• ETRI Journal
• /
• 제32권1호
• /
• pp.22-32
• /
• 2010

The pervasive game environments have activated explosive growth of the Internet over recent decades. Thus, understanding Internet traffic characteristics and precise classification have become important issues in network management, resource provisioning, and game application development. Naturally, much attention has been given to analyzing and modeling game traffic. Little research, however, has been undertaken on the classification of game traffic. In this paper, we perform an interpretive traffic analysis of popular game applications at the transport layer and propose a new classification method based on a simple decision tree, called an alternative decision tree (ADT), which utilizes the statistical traffic characteristics of game applications. Experimental results show that ADT precisely classifies game traffic from other application traffic types with limited traffic features and a small number of packets, while maintaining low complexity by utilizing a simple decision tree.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2002년도 학술발표회 논문집(II)
• /
• pp.1195-1204
• /
• 2002

The impacts of El Nino Southern Oscillation (ENSO) phenomenon on climate are widespread and extend far beyond the tropical Pacific. The phenomenon can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure barometric difference between Tahiti and Darwin, Australia. Its best-known extreme is the El Nino event. In this study, general statistical characteristics of SOI and the data from which it is derived (i.e. mean sea level pressure data at Tahiti and Darwin) are presented as guidance when using SOI far other analyses. The characteristics include the availability of the barometric pressure data, statistics of monthly pressure data, correlation of SO intensity, frequency analysis of SOI by magnitude and by month (January-December), duration properties of SOI by run analysis.

• Communications for Statistical Applications and Methods
• /
• 제29권2호
• /
• pp.161-176
• /
• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• 한국농공학회지
• /
• 제19권3호
• /
• pp.4472-4482
• /
• 1977

In order to estimate furture consumtive use, some statistical characteristics of 22-year pan evaporation data at four selected stations were calculated in this study. Districal distribution, trend analysis and time-series, statistical and periodic analysis for annual, monethly and ten-day values were performed in the statistical analysis. The stations are Seoul, Taeku, Jeonju and Mokpo for monthly data, and Suweon data are compared to the reported Penman values. The results are as followed: 1. Annual evaporation ranged to 990-1,375mm varying with the locations of the stations. The Districal distribution of evaporation in the Republic is shown in Fig. 1. 2. The trend analysis for annual evaporation resulted in detail in Table 2 and Fig. 2, through simple moving average methods. The results show relatively short-period data of about 10 years would be acceptable for field use. 3. The means and dispersions of monthly evaporation at four stations are detailed in Table 3. 4. The monthly evaporation approached to the trend of normal distribution Fig. 3 showed the examples of normal distribution for each typical monthly data. 5. The correlograms detailed in Fig. 4, shows the time-series characteristics of monthly evaporation, whose periodic term should be twelve months. 6. The periodic analysis for monthly evapolation results in Table 4. Fig. 5 shows the comparison of estimated values to actual and the trend approaches Shuster's periodic trend. 7. A periodic description of days after March 1 for irrigation periods was developed to predict ten-day evaporation in Fig. 6. The ten-day etraporation is different in the distribution form and occurence period of maximum values from the reported Penman's man's evapotranspiration.

• 방송공학회논문지
• /
• 제17권6호
• /
• pp.1091-1105
• /
• 2012

본 논문에서는 현재 표준화가 진행 중인 HEVC (high efficiency video coding) 부호화기의 고속화, 최적화, 병렬화 연구에 앞서 통계적 특성 및 복잡도 분석을 수행하였다. HEVC는 H.264/AVC에 비해 약 2배의 압축 성능을 나타내지만 부호화기 복잡도는 크게 증가하여 이는 앞으로 해결해야할 문제로 남아있다. HEVC의 높은 부호화기 복잡도를 해결하기 위한 고속화, 최적화, 병렬화 연구에 앞서, 본 논문에서는 HEVC 참조소프트웨어인 HM 7.1을 이용하여 HEVC 부호화기의 복잡도를 측정하였다. 추가적으로, 실제 응용에서 사용될 고속 HEVC 부호화기 소프트웨어에 대한 예상 복잡도를 고속 알고리듬이 적용된 HM 7.1 소프트웨어로 측정하였다. 복잡도 측정은 공통 실험 영상 및 조건을 사용하였으며 PC 환경에서 부호화기 소프트웨어의 동작 사이클을 측정하고 이를 분석하였다. 또한, 부호화를 통해 생성된 비트스트림을 이용하여 HEVC 부호화기 소프트웨어의 부호화 구조에 따른 통계적 특성과 제한적 부호화에 따른 통계적 특성에 대하여 제시하고 이를 분석한다.

