• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.57.1-57.1
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• 2005

• Journal of the KSME
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• v.34 no.9
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• pp.688-697
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• 1994

유체유동이나 열전달 그리고 물질전달 (물질의 혼합 및 확산) 또는 이들 현상이 복합적으로 나 타나는 각종 기계의 설계와 성능 해석을 하기 위해서는 그 현상을 지배하는 편미분 방정식들의 해를 수치적으로 구해야 한다. 유동 상태가 충류 유동인 경우는 지배 방정식의 수가 알고자 하는 미지변수 즉 속도, 압력, 온도, 농도 등의 개수와 같고 또한 이들 변수들의 변동이 그리 심하지 않기 때문에 적절한 수치 해법을 사용하면 그 해를 구할 수 있다. 그러나 난류유동의 경우에는 변수들이 시간상으로 또한 공간적으로 대단히 심하게 변동(fluctuation)하기 때문에 공 학적으로 우리가 원하는 정보들, 즉, 표면 마찰저항이나 양력, 얼전달 계수, 물질 확산계수 등을 현재 수준의 전자계산기로 계산하는 데는 계산시간이 엄청나게 소요될 뿐만 아니라 변수 저장 메모리도 과도하게 차지하기 때문에 실제적인 계산 방법이 되지 못하고 있다. 이러한 이유로 변수들의 순간 변화 상태를 나타내는 지배 방정식들을 해석하는 대신에 이들 지배 방정식의 시 간평균을 취하여 유도한 난류 방정식들을 사용하게 된다. 그러나 이 시간 평균 과정에서 파생 되는 또 다른 미지의 난류 변수들 때문에 난류 지배 방정식에 있어서는 그 지배 방정식의 개수 보다 미지 변수의 개수가 많아져서 난류 지배 방정식을 풀기 위해서는 시간평균 과정에서 나타난 난류 변수들을 원래 있던 미지 변수들의 함수나 방정식의 형태로 가정할 필요가 있게 되는데 이 가정되는 함수 관계들을 난류 계산 모형이라고 한다. 난류 계산 모형은 물리적인 통찰과 직관에 의해서 실용적인 형태로 가정되기도 하지만 최근에는 논리적으로 엄격한 모형 원칙에 따른 수 학적인 방법으로 유도되고 있는데 이 글에서는 일반 독자들이 쉽게 이해할 수 있도록 마하수가 낮은 2차원 비압축성 난류 유동을 예로 들어 x-y 직교 좌표계에서 표현되는 난류 계산 모형들을 소개하고 앞으로듸 발전 방향을 개관하며 현재의 응용 사례들을 예로 들어 모형의 성능을 비교 하여 보기로 한다.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.793-808
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• 2017

We compare various variable screening methods on multiclass classification problems when the data is ultrahigh-dimensional. Two different approaches were considered: (1) pairwise extension from binary classification via one versus one or one versus rest comparisons and (2) direct classification of multiclass responses. We conducted extensive simulation studies under different conditions: heavy tailed explanatory variables, correlated signal and noise variables, correlated joint distributions but uncorrelated marginals, and unbalanced response variables. We then analyzed real data to examine the performance of the methods. The results showed that model-free methods perform better for multiclass classification problems as well as binary ones.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.551-557
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• 2019

Diagnosis of abnormal faults is essential for producing high quality products. The role of real-time diagnosis is quite increasing in the batch processes of producing high value-added products such as semiconductors, pharmaceuticals, and so forth. In this study, we evaluate the effect of variable selection and future-value estimation techniques on the performance of the diagnosis system, which is based on nonlinear classification and measurement data. The diagnostic performance can be improved by selecting only the variables that are important and have high contribution for diagnosis. Thus, the diagnostic performance of several variable selection techniques is compared and evaluated. In addition, missing data of a new batch, called future observations, should be estimated because the full data of a new batch is not available before the end of the cycle. In this work the use of different estimation techniques is analyzed. A case study on the polyvinyl chloride batch process was carried out so that optimal variable selection and estimation methods were obtained: maximum 21.9% and 13.3% improvement by variable selection and maximum 25.8% and 15.2% improvement by estimation methods.

• Journal of Korea Game Society
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• v.19 no.6
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• pp.71-82
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• 2019

This paper analyzes performance difference between Unreal Engine's built-in network solution and IOCP server. To do this, we developed IOCP server and 3D game with Unreal Engine 4. Also we considered the game variable obfuscation program to prevent the modification of the memory of the code-modulated game hacking program. This paper used SCUE4 Anti-Cheat Solution, which is Unreal Engine's solution, to study preventing memory modification and to analyze performance trade-offs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.262-267
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• 2007

퍼지 ART 알고리즘에서 경계 변수는 패턴들을 클러스터링하는데 있어서 반지름 값이 되며 임의의 패턴과 저장된 패턴과의 불일치(mismatch) 허용도를 결정한다. 이 경계 변수가 크면 입력 벡터와 기대 벡터 사이에 약간의 차이가 있어도 새로운 카테고리(category)로 분류하게 된다. 반대로 경계 변수가 작으면 입력 벡터와 기대 벡터 사이에 많은 차이가 있더라도 유사성이 인정되어 입력 벡터들을 대략적으로 분류한다. 따라서 영상 인식에 적용하기 위해서는 경험적으로 경계 변수를 설정해야 단점이 있다. 그리고 연결 가중치를 조정하는 과정에서 학습률의 설정에 따라 저장된 패턴들의 정보들이 손실되는 경우가 발생하여 인식율을 저하시킨다. 본 논문에서는 퍼지 ART 알고리즘의 문제점을 개선하기 위하여 퍼지 논리 접속 연산자를 이용하여 경계 변수를 동적으로 조정하고 저장 패턴들과 학습 패턴간의 실제적인 왜곡 정도를 충분히 고려하여 승자 노드로 선택된 빈도수를 학습률로 설정하여 가중치 조정에 적용한 개선된 퍼지 ART 알고리즘을 제안하였다. 제안된 방법의 성능을 확인하기 위해서 실제 영문 명함에서 추출한 영문자들을 대상으로 실험한 결과, 기존의 ART1과 ART2 알고리즘이나 퍼지 ART 알고리즘보다 클러스터의 수가 적게 생성되었고 인식 성능도 기존의 방법들보다 우수한 성능이 있음을 확인하였다.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.475-481
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• 2015

We use polynomial regression instead of linear regression if there is a nonlinear relation between a dependent variable and independent variables in a regression analysis. The performance of polynomial regression, however, may deteriorate because of the correlation caused by the power terms of independent variables. We present a polynomial regression model for the numerical inversion of PGF and show that polynomial regression results in the deterioration of the estimation of the coefficients. We apply principal components regression to the polynomial regression model and show that principal components regression dramatically improves the performance of the parameter estimation.

• Database Research
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• v.34 no.3
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• pp.159-169
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• 2018

Discretization of continuous variables intended to improve the performance of various algorithms such as data mining by transforming quantitative variables into qualitative variables. If we use appropriate discretization techniques for data, we can expect not only better performance of classification algorithms, but also accurate and concise interpretation of results and speed improvements. Various discretization techniques have been studied up to now, and however there is still demand of research on discretization studies. In this paper, we propose a new discretization technique to set the cut-point using Wasserstein distance with considering the distribution of continuous variable values with classes of data. We show the superiority of the proposed method through the performance comparison between the proposed method and the existing proven methods.