• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.477-479
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• 1998

인터넷상에서의 다자간 회의는 수천명의 참가하는 대규모의 회의가 될 수 있으므로 다자간 회의 시스템에서는 확장성이 중요하다. 현재의 인터넷상에서의 다자간 회의 시스템은 대부분 RTP/RTCP를 이용하는데. RTCP를 이용한 피드백 정보 전송의 빈도 수와 전송 시간의 동기화 현상이 다자간 회의 시스템의 확장성에 큰 영향을 준다. 즉, 세션 참가자 수가 증가함에 따라 네트워크에 전송되는 RTCP 패킷의 숫자가 기하급수적으로 증가하게 된다. 피드백 정보의 전송 빈도 수 감소와 동기화 현상을 방지하기 위해 도입한 무작위 지연기법은 너무 단순하여 수많은 참가자들이 동시에 피드백 정보를 교환할 때에 피드백 정보 전송시간의 동기화 현상을 피하지 못해 네트워크에 혼잡 상태를 유발할 수 있다. 이러한 혼잡을 예방하기 위한 기존의 RTP/RTCP 확장 기법의 피드백 정보 전송지연은 송신자가 수신자의네트워크 상태에 따라 효율적으로 전송을 제어할 수 없게 한다. 본 논문에서는 RTP/RTCP 확장성을 증가시키는 기존의 기법들의 성능을 평가하고, 확장성 증가와 동시에 성능이 향상된 RTP/RTCP 확장 기법을 제안한다. 본 논문에서는 확장성 증가와 피드백 지연 정도를 줄이기 위해 빠른 제고 기법을 제안한다. 빠른 재고 기법은 두가지 세부 기법으로 나누어지는데, 첫째는 네트워크의 상태의 변화에 따라 RTCP피드백 정보의 전송지연 정도를 조절하는 것이고, 둘째는 무작위 지연을 선택적으로 조정하려 피드백 정보를 오랜 기간 동안에 보내지 못한 참가자에게 우선권을 주는 것이다. 본 논문에서는 시뮬레이션을 통해 제안된 확장성 기법을 이용할 때에 기존 방식에 비해 거의 비슷한 확장성을 보이면서도 초기 RTCP패킷 전송지연이 50%정도 감소함을 보여준다.구현되고 있다.팔일 전송 기법을 각각 제시하고 실험을 통해 이들의 특성을 비교분석하였다.미에서 uronic acid 함량이 두 배 이상으로 나타났다. 흑미의 uronic acid 함량이 가장 많이 용출된 분획은 sodium hydroxide 부분으로서 hemicellulose구조가 polyuronic acid의 형태인 것으로 사료된다. 추출획분의 구성단당은 여러 곡물연구의 보고와 유사하게 glucose, arabinose, xylose 함량이 대체로 높게 나타났다. 점미가 수가용성분에서 goucose대비 용출함량이 고르게 나타나는 경향을 보였고 흑미는 알칼리가용분에서 glucose가 상당량(0.68%) 포함되고 있음을 보여주었고 arabinose(0.68%), xylose(0.05%)도 다른 종류에 비해서 다량 함유한 것으로 나타났다. 흑미는 총식이섬유 함량이 높고 pectic substances, hemicellulose, uronic acid 함량이 높아서 콜레스테롤 저하 등의 효과가 기대되며 고섬유식품으로서 조리 특성 연구가 필요한 것으로 사료된다.리하였다. 얻어진 소견(所見)은 다음과 같았다. 1. 모년령(母年齡), 임신회수(姙娠回數), 임신기간(姙娠其間), 출산시체중등(出産時體重等)의 제요인(諸要因)은 주산기사망(周産基死亡)에 대(對)하여 통계적(統計的)으로 유의(有意)한 영향을 미치고 있어 $25{\sim}29$세(歲)의 연령군에서, 2번째 임신과 2번째의 출산에서 그리고 만삭의 임신 기간에, 출산시체중(出産時體重) $3.50{\sim}3.99kg$사이의 아이에서 그 주산기사망률(周産基死亡率)이 각각 가장 낮았다. 2. 사산(死産)과 초생아사망(初生兒死亡)을 구분(區分)하여 고려해

