thermal error is very difficult. because of nonlinear tendency of machine tool deformation. In this study, we tried thermal error prediction by using multi regression analysis.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.131-141
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• 2003

In this paper, we prove that multi-variate fuzzy polynomials are universal approximators for multi-variate fuzzy functions which are the extension principle of continuous real-valued function under $T_W-based$ fuzzy arithmetic operations for a distance measure that Buckley et al.(1999) used. We also consider a class of fuzzy polynomial regression model. A mixed non-linear programming approach is used to derive the satisfying solution.

• 한국기계가공학회지
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• 제11권6호
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• pp.36-41
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• 2012

In this study, we turning plastic mold steel (STAVAX) against cutting speed, depth of cut, feed rate using whisker reinforced ceramic tool (WA1). To predict cutting force, analyze principal, radial, feed force with multi-regression analysis. Results are follows: From the analysis of variance, affected factor to cutting force feed rate, depth of cut, cutting speed in order and cutting speed was very small affect to cutting force. From multi-regression analysis, we extracted regression equation and the coefficient of determination$(R^2)$ was 0.9, 0.88, 0.856 at principal, radial and feed force. It means regression equation is significant. From the experimental verification, it was confirmed that principal, radial and feed force was predictable by regression equation.

• Structural Engineering and Mechanics
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• 제45권4호
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• pp.423-437
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• 2013

Complex structures are usually assembled from several substructures with joints connecting them together. These joints have significant effects on the dynamic behavior of the assembled structure and must be accurately modeled. In structural analysis, these joints are often simplified by assuming ideal boundary conditions. However, the dynamic behavior predicted on the basis of the simplified model may have significant errors. This has prompted the researchers to include the effect of joint stiffness in the structural model and to estimate the stiffness parameters using inverse dynamics. In the present work, structural joints have been modeled as a pair of translational and rotational springs and frequency equation of the overall system has been developed using sub-structure synthesis. It is shown that using first few natural frequencies of the system, one can obtain a set of over-determined system of equations involving the unknown stiffness parameters. Method of multi-linear regression is then applied to obtain the best estimate of the unknown stiffness parameters. The estimation procedure has been developed for a two parameter joint stiffness matrix.

• 한국정밀공학회지
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• 제25권5호
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 춘계학술발표대회
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• pp.938-940
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• 2015

CCTV는 범죄상황 발생시 보안과 증거확보를 위해 사용되어 왔다. 그러나 실제 상황에서 범죄가 발생하기 전 예방을 하는 것 보다 사후 처리에 용도를 두고 있으며, 범죄 예방의 목적에 대해 미미한 효과를 보이고 있다. 본 논문에서는 CCTV로 수집된 보행자의 데이터를 통해 객체의 행동을 분석하여 위험도로 행동의 위험여부를 추정하기 위한 Flexible Multi-level Regression 모델을 제안하였다. 제안된 모델을 통해 관찰된 객체의 행동이 이상행동이라고 판단될 시 위험을 받는 객체에게 알림을 주어 범죄 발생 전 즉각적인 대응이 가능하며 빠른 상황판단이 가능할 것으로 예상된다.

• 가정간호학회지
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• 제23권2호
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• pp.206-215
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• 2016

Purpose: The purpose of this study was to investigate the level of cultural competence, intercultural communicative competence, and multi-cultural job stress among healthcare workers and to explore factors that are related to their cultural competence. Methods: The study subjects were 142 healthcare workers at a general hospital. Data were collected using a questionnaire on cultural competence, intercultural communicative competence, and multi-cultural job stress. A t-test, ANOVA Pearson's correlation coefficient, and multiple regression analysis were conducted using SPSS. Results: Cultural competence was significantly related to the necessity of multi-cultural education, and intercultural communicative competence was significantly related to age, a vocational career, communication in foreign languages, and having multi-cultural neighbors. Moreover, multi-cultural job stress was significantly related to religion. In multiple regression results, cultural competence was found to be related to intercultural communicative competence and multi-cultural job stress. Conclusion: Healthcare workers who are set to care for multi-cultural patients should improve intercultural communicative competence and reduce multi-cultural job stress.

• Computers and Concrete
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• 제21권4호
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• 한국항공우주학회지
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• 제43권7호
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• pp.601-608
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• 2015

유연날개의 공력 및 구조 설계값을 설계 변수로 하여 정적 상태에서의 정적 공탄성해석 및 최적화를 수행하였다. 정적 공탄성해석과 최적화를 위해 상용 해석소프트웨어들이 연계된 강건한 다분야 최적설계 시스템을 개발하였다. 최적화 설계변수로는 가로세로비, 테이퍼비, 후퇴각과 날개 위아래 스킨 두께를 설정하였다. 전역적 다목적 최적화를 위해 실수기반 적응영역 다목적 유전자 알고리즘을 적용하였으며 계산시간을 줄이기 위해 메타모델로 서포트벡터회귀 기법을 적용하였다. 유연날개에 대한 파레토 결과 분석을 통해 최대 항속시간과 최소 중량에 대한 최적 결과를 확인하였다.