• 터널과지하공간
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• 제10권3호
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• pp.378-386
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• 2000

본 논문은 통계적 기법에 의한 균열망 해석 프로그램 (NAPSAC)을 사용하여 암반의 수리특성을 평가하기 위해 시도되었다. 암반내 존재하는 절리의 방향성을 고려한 투수계수를 산정하기 위해 마북리 시험터널 주위의 시추자료와 막장 자료로부터 얻어진 균열에 대한 정보로부터 대상지역의 등가 투수계수를 추정하였다. 대상지역의 관찰자료 (균열망에 대한 자료, 수리지질학적 자료 )로부터 통계적 균열망 해석을 위한 입력자료를 결정하여 해석모델의 신뢰성을 확보하였다. 구현된 모델로부터 모델의 크기를 증가함에 따라서 이방성 투수계수 및 투수계수의 변화를 계산하였다. 해석결과 대상지역의 투수성은 균열군의 방향성에 의해 강한 이방성을 보였다.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.625-632
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• 2001

Principal component analysis(PCA) is a multivariate technique falling under the general title of factor analysis. The purpose of PCA is to Identify the dependence structure behind a multivariate stochastic observation In order to obtain a compact description of it. In engineering field PCA is utilized mainly (or data compression and restoration. In this paper we propose a new robust Hebbian algorithm for robust PCA. This algorithm is based on a hyperbolic tangent function due to Hampel ef al.(1989) which is known to be robust in Statistics. We do two experiments to investigate the performance of the new robust Hebbian learning algorithm for robust PCA.

• 한국농공학회지
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• 제39권6호
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• 대한교통학회지
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• 제7권1호
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• pp.57-70
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• 1989

The purpose of study is to analyze the spatial population distribution and accessibility of network in urban areas. This study examines the forty-six political subdivision cities in Korea at the end of 1983, except the four metrpolitans (Seoul, Pusan, Daeku and Incheon). Evaluation indexes are classified the spatial pupulation distribution and accessibility of network. To analyze the cities, 10 indexes and the statistical techniques such as descriptive analysis, correlation analysis, factor analysis and cluster analysis were used. According to the results of cluster analysis, 15 cities (Ulsasn, Suwon, Bucheon, Chungju and etc.) are classified dispersed cities and another 15 cities (Kwangju, Daejun, Sungnam, Mokpo and etc.) are classified concentrated cities.

• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• 응용통계연구
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• 제29권4호
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• pp.613-625
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• 2016

과학 기술의 발전은 사회를 급격하게 변화시켜 왔다. 특허 자료 분석은 현대 과학 기술의 흐름을 이해하고 미래 유망기술을 예측할 수 있게 한다. 본 연구에서는 기술의 동향을 파악하고자 1985년과 2012년 사이에 미국 특허청에 등록된 특허를 중심으로 특허 인용 네트워크를 분석한다. 주요 기술군을 파악하기 위해 PageRank 알고리즘 외에 다양한 중심성 지표를 이용하고, 통계적 네트워크 모형을 통해 유사한 기술들의 군집을 찾아내고자 한다.

• 대한원격탐사학회지
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• 제15권2호
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• pp.107-117
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• 1999

본 연구에서는 원격탐사를 이용하여 수질 파라미터들을 평가하는데 기존의 다중 회귀나 밴드비 회귀 분석을 이용한 통계적인 방법과 신경망을 이용한 방법을 비교하였다. 사용된 영상은 1996년 3월 18일 대청호 유역의 Landsat TM 영상이며, 30개의 현장 실측치가 위성이 통과하는 시간대에 샘플링되었다. 적용된 신경망은 3개의 층으로 구성된 전향 신경망이며 훈련방법으로는 역전파를 사용하였다. 본 연구에서는 가용한 훈련 데이터 셀이 작으므로 cross-validation 방법이 적용되었다. 비록 기존의 회귀분석에 의한 결과도 어느 정도 유의하게 나왔지만, 신경망에 의한 결과가 훨씬 성공적인 수행을 보여주었다. 신경망을 이용한 수질평가는 신경망이 자료의 비선형적 속성을 잘 반영해주기 때문에 기존의 통계적 기법보다 훨씬 나은 결과를 제공한다고 판단된다.

• Communications for Statistical Applications and Methods
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• 제25권6호
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• pp.659-671
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• 2018

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (3)
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• pp.49-51
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• 2003

Developers of network games have used several prediction techniques for hiding transmission delay to support the real­time requirement of network games. Nowadays many researches that are related with network game are in progress to solve delay problems more radically, such as to propose new routers architecture and transport protocols suitable to characteristics of network game traffic. So for these advanced researches the tasks to grasp the traffic characteristics of a network game are needed. In this paper we aimed to capture the traffic of MMORPG and present the statistical analysis of measured data. The measurement and the analysis were accomplished with the server of 'Lineage' that regarded as the most successful MMORPG. Next, we have implemented a traffic generator that reflects the characteristics of MMORPG and shown that the trace generated by MMORPG traffic generator had identical characteristics with actual traffic using statistical testing method. We expect that this traffic generator can be used in many researches related with a network game.