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.61-74
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• 2010

ITS (Intelligent transportation systems) collects real-time traffic data, and accumulates vest historical data. But tremendous historical data has not been managed and employed efficiently. With the introduction of data management systems like ADMS (Archived Data Management System), the potentiality of huge historical data dramatically surfs up. However, traffic data in any data management system includes missing values in nature, and one of major obstacles in applying these data has been the missing data because it makes an entire dataset useless every so often. For these reasons, imputation techniques take a key role in data management systems. To address these limitations, this paper presents a promising imputation technique which could be mounted in data management systems and robustly generates the estimations for missing values included in historical data. The developed model, based on NPR (Non-Parametric Regression) approach, employs various traffic data patterns in historical data and is designated for practical requirements such as the minimization of parameters, computational speed, the imputation of various types of missing data, and multiple imputation. The model was tested under the conditions of various missing data types. The results showed that the model outperforms reported existing approaches in the side of prediction accuracy, and meets the computational speed required to be mounted in traffic data management systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.433-433
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• 2018

한국은 지진에 대한 관심이 낮았으나, 2016년 09월 12일 경상북도 경주에서 가장 큰 규모인 5.8의 지진이 발생하였으며, 강력한 지진이 발생할 수 있다는 경고가 이어지고 있다. 지진과 관련된 정확한 원인 분석과 정량적인 평가가 체계적으로 이루어지지 않고 있어, 규모와 빈도, 위험지역 분석 등 정밀한 평가와 예방대책을 마련해야 한다. 정량적인 지진 발생 분석을 위해 본 연구에서는 지진 발생과 지하수와 같은 수문기상학적인 인자에 의해 영향을 받는다는 가설을 세우고 지하수의 변동 패턴과 지진의 발생 패턴의 유사점을 추정하였다. 이를 위해 지진자료의 통계적인 특성을 분석하였다. 그리고 지질특성이나 지각 판 운동 외에도 수문순환이 영향을 미치는지 확인하기 위해 육지와 바다에서 발생한 지진으로 구분하여 지진발생횟수와 에너지를 분석하였다. 분석결과, 육지와 바다로 구분했을 때 바다에서 더 많은 지진이 일어났다. 또한 Wilcoxon rank-sum test 비모수 추정기법을 통하여 분석한 결과 서로 다른 성질을 보여 따로 분석하였다. 그 결과, 동해와 남해, 서해와 동해가 같은 성질을 보이는 것으로 분석되었다. 그리고 육지는 8월부터 이듬해 7월까지 지진발생의 한 주기를 이룰 가능성을 보였다. 그러나 바다는 육지와 정반대로 2월부터 7월까지 많은 지진 에너지가 발생하고 있으며, 1월까지는 에너지 수준이 상대적으로 낮은 것으로 분석되었다. 이와 같이 지하수가 육지에서 바다까지 유동하는 시간으로 인해 6개월의 시간지연이 발생하는 것으로 판단된다.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.39-52
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• 2002

Traffic data by vehicle classification is difficult for mutual exchange of data due to the different vehicle classification from each other by the data sources; as a result, application of the data is very limited. In Particular. in case of TCS vehicle classification in national highways, passenger car, van and truck are mixed in one category and the practical usage is very low. The research standardize the vehicle classification to convert other data and develop the model which can estimate national highway traffic data by the standardized vehicle classification from the raw traffic data obtained at the highway tollgates. The tollgates are categorized into several groups by their features and the model estimates traffic data by the standardized vehicle classification by using the point estimation and bootstrap algorithm. The result indicates that both of the two methods above have the significant level. When considering the bias of the extreme value by the sample size, the bootstrap algorithm is more sophisticated. Using result of this study, we is expect the usage improvement of TCS data and more specific comparison between the freeway traffic investigation and link volume on freeway using the TCS data.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.117-130
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• 2008

This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.319-326
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• 2013

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of logarithmic hazard learning effects property.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.