• 한국지하수토양환경학회지:지하수토양환경
• /
• 제21권6호
• /
• pp.67-79
• /
• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2006년도 학술발표회 논문집
• /
• pp.1935-1939
• /
• 2006

In this study, the new dispersion-correction terms are added to leap-frog finite difference scheme for the linear shallow-water equations with the purpose of considering the dispersion effects such as linear Boussinesq equations for the propagation of tsunamis. And, dispersion-correction factor is determined to mimic the frequency dispersion of the linear Boussinesq equations. The numerical model developed in this study is tested to the problem that initial free surface displacement is a Gaussian hump over a constant water depth, and the results from the numerical model are compared with analytical solutions. The results by present numerical model are accurate in comparison with the past models.

• 품질경영학회지
• /
• 제34권2호
• /
• pp.122-128
• /
• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.

• 대한음성학회지:말소리
• /
• 제64호
• /
• pp.77-88
• /
• 2007

Nowadays, mel-frequency cesptral coefficients (MFCCs) and Gaussian mixture models (GMMs) are used for the pathological voice detection. This paper suggests a method to improve the performance of the pathological/normal voice classification based on the MFCC-based GMM. We analyze the characteristics of the mel frequency-based filterbank energies using the fisher discriminant ratio (FDR). And the feature vectors through the linear discriminant analysis (LDA) transformation of the filterbank energies (FBE) and the MFCCs are implemented. An accuracy is measured by the GMM classifier. This paper shows that the FBE LDA-based GMM is a sufficiently distinct method for the pathological/normal voice classification, with a 96.6% classification performance rate. The proposed method shows better performance than the MFCC-based GMM with noticeable improvement of 54.05% in terms of error reduction.

• Communications for Statistical Applications and Methods
• /
• 제21권5호
• /
• pp.423-433
• /
• 2014

Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.

• Communications for Statistical Applications and Methods
• /
• 제19권6호
• /
• pp.793-798
• /
• 2012

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

• 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.

• Journal of Radiation Protection and Research
• /
• 제28권4호
• /
• pp.311-319
• /
• 2003

본 연구는 격자형 가우시안 플룸모형을 Matlab언어를 이용하여 구축한 후, 영광원자력시설의 부지에서 시행된 추적자 확산실험자료를 이용하여 예측력을 평가하였다. 풍하방향으로는 20km까지 10m간격으로 격자를 구분하였으며, 풍하방향에 수직인 지표방향은 방출점을 중심으로 상하 5km를 각각 10m 간격으로 구분하여 $1,990{\times}1,000{\times}1$의 격자망으로 구성하였다. 실험당시의 대기안정도는 P-G방법에 의해 B등급으로 나타났으며 이를 이용하여 각 격자의 농도예측을 수행하였다. 반경 3km의 A-line의 경우가 반경 8km근방의 B-line에 비해 격자형 가우시안 모형의 예측력이 뛰어난 것으로 나타났으며, 방출점에서 거리가 멀어질수록 P-G방법에 의한 확산폭의 산정은 모형의 예측력을 떨어뜨리는 것으로 나타났다. 모형의 예측력을 향상시키기 위하여 P-G 방법에 의한 확산폭인 sigma y 및 sigma z를 선형계획법을 이용하여 수정하였다. 수정된 확산인자를 적용한 결과 3km와 8km 모두 모형의 예측력이 향상됨을 확인할 수 있었다. 향후 추적자 확산실험 데이터의 축적을 통해 기상조건에 따른 확산인자에 대한 경험식을 개발한다면 격자형 가우시안 모델이 원자력시설에서의 대기질 환경영향평가에 유용하게 쓰일 수 있을 것으로 기대된다.

• 대한전자공학회논문지
• /
• 제24권2호
• /
• pp.280-291
• /
• 1987

In this paper, the linear autoregressive model of EMG signal for four basic arm functions was presented and parameters for each function were estimated. The signal identification was carried out using function discrimination algorithm. It was validated that EMG signal was a widesense stationary process and the linear autoregressive model of EMG signal was constructed through approximating it to Gaussian process. It was confined that Levinson-Durbin algoridthm is a more appropriate one than the recursive least square method for parameter estimation of the linear model. Optimal function discrimination was acquired when sampling frequency was 500Hz and two electrodes were attached to bicep and tricep muscle, respectively. Parameter values were independent of variance and the number of minimum data for function discrimination was 200. Bayesian discrimination method turned out to be a better one than parallel filtering method for functional discrimination recognition.

• 응용통계연구
• /
• 제16권2호
• /
• pp.365-376
• /
• 2003

본 연구에서는 비정규 시계열 자료에 관한 다양한 회귀모형을 고찰하고, 이들 모형의 선택 기준에 관하여 연구해 보았다. 모형 선택의 기준으로는 AIC (Akaike information criterion), BIC (Baysian information criterion) 그리고 우도비 검정을 확장 적용하였다. 또한, 실제의 Polio 자료분석을 통해 이를 적용해보았다.