• 상하수도학회지
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• 제7권1호
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• pp.29-36
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• 1993

The complex nature of low flow mixing in natural channels has been investigated using both laboratory experiments and the numerical solution of a proposed mathematical model that is based on a set of mass balance equations describing the mixing and mass exchange mechanisms. Laboratory experiments, which involved collection of channel geometry, hydraulic, and dye dispersion test data, were conducted in a model of four pool and riffle sequences in a 49-m long tilting flume. The experimental results show that flow over the model pool-riffle sequences is highly non-uniform. Concentration-time curves are significantly skewed with long tails. Comparison between measured and predicted concentration-time curves shows good agreement in the general shape, peak concentration and time to peak. The proposed model shows significant improvement over the conventional one-dimensional dispersion model in predicting natural mixing processes in open channels under low flow conditions through pools and riffles.

• 대한조선학회논문집
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• 제41권2호
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• pp.114-120
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• 2004

Propeller blades have complex airfoil section type geometry and the thickness is continuously varied to both its length and cord-wise direction. in the present research, the finite element analysis program PROSTEC (Propeller Stress Evaluation Code) is developed to calculate the structural responses of propeller blades in irregular ship wake field. To represent the curved and skewed geometry of propeller blades accurately, 20-node curved solid element using the quadratic shape function is adopted. Input data for the analysis including the geometry and pressure distribution of propeller blades can be generated automatically from the propeller design program. And to visualize the results of analysis on windows system conveniently, the post processor PROSTEC-POST is developed.

• 천문학회보
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• 제39권1호
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• pp.57.2-57.2
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• 2014

Protostellar jets and outflows are signatures of star formation and promising mechanisms for driving supersonic turbulence in molecular clouds. We quantify outflow-driven turbulence through three-dimensional numerical simulations using an isothermal version of the total variation diminishing code. We drive turbulence in real space using a simplified spherical outflow model, analyze the data through density probability distribution functions (PDFs), and investigate density and velocity power spectra. The real-space turbulence-driving method produces a negatively skewed density PDF possessing an enhanced tail on the low-density side. It deviates from the log-normal distributions typically obtained from Fourier-space turbulence driving at low densities, but can provide a good fit at high densities, particularly in terms of mass-weighted rather than volume-weighted density PDF. We find shallow density power-spectra of -1.2. It is attributed to spherical shocks of outflows themselves or shocks formed by the interaction of outflows. The total velocity power-spectrum is found to be -2.0, representative of the shock dominated Burger's turbulence model. Our density weighted velocity power spectrum is measured as -1.6, slightly less that the Kolmogorov scaling values found in previous works.

• International Journal of Reliability and Applications
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• 제13권2호
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• pp.91-104
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• 2012

Sarhan and Kundu (2009) introduced a new distribution named as the generalized linear failure rate distribution. This distribution generalizes several well known distributions. The probability density function of the generalized linear failure rate distribution can be right skewed or unimodal and its hazard function can be increasing, decreasing or bathtub shaped. This distribution can be used quite effectively to analyze lifetime data in place of linear failure rate, generalized exponential and generalized Rayleigh distributions. In this paper, we apply the simulated annealing algorithm to obtain the maximum likelihood point estimates of the parameters of the generalized linear failure rate distribution. Simulated annealing algorithm can not only find the global optimum; it is also less likely to fail because it is a very robust algorithm. The estimators obtained using simulated annealing algorithm have been compared with the corresponding traditional maximum likelihood estimators for their risks.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.766-768
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• 2003

Selectivity estimation for spatial query is curial in Spatial Database Management Systems(SDBMS). Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count arising from properties of spatial dataset, they can not get such effects in little memory space.* Therefore, we need to compress spatial dataset into little memory. In this paper, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results. Our method is based on two techniques:(a)MinSkew partitioning algorithm which deal with skewed spatial datasets. efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. The experimental result shows that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (B)
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• pp.19-21
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• 2004

이 논문은 인덱스가 존재하지 않는 두 입력 테이블의 공간 조인 연산 과정 중 여과 단계 처리에 중점을 둔다. 관련 연구는 Spatial Hash Join(SHJ)과 Scalable Sweeping-Based Spatial Join(SSSJ) 알고리즘이 대표적이다. 하지만 조인을 위한 입력 테이블의 객체들이 편중 분포할 경우 성능이 급격히 저하되는 문제를 가지고 있다. 따라서, 이 논문에서는 이러한 문제를 해결하기 위해 기존 SHJ 알고리즘과 SSSJ 알고리즘의 특성을 이용한 Spatial Hash Strip Join(SHSJ) 알고리즘을 제안한다. 기존 SHJ 알고리즘과의 차이점은 입력 데이터 집합을 버킷에 할당할 때 버킷 용량에 제한을 두지 않는다는 점과 버킷의 조인 단계에서 I/O 성능의 향상을 위해 우수한 SSSJ 알고리즘을 사용한다는 것이다. 끝으로 이 논문에서 제안한 SHSJ 알고리즘의 성능은 실제 Tiger/line 데이터를 이용하여 실험한 결과 기존의 SHJ와 SSSJ 알고리즘 보다 편중된 입력 테이블의 조인 연산에 대해 월등히 우수함이 검증되었다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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• pp.656-659
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• 2014

오픈소스로부터 촉발된 분산 시스템의 보편화로 기존 상용 시스템으로는 제공하지 못한 다양한 종류의 서비스가 각광받고 있다. 특히, 테라바이트 단위를 넘어 페타바이트 단위의 데이터를 다루는 서비스의 등장으로 드러난 오픈소스 분산 시스템의 문제를 개선하기 위한 시도가 학계 및 업계에서 다각적으로 이뤄지고 있다. 이러한 시도는 새로운 방법론을 제시하는 것에서부터 기존 분산 데이터베이스 관리 시스템(Distributed DBMS)에서 사용된 방법론들을 적용하는 것까지 다양하게 이뤄지고 있다. 본 논문에서는 특정 키 값(Key Value)에 불균등 분포된 데이터에 대한 조인 연산의 탐색 공간을 밀집 인덱스를 통해 줄여 비교적 높은 시간 복잡도를 완화하는 방법론을 제시하고자 한다.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.625-639
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• 2020

The gamma distribution is a flexible right-skewed distribution widely used in many areas, and it is of great interest to estimate the probability of a random variable exceeding a specified value in survival and reliability analysis. Therefore, the study develops a fixed-accuracy confidence interval for P(X > c) when X follows a gamma distribution, Γ(α, β), and c is a preassigned positive constant through: 1) a purely sequential procedure with known shape parameter α and unknown rate parameter β; and 2) a nonparametric purely sequential procedure with both shape and rate parameters unknown. Both procedures enjoy appealing asymptotic first-order efficiency and asymptotic consistency properties. Extensive simulations validate the theoretical findings. Three real-life data examples from health studies and steel manufacturing study are discussed to illustrate the practical applicability of both procedures.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.