• 한국군사과학기술학회지
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• 제2권2호
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• pp.157-165
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• 1999

Production and measurement of a super-polished few-ppm-scattering mirror substrate are investigated. In order to improve the surface roughness directly determining scattering, the super-polishing process using Bowl-Feed technique is tried. The surface quality of the super-polished substrate is estimated by the phase-measuring interferometer. For the reliable roughness measurement using the interferometer, data averaging method is applied so that the optimal data averaging condition, 30 phase-data averaging and 20 intensity-data averaging, minimizing the measurement error is experimently searched. Based on the optimal data averaging condition, surface roughness of home-made mirror substrate is measured to be less than $0.5{\AA}$ rms corresponding to 2-ppm total-integrated-scattering.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• 한국음향학회지
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• 제10권2호
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• pp.23-28
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• 1991

본 논문을 특정화자에 대한 단독어 음성 인식에 대한 연구이다. 우리는 표준패턴으로서 변형된 dynamic linear averaging 방법을 이용한 DTW 음성 인식 시스템을 제안한다. 57개의 모든 도시명이 인식 대상 어휘로 선정되었고 12차 LPC cepstram 계수를 특징계수로 사용하였다. 이 논문은 표준패턴으로 변형된 dynamic linear averaging 방법을 이용하여 인식 실험을 한것 이외에도 같은 데이터 같은 조건상에서 causal 방법과 dynamic averaging방법, linear averaging방법, clustering 방법을 이용하여 실험하였다. 실험결과로 변형시킨 dynamic linear averaging 방법을 이용한 DTW 음성인식이 97.6%로 가장 좋은 인식율을 보였다.

• 한국대기환경학회지
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• 제2권1호
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• pp.41-54
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• 1986

Methods used for averaging PH's of rainwater and site representation have been studied, Statistical analysis was attempted regarding effects of ionic components on PH's utilizing 847 data altogether obtained in two years, 1984 and 1985. The outcome of the study may be assumarized as follows: 1. Methods for Averaging PH Volume weighted method is considered to be acceptable providing that precipitation is measured at the same time when the samples are taken. Without precipitation data a simple averaging method should be the next choice. 2. Site Representation A statistical method used for optimizing a monitoring newtork was applied using the data collected. Because of a limited number of data, no discernible conclusion can be reached suggesting that the method can serve as a good guide when the data base becomes more reliable. 3. A good correlation appears to exist betwen conductivities and ionic components in rainwater. It would, therefore, be possible to certain extend to estimate ionic concentrations from conductivity measurements by correlation equations. 4. The acidity of rainwater is effected by $SO_4^{2-}, NO_3^-, Cl^- and NH_4^+ with SO_4^{2-}$ being the most significant as demonstrated by standardized regression analysis.

• 한국환경과학회지
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• 제20권1호
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• pp.107-118
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• 2011

While the assessment of mean flow field is very important to characterize the hydrodynamic aspect of the flow regime in river, the conventional methodologies have required very time-consuming efforts and cost to obtain the mean flow field. The paper provides an efficient technique to quickly assess mean flow field by developing and applying spatial averaging method utilizing repeatedly surveyed acoustic Doppler current profiler(ADCP)'s cross-sectional measurements. ADCP has been widely used in measuring the detailed velocity and discharge in the last two decades. In order to validate the proposed spatial averaging method, the averaged velocity filed using the spatial averaging was compared with the bench-mark data computed by the time-averaging of the consistent fix-point ADCP measurement, which has been known as a valid but a bit inefficient way to obtain mean velocity field. The comparison showed a good agreement between two methods, which indicates that the spatial averaging method is able to be used as a surrogate way to assess the mean flow field. Bed shear stress distribution, which is a derived hydrodynamic quantity from the mean velocity field, was additionally computed by using both spatial and time-averaging methods, and they were compared each other so as to validate the spatial averaging method. This comparison also gave a good agreement. Therefore, such comparisons proved the validity of the spatial averaging to quickly assess mean flow field. The mean velocity field and its derived riverine quantities can be actively used for characterizing the flow dynamics as well as potentially applicable for validating numerical simulations.

• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.84-94
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• 2011

In this paper, we propose a method to provide the distribution of option price under local volatility model when market-provided implied volatility data are given. The local volatility model is one of the most widely used smile-consistent models. In local volatility model, the volatility is a deterministic function of the random stock price. Before estimating local volatility surface (LVS), we need to estimate implied volatility surfaces (IVS) from market data. To do this we use local polynomial smoothing method. Then we apply the Dupire formula to estimate the resulting LVS. However, the result is dependent on the bandwidth of kernel function employed in local polynomial smoothing method and to solve this problem, the proposed method in this paper makes use of model averaging approach by means of bandwidth priors, and then produces a robust local volatility surface estimation with a confidence interval. After constructing LVS, we price barrier option with the LVS estimation through Monte Carlo simulation. To show the merits of our proposed method, we have conducted experiments on simulated and market data which are relevant to KOSPI200 call equity linked warrants (ELWs.) We could show by these experiments that the results of the proposed method are quite reasonable and acceptable when compared to the previous works.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.589-598
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• 2018

To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA). PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C), PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC), to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor ($W^a$) with a weighting factor based on the Euclidean distance ($W^d$), and the third approach proposes applying $W^d$, TC, and C, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1) identify and isolate a drifted sensor that should undergo calibration, (2) identify a faulty sensor/s due to long and continuous missing data range, and (3) identify a healthy sensor.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
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• pp.263-266
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• 2007

본 논문은 베이지안 통계 추론에 대하여 논의한다. 논문은 베이지안 추론, Markov Chain과 Monte Carlo 적분, MCMC(Markov Chain Monte Carlo) 기법, Metropolis-Hastings 알고리즘, Gibbs 샘플링, Maximum Likelihood Estimation, EM 알고리즘, 상실된 데이터 보완 기법, BMA(Bayesian Model Averaging) 순서로 논의를 진행한다. 이러한 통계적 기법들은 대용량의 데이터를 처리하는 생물학, 의학, 생명 공학, 과학과 공학, 그리고 일반 데이터 조사와 처리 등에 사용되고 있으며, 최적의 추론 결과를 이끌어 내는데 중요한 방법을 제공하고 있다. 그리고 마지막으로 PC(Principal Component) 분석 기법에 대하여 논의한다. PC 분석 기법도 데이터 분석과 연구에 많이 활용된다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 추계학술대회
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• pp.265-268
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• 1995

Signal averaging technique to improve signal-to-noise ratio has widely been used in various fields, especially in electrophysiology. Estimation of the EP(evoked potential) signal using the conventional averaging method fails to correctly reconstruct the original signal under EEG(electroencephalogram) noise especial]y when the latency times of the evoked potential are not identical. Therefore, a technique based on the bispectrum averaging was proposed for recovering signal waveform from a set o noisy signals with variable signal dalay. In this paper an improved bispectrum estimation technique of the RP signal is proposed using a confidence thresholding of the EP signal in frequency domain in which energy distribution of the EP signal is usually not uniform. The suggested technique is coupled with the conventional bispectrum estimation technique such as least square method and recursive method. Some results with simulated data and real EP signal are shown.

• 대한원격탐사학회지
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• 제28권6호
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.