• Title/Summary/Keyword: Error distribution

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p-Adaptive Mesh Refinement of Plate Bending Problem by Modified SPR Technique (수정 SPR 기법에 의한 휨을 받는 평판문제의 적응적 p-체눈 세분화)

  • Jo, Jun-Hyung;Lee, Hee-Jung;Woo, Kwang-Sung
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2007.04a
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    • pp.481-486
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    • 2007
  • The Zienkiewicz-Zhu(Z/Z) error estimate is slightly modified for the hierarchical p-refinement, and is then applied to L-shaped plates subjected to bending to demonstrate its effectiveness. An adaptive procedure in finite element analysis is presented by p-refinement of meshes in conjunction with a posteriori error estimator that is based on the superconvergent patch recovery(SPR) technique. The modified Z/Z error estimate p-refinement is different from the conventional approach because the high order shape functions based on integrals of Legendre polynomials are used to interpolate displacements within an element, on the other hand, the same order of basis function based on Pascal's triangle tree is also used to interpolate recovered stresses. The least-square method is used to fit a polynomial to the stresses computed at the sampling points. The strategy of finding a nearly optimal distribution of polynomial degrees on a fixed finite element mesh is discussed such that a particular element has to be refined automatically to obtain an acceptable level of accuracy by increasing p-levels non-uniformly or selectively. It is noted that the error decreases rapidly with an increase in the number of degrees of freedom and the sequences of p-distributions obtained by the proposed error indicator closely follow the optimal trajectory.

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A Study on the Improvement of Transmission Error and Tooth Load Distribution using Micro-geometry of Compound Planetary Gear Reducer for Tractor Final Driving Shaft (트랙터 최종구동축용 복합유성기어 방식 감속기의 Micro-geometry를 이용한 전달 오차 및 치면 하중 분포 개선에 관한 연구)

  • Lee, Nam Gyu;Kim, Yong Joo;Kim, Wan Soo;Kim, Yeon Soo;Kim, Taek Jin;Baek, Seung Min;Choi, Yong;Kim, Young Keun;Choi, Il Su
    • Journal of Drive and Control
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    • v.17 no.1
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    • pp.1-12
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    • 2020
  • This study was to develop a simulation model of a compound planetary gear reducer for the final driving shaft using a gear analysis software (KISSsoft, Version 2017, KISSsoft AG, Switzerland). The aim of this study is to analyze transmission error and the tooth load distribution through micro-geometry using the simulation model. The tip and root relief were modified with Micro-geometry in the profile direction, and crowning was modified with Micro-geometry in the lead direction. The transmission error was analyzed using the PPTE (Peak to Peak Transmission Error) value, and the tooth load distribution was analyzed for the concentrated stress on the tooth surface. As a result of modifying tip and relief in the profile direction, the transmission error was reduced up to 40.7%. In the case of modifying crowning in the lead direction, the tooth load was more evenly distributed than before and decreased the stress on the tooth surface. After modifying the profile direction for the 1st and 2nd planetary gear train, the bending and contact safety factors were increased by 31.7% and 17%, and 18.3% and 12.5% respectively. Moreover, the bending and safety factors after modifying lead direction were increased by 59.5% and 32.7%, respectively for the 1st planetary gear train, and 59.6% and 43.6%, respectively for the 2nd planetary gear train. In future studies, the optimal design of a compound planetary gear reducer for the final driving shaft is needed considering both the transmission error and tooth load distribution.

A Study on the Inference Model of In-use Vehicles Emission Distribution according to the Vehicle Mileage (주행거리별 운행차 배출가스 분포 추정 모델에 관한 연구)

  • 김현우
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.4
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    • pp.85-92
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    • 2002
  • To investigate the safety of the in-use vehicles emission against the tail-pipe emission regulation, in-use vehicles emission trend according to vehicle mileage should be known. But it is impossible to collect all vehicles emission data In order to know that. Therefore, it is necessary to establish a statistically meaningful inference method that can be used generally to estimate in-use vehicles emissions distribution according to the vehicle mileage with relatively less in-use vehicles emission data. To do this, a linear regression model that solved the problems of data normality and common variance of error was studied. As a way that can secure the data normality, In(emission) instead of emission itself was used as a sampled data. And a reciprocal of mileage was suggested as a factor to secure common variance of error. As an example, 36 data of FTP-75 test were handled in this study. As a result, using average value and standard deviation at each mileage which were inferred from a linear regression model, probability density distribution and cumulative distribution of emissions according to the vehicle mileage were obtained and it was possible to predict the deterioration factor through full useful life mileage and also possible to decide whether those in-use vehicles will meet the tail-pipe emission regulations or not.

