• 응용통계연구
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• 제36권4호
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• pp.323-335
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• 2023

고차원 데이터의 인과 추론에서 고차원 공변량의 차원을 축소하고 적절히 변형하여 처리와 잠재 결과에 영향을 줄 수 있는 교란을 통제하는 것은 중요한 문제이다. 평균 처리 효과(average treatment effect; ATE) 추정에 있어서, 성향점수와 결과 모형 추정을 이용한 확장된 역확률 가중치 방법이 주로 사용된다. 고차원 데이터의 분석시 모든 공변량을 포함한 모수 모형을 이용하여 성향 점수와 결과 모형 추정을 할 경우, ATE 추정량이 일치성을 갖지 않거나 추정량의 분산이 큰 값을 가질 수 있다. 이런 이유로 고차원 데이터에 대한 적절한 차원 축소 방법과 준모수 모형을 이용한 ATE 방법이 주목 받고 있다. 이와 관련된 연구로는 차원 축소부분에 준모수 모형과 희소 충분 차원 축소 방법을 활용한 연구가 있다. 최근에는 성향점수와 결과 모형을 추정하지 않고, 차원 축소 후 매칭을 활용한 ATE 추정 방법도 제시되었다. 고차원 데이터의 ATE 추정 방법연구 중 최근에 제시된 네 가지 연구에 대해 소개하고, 추정치 해석시 유의할 점에 대하여 논하기로 한다.

• 대한기계학회논문집B
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• 제38권9호
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• pp.747-755
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• 2014

광자(Photon)이나 전자기파(Electromagnetic Wave) 등의 형태로 직접 열을 전달하는 특징을 가지고 있는 복사열전달은 중간 매질의 열전달 관여여부에 따라 표면복사(Surface Radiation)와 기체복사(Gas Radiation)의 형태로 구분될 수 있다. 본 연구에서는 원통 형상에서의 표면복사에 대해 미지의 복사물성치들을 역해석 방법을 이용해 역추정하였다. 이때, 효율적인 역해석을 위해 반발 입자 군집 최적화(Repulsive Particle Swarm Optimization, RPSO) 알고리즘을 역해석 기법으로 채택하였다. 이로부터 얻은 해의 수렴성과 정확도 등을 기존의 유전알고리즘(GA) 결과와 비교해 봄으로써, 표면복사 현상에 대한 역해석의 적용 가능성을 고찰하고자 하였다.

• 대한기계학회논문집B
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• 제23권3호
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• pp.364-373
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• 1999

A combination of conjugate gradient and Levenberg-Marquardt method is used to estimate the spatially varying scattering coefficient, ${\sigma}(r)$, in the solid and hollow spheres by utilizing the measured transmitted beams from the solution of an inverse analysis. The direct radiation problem associated with the inverse problem is solved by using the $S_{12}-approximation$ of the discrete ordinates method. The accuracy of the computations increased when the results from the conjugate gradient method are used as an initial guess for the Levenberg-Marquardt method of minimization. Optical thickness up to ${\tau}_0=3$ is used for the computations. Three different values of standard deviation are considered to examine the accuracy of the solution from the inverse analysis.

• 한국항해항만학회지
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• 제28권4호
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• pp.305-309
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• 2004

The problem for parameters estimation of the received signals impinging on array sensors has long been of great research Interest in a great variety of applications, such as radar, sonar, and land mobile communications systems. Conventional subspace-based algorithms, such as MUSIC and ESPRIT, require an extensive computation of inverse matrix and eigen-decomposition In this paper, we propose a new parameters estimation algorithm via nonlinear minimization, which is simplified computationally and estimates signal parameters simultaneously.

• 대한기계학회논문집A
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• 제27권9호
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• pp.1499-1507
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• 2003

Physical importance of cutting temperatures has long been recognized. Cutting temperatures have strongly influenced both the tool life and the metallurgical state of machined surfaces. Temperatures in drilling processes are particularly important, because chips remain in contact with the tool for a relatively long time in a hole. Tool temperatures tend to be higher in drilling processes than in other in machining processes. This paper concerns with modeling of thermal behaviors in drilling processes as well as estimation of the cutting temperature distribution based on remote temperature measurements. One- and two-dimensional estimation problems are proposed to analyze drilling temperatures. The proposed thermal models are compared with solutions of finite element methods. Observer algorithms are developed to solve inverse heat conduction problems. In order to apply the estimation of cutting temperatures, approximation methods are proposed by using the solution of the finite element method. In two-dimensional analysis, a moving heat source according to feedrate of the drilling process is regarded as a fixed heat source with respect to the drilling location. Simulation results confirm the application of the proposed methods.

• 전기전자학회논문지
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• 제25권4호
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.479-493
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• 2012

The inverse Weibull distribution(IWD) is a complementary Weibull distribution and plays an important role in many application areas. In this paper, we develop a Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution(EIWD). We obtained Bayesian estimators through the squared error loss function (quadratic loss) and LINEX loss function. This is done with respect to the conjugate priors for shape and scale parameters. The results may be of interest especially when only record values are stored.

• 대한토목학회논문집
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• 제10권4호
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• pp.35-42
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• 1990

구조물의 손상도를 Inverse Modal Perturbation 기법을 이용하여 추정하는 방법에 대하여 연구하였다. 손상된 구조물에 대하여 측정된, 제한된 수위 고유진동수와 고유진동모우드로 이루어진 Perturbation 식에 최적화기법을 적용하여, 손상된 구조물의 부재강성의 감소량을 추정하였다. 예제해석은 기둥모형과 트러스구조의 여러 가지 경우에 대하여 수행하였는데, 가정한 손상도에 따른 자유진동특성의 변화량을 바탕으로 측정한 손상도를 가정한 값과 비교하는 수치모의 실험방법을 통하여 본 기법의 효율성을 입증하였다.

• Journal of Mechanical Science and Technology
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• 제16권5호
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• pp.710-719
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• 2002

This study investigates a nonlinear inverse convection problem for a laminar-forced convective flow between two parallel plates. The upper plate is exposed to unknown heat flux while the lower plate is insulated. The unknown heat flux is determined using temperature measured on the lower plate. The thermophysical properties of the fluid are temperature dependent, which renders the problem nonlinear. The sequential gradient method is applied to this nonlinear inverse problem in order to solve the problem efficiently. The function specification method is incorporated to stabilize the sequential estimation. The corresponding adjoint formalism is provided. Accuracy and stability have been examined for the proposed method with test cases. The tendency of deterministic error is investigated for several parameters. Stable solutions are achieved eve]1 with severely impaired measurement data.

• 제어로봇시스템학회논문지
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• 제1권2호
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.