• Title/Summary/Keyword: Cumulative data

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Notes on a skew-symmetric inverse double Weibull distribution

  • Woo, Jung-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.459-465
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    • 2009
  • For an inverse double Weibull distribution which is symmetric about zero, we obtain distribution and moment of ratio of independent inverse double Weibull variables, and also obtain the cumulative distribution function and moment of a skew-symmetric inverse double Weibull distribution. And we introduce a skew-symmetric inverse double Weibull generated by a double Weibull distribution.

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Species Diversity of a Stratified Hornbeam Community in Kwangneung Forest (광릉산림에 있어서 서나무군집의 층에 따른 종다양성에 관한 연구)

  • 이광석;장남기
    • Asian Journal of Turfgrass Science
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    • v.9 no.2
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    • pp.131-136
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    • 1995
  • The herb, shrub, understory and canopy strata, which arbitrarily delineated by size classes, were sampled separately. The former one were sampled by the pin-point quadrat method. And remaining three by size quadrats, diversity (H= =$\Sigma$ Pi log Pi) of of each stratum was estimated for each set of census data. Species diversity within a stratum was independent of sample plot size above a minimum cumulative area. Diversity based on plotless and plot samples could he determined by the same equation, and by pooling the data needed to estimate diversity of each stratum.

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A Study on Cost Function of Korea Railroad Industry( I ) (우리나라 철도산업의 비용함수추정 연구(I))

  • Yoo Jae-Kyun;Kim Kyoung-Tae
    • Proceedings of the KSR Conference
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    • 2004.06a
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    • pp.392-396
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    • 2004
  • The number of parameters and the number of samples are the key point of trans-log cost function which studied recently. Most of models show that the number of parameters is more than the number of samples. Therefore, these studies gave unreliability of the estimation results. First, we surveyed theoretical cases and researches for the formulation of cost function of railroad industry. Second, we will suggest trans-log cost function by analyzing cost data of KNR. And, the cumulative data will be needed for the confidence of estimation results.

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Multivariate CUSUM Charts with Correlated Observations

  • Cho, Gyo-Young;Ahn, Young-Sun
    • Journal of the Korean Data and Information Science Society
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    • v.12 no.1
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    • pp.127-133
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    • 2001
  • In this article we establish multivariate cumulative sum (CUSUM) control charts based on residual vector with correlated observations. We first find the residual vector and its expectation and variance-covariance matrix and then evaluate the average run length (ARL) of the control charts.

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An Efficient Video Retrieval Algorithm Using Luminance Projection

  • Kim, Sang-Hyun
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.891-898
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    • 2004
  • An effective video indexing is required to manipulate large video databases. Most algorithms for video indexing have been commonly used histograms, edges, or motion features. In this paper, we propose an efficient algorithm using the luminance projection for video retrieval. To effectively index the video sequences and to reduce the computational complexity, we use the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed video indexing and video retrieval algorithm yields the higher accuracy and performance than the conventional algorithm.

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A Study on Cost Function of Korea Railroad Industry(I) (우리나라 철도산업의 비용함수추정 연구(I))

  • Yoo Jae-Kyun;Kim Kyoung-Tae
    • Proceedings of the KSR Conference
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    • 2004.10a
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    • pp.1765-1769
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    • 2004
  • The number of parameters and the number of samples are the key point of trans-log cost function which studied recently. Most of models show that the number of parameters is more than the number of samples. Therefore, these studies gave unreliability of the estimation results. First, we surveyed theoretical cases and researches for the formulation of cost function of railroad industry. Second, we will suggest trans-log cost function by analyzing cost data of KNR. And, the cumulative data will be needed for the confidence of estimation results.

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A Study on the Monitoring of Reject Rate in High Yield Process

  • Nam, Ho-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.3
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    • pp.773-782
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    • 2007
  • The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

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A Weibull Model Building Technique for Reliability Assessment with Limited failure Data (신뢰도 평가에서 제한된 데이터를 이용한 와이블분포 모형화 기법)

  • Kim, Gwang-Won
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.55 no.3
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    • pp.109-115
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    • 2006
  • The Weibull distribution is a good candidate for accurate probabilistic model with its rich shape-forming ability and relatively simple CDF(cumulative distribution function). If there are sufficient information to get convincible mean and variance for a probabilistic event, reliable parameters of the Weibull distribution can be determined uniquely. However, sufficient information is not given as usual. There needs more deliberate model building method for that case. This Paper presents an effective parameter estimation technique for Weibull distribution with limited failure data.

Prediction of Asphalt Pavement Service Life using Deep Learning (딥러닝을 활용한 일반국도 아스팔트포장의 공용수명 예측)

  • Choi, Seunghyun;Do, Myungsik
    • International Journal of Highway Engineering
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    • v.20 no.2
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    • pp.57-65
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    • 2018
  • PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

A Study on the Estimation of Change Orders Impact for the Public Construction (공공건설공사 설계변경에 따른 손실 추정에 관한 기초연구)

  • Lee, Min-Jae;Park, Bum-Jin;Im, Keon-Soon
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.3D
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    • pp.363-369
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    • 2008
  • Change is inevitable and is a reality of construction projects. Change adjustment includes the cost associated with materials, labor, etc. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative impact of the changes. What is needed is a reliable method to identify and quantify the loss of productivity caused by cumulative impact of change orders. This study survey the change orders data in domestic area for public construction and analyze to quantify change order impact. This study developed concepts of "%CO", "%Delta", "%T" to capture change order effect on project and search the relationships between them. Finally, this study find strong relationship between change order and loss.