• Journal of Animal Science and Technology
• /
• 제54권6호
• /
• pp.463-469
• /
• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.

• 한국콘텐츠학회논문지
• /
• 제13권1호
• /
• pp.300-310
• /
• 2013

본 연구는 주관적 구강건강 인지 특성과 구강보건 지식 및 구강보건 실천 행태간의 연관 요인들을 알아보고자, 2011년 6월 23일부터 2012년 3월 24일까지 일부지역 성인 763명의 설문지를 채택 분석하여 다음과 같은 결론을 얻었다. 1. 일반적 특성에 따른 구강보건 지식은 성별, 학력, 직업에서 통계적으로 유의하였다(p<0.001). 2. 일반적 특성에 따른 구강보건 실천 행태는 성별, 연령, 학력, 직업, 소득에서 통계적으로 유의하였다(p<0.001). 3. 주관적 구강건강 인지 특성에 따른 구강보건 지식과 구강보건 실천 행태에서는 공통적으로 구강건강의 중요성, 주관적 구강건강 상태, 구강건강 관심도에서 통계적으로 유의하였다(p<0.001). 4. 구강보건 지식과 구강보건 실천 행태의 관련 요인 회귀분석에서는 공통적으로 구강건강 관심도에서 통계적으로 유의하였으며(p<0.001) 서로 연관성이 있었다. 이상의 결과로 볼 때 구강보건 교육프로그램 개발과 구강보건교육 방법 연구 및 실시에 있어서 개인의 일반적 특성 및 주관적 구강건강 인지 특성이 충분히 고려될 때 실질적인 국민의 구강건강 증진을 위한 구강보건 실천력이 향상 될 수 있을 것으로 사료되었다.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 2006년도 추계 학술대회
• /
• pp.205-210
• /
• 2006

Nowaday manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. It is measured automatically do much quality characteristics thereby and great many data happen in a day. corporations is important if have gotten fast information that are useful from wide data to go first in international competition according to these change. Statistical process control(SPC) techniques are used as a problem solution tool at manufacturing process until present. However, this statistical methods is not applied more extensively because have much restrictions in realistic problem. In this paper, wish to develop more realistic and scientific new statistical design techniques doing to integrate data mining(DM) and statistical methods by the alternative to cope these problem. First step selects significant factor using DM techniques from datas of manufacturing process including much factors and second step wish to find optimum of process after get the estimated response function through response surf ace methodology(RSM) that is statistical techniques.

• 대한안전경영과학회지
• /
• 제2권4호
• /
• pp.9-17
• /
• 2000

For the accident prevention, it is necessary to identify common factors and characteristics contributing to the industrial accidents. This study concerned with the accident characteristics by age of injured person in the manufacturing industries. To make the statistical comparisons for the accident characteristics by age group, we derived the distributions of occupational injuries in terms of accident time, accident type, activity at time of accident, injury type, injured part of body, agency of accident, and workday losses. The results show that the characteristics of the occupational injuries by age group are not significantly different in a31 characteristics investigated in this study.

• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.457-469
• /
• 2003

In this paper, we have proposed the efficient algorithm that can segment the video scene change using a various statistical characteristics obtained from by applying the wavelet transformation for each frames. Our method firstly extracts the histogram features from low frequency subband of wavelet-transformed image and then uses these features to detect the abrupt scene change. Second, it extracts the edge information from applying the mesh method to the high frequency subband of transformed image. We quantify the extracted edge information as the values of variance characteristic of each pixel and use these values to detect the gradual scene change. And we have also proposed an algorithm how extract the proper key frame from segmented video scene. Experiment results show that the proposed method is both very efficient algorithm in segmenting video frames and also is to become the appropriate key frame extraction method.