• 응용통계연구
• /
• 제23권1호
• /
• pp.73-79
• /
• 2010

Missing values for unit root processes are imputed by the most recent observations. Treating the imputed observations as if they are complete ones, semiparametric unit root tests are extended to missing value situations. Also, an invariance principle for the partial sum process of the imputed observations is established under some mild conditions, which shows that the extended tests have the same limiting null distributions as those based on complete observations. The proposed tests are illustrated by analyzing an unequally spaced real data set.

• International Journal of Reliability and Applications
• /
• 제7권1호
• /
• pp.41-53
• /
• 2006

The exact cause of the system's failure is often unknown in the masked system lifetime data. In such type of data, there are two observable quantities, namely (i) the systems time to failure and (ii) the set of systems components that contains the component, which might cause the system to fail. Our objective in this paper is to use the maximum likelihood procedure in the presence of masked data to make inference for the reliability of the system's components. We assume a multi-component series system where each component has a constant failure rate. Different cases that permit for closed form solutions of point estimates are considered. The results obtained in this paper generalize other published results.

• KIEE International Transactions on Power Engineering
• /
• 제4A권4호
• /
• pp.254-261
• /
• 2004

Traditionally, electrical power systems had formed the vertically integrated industry structures based on the economics of scale. However, power systems have been recently reformed to increase their energy efficiency. According to these trends, the Korean power industry underwent partial reorganization and competition in the generation market was initiated in 2001. In competitive electric markets, accurate load data is one of the most important issues to maintaining flexibility in the electric markets as well as reliability in the power systems. In practice, the measuring load data can be uncertain because of mechanical trouble, communication jamming, and other issues. To obtain reliable load data, an efficient evaluation technique to adjust the missing load data is required. This paper analyzes the load pattern of historical real data and then the tuned ARIMA (Autoregressive Integrated Moving Average), PCHIP (Piecewise Cubic Interpolation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and also tested against historical measured data from the Korea Energy Management Corporation (KEMCO).

• 한국지하수토양환경학회:학술대회논문집
• /
• 한국지하수토양환경학회 2003년도 추계학술발표회
• /
• pp.213-216
• /
• 2003

Efforts for better understanding of the interaction between groundwater recharge and thermal regime of the subsurface medium is gaining momentum for its diverse applications in water resources. A numerical model is developed to simulate temperature variations of the subsurface under time varying groundwater recharge. The model utilizes MacCormack scheme for finite difference approximation of the partial differential equation describing the conductive and advective heat transport. For the estimation of recharge rate, optimization of the model is realized by searching for the unknown parameters which minimize the root-mean-square error between simulated and measured temperatures. Simulation results for 22-year time series data of temperature measurements reveal that the proposed model can accurately simulate subsurface temperature variations resulting from the redistribution of the heat due to the movement of water and it can also estimate temporal variations of recharge. Seasonal variations of recharge and a linear relationship between precipitation and recharge are clearly reflected in the simulated results.

• Journal of the Korean Statistical Society
• /
• 제27권1호
• /
• pp.67-90
• /
• 1998

In this paper we analytically study model misspecification that arises in regression analysis of nonstationary seasonal time series. We assume the underlying data generating process is a seasonally or a regularly and seasonally integrated process. We first study consequences of totally misspecified cases where seasonal indicator variables, a linear time trend, or another statistically independent seasonally integrated process are used as predictor variables in order to model the nonstationary seasonal behavior of the dependent variable. Then we study consequences of partially misspecified cases where the dependent variable and a predictor variable are cointegrated at some, but not all of the frequencies corresponding to the nonstationary roots.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2002년도 하계학술대회 논문집 Vol.3 No.2
• /
• pp.939-944
• /
• 2002

PD phenomena can be regarded as a deterministic dynamical process where PD should be occurred if the local electric field be reached to be sufficiently high. And thus, its mathematical model can be described by either difference equations or differential equations using several state variables obtained from the time sequential measured data of PD signals. These variables can provide rich and complex behavior of detectable time series, for which Chaos theory can be employed. In this respect, a new PD pattern recognition method is proposed and named as 'Chaotic Analysis of Partial Discharges (CAPD)' for this work. For this purpose, six types of specimen are designed and made as the models of the possible defects that may cause sudden failures of the underground power transmission cables under service, and partial discharge signals, generated from those samples, are detected and then analyzed by means of CAPD. Throughout the work, qualitative and quantitative properties related to the PD signals from different defects are analyzed by use of attractor in phase space, information dimensions ($D_0$ and D2), Lyapunov exponents and K-S entropy as well. Based on these results, it could be pointed out that the nature of defect seems to be identified more distinctively when the CAPD is combined with traditional statistical method such as PRPDA. Furthermore, the relationship between PD magnitude and the occurrence timing is investigated with a view to simulating PD phenomena.

• Journal of the Korean Data and Information Science Society
• /
• 제25권4호
• /
• pp.807-817
• /
• 2014

본 논문은 최근 많은 관심을 받는 미세먼지 (PM10)의 일별 평균농도에 대해서 전국 16개 시도에서 2008년부터 2011년까지 관측한 다변량 시계열 자료에 대한 연구이다. 다변량 시계열 모형을 이용해서 시간 및 공간에 대한 상관관계를 동시에 고려, 일변량 혹은 특정 지역에 국한해서 분석한 기존의 연구와 차별성을 두었다. 또한 Davis 등 (2013)이 제안한 부분 스펙트럼 일관성 (partial spectral coherence)을 통해 다른 지역간의 상호 의존성을 파악하고 이를 토대로 변수 선택을 통해 희박벡터자기회귀모형 (sVAR; sparse vector autoregressive model)을 적합하는 방법론을 적용하여 고차원 자료 분석의 단점 및 한계를 보완하였으며 예측력 비교를 통해서 sVAR 모형 적합의 타당성을 검증하였다.

• Journal of applied mathematics & informatics
• /
• 제5권2호
• /
• pp.373-382
• /
• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제50권9호
• /
• pp.430-438
• /
• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• 제어로봇시스템학회논문지
• /
• 제5권6호
• /
• pp.698-704
• /
• 1999

There are several types of defects of SMDs mounted on PCB, that is, missing components, misalignment, wrong parts and poor solder joints. This research study mainly focuses on measuring of deviation of SMD leads using the partial image of component, not using the full image. This processing based on the partial image has the advantage of the reduction in calculation time compared to the full image. Since position of lead is calculated with respect of the reduction in calculation time compared to the full image. Since position of lead is calculated with respect to pad, the accuracy of the system is not dependent on percise positioning stage. The grabbed image of gray scale is converted into binary format using a cutomatic threshold. After small fragments in the image is removed by a series of morphology operations such as opening and closing, the centroids of PCB pads and SMD leads is obtained together with labeling of blobs. Translational shift and rotationial angle of SMD are succedingly estimated using above information and chip data. The expression that can calculate the maximum deviation of leads with respect to PCB pads has been derived, and inferior mounting of SMD is judged by a given criterion. Some experiments have been executed to verify this measuring scheme.