• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.2
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• pp.23-31
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• 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
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• v.11 no.4
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• pp.326-337
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• 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
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• v.17 no.4
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• pp.1271-1278
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• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.6
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• pp.765-772
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• 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
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• v.24 no.4
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• pp.383-396
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• 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.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• Water for future
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• v.29 no.5
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• pp.215-222
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• 1996

An expression for the cross covariance of the logconductivity and the head in nonstationary porous formation is obtained. This cross covariance plays a key role in the inverse problem, i.e., in inferring the statistical characteristics of the conductivity field from head data. The nonstationary logconductivity is modeled as superposition of definite linear trend and stationary fluctuation and the hydraulic head in saturated aquifers is found through stochastic analysis of a steady, two-dimensional flow. The cross covariance with a Gaussian correlation function is investigated for two particular cases where the trend is either parallel or normal to the head gradient. The results show that cross covariances are stationary except along separation distances parallel to the mean flow direction for the case where the trend is parallel to head gradient. Also, unlike the stationary model, the cross covariance along distances normal to flow direction is non-zero. From these observations we conclude that when a trend in the conductivity field is suspected, this information must be incorporated in the analysis of groundwater flow and solute transjport.

• ETRI Journal
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• v.39 no.4
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• pp.592-604
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• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 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.