On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation

  • Fonseca Junior, Joao Gari da Silva;Oozeki, Takashi;Ohtake, Hideaki;Takashima, Takumi;Kazuhiko, Ogimoto
    • Journal of Electrical Engineering and Technology
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    • v.10 no.3
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    • pp.1342-1348
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    • 2015
  • The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

Ichthyoplankton Detection Proportion and Margin of Error for the Scomber japonicus in Korean Coastal Seas

  • Kim, Sung;Cho, Hong-Yeon
    • Ocean and Polar Research
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    • v.39 no.2
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    • pp.73-84
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    • 2017
  • The probability distribution of ichthyoplankton is important for enhancing the precision of sampling while reducing unnecessary surveys. To estimate the ichthyoplankton detection proportion (IDP) and its margin of error (ME), the monitoring information of the chub mackerel's (Scomber japonicus) ichthyoplankton presence-absence sampling data has been were collected over approximately 30 years (from 1982 to 2011) in the Korean coastal seas. Based on the computed spatial distributions of the mackerel's IDP and ME, the confidence interval (CI) range, defined as 2 ME, decreases from approximately 80% to 40% as the sample size n increases from 4 to 24 and the ME is approximately 40% in the typical (seasonal survey) case n = 4 per year. The IDP and ME off Jeju Island are relatively high at the 0.5-degree smoothing level. After increasing the spatial smoothing level to 1.0-degree, the ME decreased, and the spatial distribution pattern also changed due to the over-smoothing effects. In this study, the 0.5-degree smoothing is more suitable for the distribution pattern than the 1.0-degree smoothing level. The area of the high IDP and the low ME on the mackerel's ichthyoplankton was similar to the estimated spawning ground in the Korean peninsula. This information could contribute to enhancing for the spawning ecology surveys.

A Causality Analysis of the Hairtail Price by Distribution Channel Using a Vector Autoregressive Model (VAR 모형을 이용한 유통단계별 갈치가격의 인과성 분석)

  • Kim, Cheol-Hyun;Nam, Jong-Oh
    • The Journal of Fisheries Business Administration
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    • v.46 no.1
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    • pp.93-107
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    • 2015
  • This study aims to analyze causalities among Hairtail prices by distribution channel using a vector autoregressive model. This study applies unit-root test for stability of data, uses Granger causality test to know interaction among Hairtail Prices by distribution channel, and employes the vector autoregressive model to estimate statistical impacts among t-2 period variables used in model. Analyzing results of this study are as follows. First, ADF, PP, and KPSS tests show that the change rate of Hairtail price by distribution channel differentiated by logarithm is stable. Second, a Granger causality test presents that the producer price of Hairtail leads the wholesale price and then the wholesale price leads the consumer price. Third, the vector autoregressive model suggests that the change rate of Hairtail producer price of t-2 period variables statistically, significantly impacts change rates of own, wholesale, and consumer prices at current period. Fourth, the impulse response analysis indicates that impulse responses of the structural shocks with a respectively distribution channel of the Hairtail prices are relatively more powerful in own distribution channel than in other distribution channels. Fifth, a forecast error variance decomposition of the Hairtail prices points out that the own price has relatively more powerful influence than other prices.

Vessel traffic geometric probability approaches with AIS data in active shipping lane for subsea pipeline quantitative risk assessment against third-party impact

  • Tanujaya, Vincent Alvin;Tawekal, Ricky Lukman;Ilman, Eko Charnius
    • Ocean Systems Engineering
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    • v.12 no.3
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    • pp.267-284
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    • 2022
  • A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.

Bayes Estimation of a Reliability Function for Rayleigh Model

  • Kim, Yeung-Hoon;Sohn, Joong-Kweon
    • Journal of the Korean Statistical Society
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    • v.23 no.2
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    • pp.445-461
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    • 1994
  • This paper deals with the problem of obtaining some Bayes estimators and Bayesian credible regions of a reliability function for the Rayleigh distribution. Using several priors for a reliability function some Bayes estimators and Bayes credible sets are proposed and studied under squared error loss and Harris loss. Also the performances and behaviors of the proposed Bayes estimators are examined via Monte Carlo simulations and some numericla examples are given.

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Estimation of the Lorenz Curve of the Pareto Distribution

  • Kang, Suk-Bok;Cho, Young-Suk
    • Communications for Statistical Applications and Methods
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    • v.6 no.1
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    • pp.285-292
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    • 1999
  • In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

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