• 한국자동차공학회논문집
• /
• 제14권2호
• /
• pp.23-31
• /
• 2006

Tolerance analysis of auto body requires the consideration of its compliance because of potentially significant deformation during the spot-weld assembly process. In this paper, a relatively recent method for such analyses is briefly introduced as one can find in the literature. In this method, it is important to take into account of the covariance between the sources of variation as they are closely located, which is the case in most auto body assembly. However, it is often impossible to know such covariance, for example, when a new car is being developed. Therefore, a mechanics-based method is proposed in this paper to estimate the covariance among the sources of variation by finite element analyses and simple statistical computations. The proposed method is illustrated by applying it to a three-dimensional model of real front wheel housing.

• International Journal of Aeronautical and Space Sciences
• /
• 제11권4호
• /
• pp.326-337
• /
• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Journal of the Korean Data and Information Science Society
• /
• 제17권4호
• /
• pp.1271-1278
• /
• 2006

Recently, a profit analysis in hospital management is considered as an important marketing concept. When spatial variability is presented, we must analyze the hospital data with spatial statistical methods. In this study, we present a regression model using spatial covariance for adjustment. And we compare the nonspatial model with spatial model.

• 한국지능시스템학회논문지
• /
• 제25권4호
• /
• pp.361-368
• /
• 2015

본 논문은 실내 환경에서 이동로봇의 위치추정을 위해 무향 칼만 필터(UKF, Unscented Kalman Filter)를 적용하는 방법을 기술한다. 위치 추정을 위해 적용한 무향 칼만 필터 방법은 측정 거리에 따라 오차 공분산 값을 조절하는 새로운 측정 불확실성 모델을 제안한다. 또한 이 방법은 속도정보의 불확실성 및 측정 불확실성에 관한 오차 공분산 행렬의 비 대각 성분을 '0'이 아닌 값으로 설정한다. 이 방법은 100*40m 의 실내 작업환경에서 외수용성 센서로서 레이저영역측정기(Laser range finder)를 가진 차륜형 이동로봇을 이용한 실험을 통하여 평가한다. 이 실험에서는 적응적 불확실성 모델을 사용하지 않는 보통의 방법과 제안된 방법의 추정성능을 비교한다. 또한 이 실험은 오차 공분산의 비 대각성분을 '0'이 아닌 값으로 설정하여 추정 성능이 개선되는 것을 확인한다. 이 논문은 이동로봇의 위치추정을 위한 실용적인 UKF 방법을 구현하고 그 성능을 평가 하는 것을 주요 내용으로 한다.

• Asian-Australasian Journal of Animal Sciences
• /
• 제30권6호
• /
• pp.765-772
• /
• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

• Communications for Statistical Applications and Methods
• /
• 제24권4호
• /
• pp.383-396
• /
• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• 응용통계연구
• /
• 제21권2호
• /
• pp.291-299
• /
• 2008

동일 개체가 여러 시점에서 반복되어 측정될 때, 측정값들 간에 종속성을 예상할 수 있다. 본 논문은 한 개체의 이가 반응변수가 g개 시점에서 관측될 때, 종속적인 g개 이 가변수들의 다변량 분포로부터 각 시점에서의 주변분포의 동질성을 파악하기 위한 로짓모형을 제시하고 자료분석 방법을 제공하고자 한다. 모형과 관련된 가정으로 반복측정이 행해지는 g개 시점은 각기 서로 다른 요인 또는 공변량의 결합수준들로 구성된다고 가정한다. 또한, 모형에서 고려된 처치들이 반복측정에 기인하는 서로 다른 크기의 실험 단위들에 행해질 때 모수들을 추정하기 위한 방법으로 가중최소제곱법을 다루고 있다. 여기서 가중최소제곱법은 반응변수들의 종속성으로 인한 공분산 구조에 근거한 모형내 모수들의 효과를 효율적으로 추론하기 위해 이용된다. 제시된 모형은 주변로짓을 이용함으로써 단순히 주변확률분포의 동질성에 대한 검정뿐만 아니라 모형의 타당성 및 요인들의 수준변화에 따른 효과를 파악하기 위한 효과적인 모형임을 보여준다.

• 물과 미래
• /
• 제29권5호
• /
• pp.215-222
• /
• 1996

본 논문에서는 다공성 매질의 특수율이 비정체형인 경우 대수투수계수-수두 교차공분산에 관한 식을 유도하였으며, 이 교차공분산은 수두분포로부터 특수장의 통계학적 특성을 유추하는데(inverse problem) 매우 중요한 역할을 담당한다. 비정체형 대수투수계수는 일정한 선형경향과 정체형인 미소 변동의 합으로 구성되었으며, 2차원 포화대수층에서 정상 유동문제를 추계학적으로 해석하여 수두분포를 얻었고 이로부터 교차공분산을 유도하였다. 투수계수의 상관함수가 가우스분포를 가지고 그 경향이 수두 경사와 평행이거나 직교하는 두 가지 경우에 대하여 교차공분산을 살펴 본 결과, 투수장의 경향이 주 흐름방향과 평행한 경우 흐름방향 쪽만 제외하고는 정체형임이 밝혀졌다. 또한, 흐름방향과 직교하는 쪽으로의 교차공분산은 정체형 모델 결과와 달리 영이 아님를 알 수 있었다. 따라서 지하수 유동이나 오염물질 확산문제를 다룰 경우, 투수계수장에 어떤 경향이 존재한다고 의심될 때에는 반드시 그 경향을 해석과정에 포함시켜야 한다.

• ETRI Journal
• /
• 제39권4호
• /
• pp.592-604
• /
• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.25-25
• /
• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.