• 물과 미래
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• 제28권1호
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• pp.81-90
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• 1995

본 연구에서는 장기간의 수문기상자료를 보유하고 있으나 유출량자료의 관측년한이 짧은 유역에서 장기간의 월유출량자료를 확장하고 예측할 수 있는 추계학적 시스템 모형을 개발하고자 한다. 그 방법으로 주기성과 경향성을 갖는 월유출량, 월강수량 및 윌증발량자료를 시계열 분석하여 seasonal ARIMA 형태의 단변량 모형을 유도하는 한편, 각 계열간의 교차상관분석으로부터 월강수량 및 윌증발량을 입력변수로 하고 월유출량을 출력변수로 하는 다중 입력-단일 출력관계의 설명모형을 유도하여 단변량 시계열모형과 비교 검토하였다. 본 연구의 결과 월유출량자료의 확장과 예측에 있어서 다중 입출력모형의 정확성과 적용가능성이 매우 높은 것으로 판단되었다.

• Wind and Structures
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• 제11권3호
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• pp.209-220
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• 2008

Flutter derivatives provide the basis of predicting the critical wind speed in flutter and buffeting analysis of long-span cable-supported bridges. Many studies have been performed on the methods and applications of identification of flutter derivatives of bridge decks under wind action. In fact, strong wind, especially typhoon, is always accompanied by heavy rain. Then, what is the effect of rain on flutter derivatives and flutter critical wind speed of bridges? Unfortunately, there have been no studies on this subject. This paper makes an initial study on this problem. Covariance-driven Stochastic Subspace Identification (SSI in short) which is capable of estimating the flutter derivatives of bridge decks from their steady random responses is presented first. An experimental set-up is specially designed and manufactured to produce the conditions of rain and wind. Wind tunnel tests of a quasi-streamlined thin plate model are conducted under conditions of only wind action and simultaneous wind-rain action, respectively. The flutter derivatives are then extracted by the SSI method, and comparisons are made between the flutter derivatives under the two different conditions. The comparison results tentatively indicate that rain has non-trivial effects on flutter derivatives, especially on and $H_2$ and $A_2$thus the flutter critical wind speeds of bridges.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• 한국환경과학회지
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• 제8권3호
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• pp.349-354
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• 1999

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the mose cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must bulid a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

• 한국시뮬레이션학회논문지
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• 제10권2호
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• pp.61-74
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• 2001

The reliability evaluation of the large scale network becomes very complicate according to the growing size of network. Moreover if the reliability is not constant but follows probability distribution function, it is almost impossible to compute them in theory. This paper studies the network evaluation methods in order to overcome such difficulties. For this an efficient path set algorithm which seeks the path set connecting the start and terminal nodes efficiently is developed. Also, various variance reduction techniques are applied to compute the system reliability to enhance the simulation performance. As a numerical example, a large scale network is given. The comparisons of the path set algorithm and the variance reduction techniques are discussed.

• 대한전기학회논문지
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• 제39권7호
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• pp.729-739
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• 1990

This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

• International Journal of Reliability and Applications
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• 제2권1호
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• pp.37-48
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• 2001

This paper presents a new dynamic human reliability analysis method and its application for quantifying the human error probabilities in implementing management action. For comparisons of current HRA methods with the new method, the characteristics of THERP, HCR, and SLIM-MAUD, which are most frequency used method in PSAs, are discussed. The action associated with implementation of the cavity flooding during a station blackout sequence is considered for its application. This method is based on the concepts of the quantified correlation between the performance requirement and performance achievement. The MAAP 3.0B code and Latin Hypercube sampling technique are used to determine the uncertainty of the performance achievement parameter. Meanwhile, the value of the performance requirement parameter is obtained from interviews. Based on these stochastic obtained, human error probabilities are calculated with respect to the various means and variances of the things. It is shown that this method is very flexible in that it can be applied to any kind of the operator actions, including the actions associated with the implementation of accident management strategies.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.791-795
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• 2005

A simulation model is proposed to evaluate the effect of correlations between activity durations on the overall project duration. The proposed model incorporates NORTA, a recent developed statistical method, into the simulation process to allow arbitrarily specified marginal distributions for activity durations and any desired correlation structure. The generality is of practical value when systematic data is not available and planners have to rely on arbitrary experts' estimation, which may involve a mixed situation when some activity durations are continuously distributed whereas others are discrete outcomes. The proposed model is validated by showing that the correlation coefficients of the simulation results are close to the originally specified ones. The simulation results are compared to two conventional approaches: PERT and simulation without correlation. The comparisons illustrate that the proposed model can provide important management information, which would otherwise be distorted due to the neglect of the correlations between activity durations.

• Earthquakes and Structures
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• 제14권3호
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• pp.215-227
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• 2018

Supplemental passive control devices are widely considered as an important tool to mitigate the dynamic response of a building under seismic excitation. Nevertheless, a systematic method for strategically placing dampers in the buildings is not prescribed in building codes and guidelines. Many deterministic and stochastic methods have been proposed by previous researchers to investigate the optimum distribution of the viscous dampers in the steel frames. However, the seismic performances of the retrofitted buildings that are under large earthquake intensity levels or near collapse state have not been evaluated by any seismic research. Recent years, an increasing number of studies utilize genetic algorithms (GA) to explore the complex engineering optimization problems. GA interfaced with nonlinear response history (NRH) analysis is considered as one of the most powerful and popular stochastic methods to deal with the nonlinear optimization problem of damper distribution. In this paper, the effectiveness and the efficiency of GA on optimizing damper distribution are first evaluated by strong ground motions associated with the collapse failure. A practical optimization framework using GA and NRH analysis is proposed for optimizing the distribution of the fluid viscous dampers within the moment resisting frames (MRF) regarding the improvements of large drifts under intensive seismic context. Both a 10-storey and a 20-storey building are involved to explore higher mode effect. A far-fault and a near-fault earthquake environment are also considered for the frames under different seismic intensity levels. To evaluate the improvements obtained from the GA optimization regarding the collapse performance of the buildings, Incremental Dynamic Analysis (IDA) is conducted and comparisons are made between the GA damper distribution and stiffness proportional damping distribution on the collapse probability of the retrofitted frames.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]