• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.400-407
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• 1995

The straightness property is one of fundamental geometric tolerances to be strictly controlled for guideways of machine tools and measuring machines. The staightness measurement for long guideways was usually difficult to perform, and it needed additional equipments or special treatment with limited application. In this paper, a new approach is proposed using the profile matching technique for the long guideways, which can be applicable to most of straghtness measurements. An edge of relativelly sthort length is located along a divided section of a long guideway, and the local straightness measurement is performed. The edge is then moved to the next section with several positions overlap. After thelocal straightness profile is measured for every section along the long guideway with overlap, the global straightness profile is constructed using the profile matching technique based on theleast squares method. The proposed techinique is numerically tested for two cases of known global straightness profile arc profile and irregular profile and those profiles with and without random error intervention, respectively. When norandom errors are involved, the constructed golval profile is identical to the original profile. When the random errors are involved, the effect of the number of overlap points are investigated, and it is also found that the difference between the difference between the constructed and original profiles is very close to the limit of random uncertainty with juist few overlap points. The developed technique has been practically applied to a vertical milling machine of moving table type, and showed good performance. Thus the accuracy and efficiency of the proposed method are demonstrated, and shows great potential for variety of application for most of straightness measuirement cases using straight edges, laser optics, and angular measurement equipments.

• Progress in Medical Physics
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• v.22 no.2
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• pp.72-78
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• 2011

The aim of this study is to evaluate the patient's setup errors in TomoTherapy (Hi-Art II, TomoTherapy, USA) Bodyfix system (Medical Intelligence, Ele-kta, Schwabmuchen, Germany) pressure in the vacuum compression, depending on and were evaluated. Bodyfix immobilization system and vacuum pressure was compression applied to the patients who received Tomotherapy thoracic and abdominal area, 21 patients were selected and TomoTehpay treatment total 477 of MVCT images were obtained. The translational (medial-lateral: ML, anterior-posterior: AP, superior-inferior: SI directions) and rolling were recorded and analyzed statistically. Using Pearson's product-moment coefficient and One-way ANOVA, the degree of correlation depending on the different vacuum pressure levels were statistically analyzed for setup errors from five groups (p<0.05). The largest average and standard deviation of systematic errors were 6.00, 5.95 mm in the AP and SI directions, respectively. The largest average of random errors were 4.72 mm in the SI directions. The correlation coefficients were 0.485, 0.244, and 0.637 for the ML-Roll, AP-Vector, and SI-Vector, respectively. SI-Vector direction showed the best relationship. In the results of the different degree of vacuum pressure in five groups (Pressure range: 30~70 mbar), the setup errors between the ML, SI in both directions and Roll p=0.00 (p<0.05) were shown significant differences. The average errors of SI direction in the vacuum pressure of 40 mbar and 70 mbar group were 4.78 mm and -0.74 mm, respectively. In this study, the correlation between the vacuum pressure and the setup-errors were statistically analyzed. The fact that setup-errors in SI direction is dependent in vacuum pressure considerly setup-errors and movement of interal organs was identified. Finally, setup-errors, and it, based on the movement of internal organs in Bodyfix system we should apply more than 50 mbar vacuum pressure. Based on the results of this study, it is suggested that accuracy of the vacuum pressure and the quantitative analysis of movement of internal organs and the tumor should be studied.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.133-139
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• 2011

Estimation of biomass carbon stock is an important research for estimation of public benefit of forest. Previous studies about biomass carbon stock estimation have limitations, which come from the used deterministic models. The most serious problem of deterministic models is that deterministic models do not provide any explanation about the relevant effects of errors. In this study, the effects of location errors were analyzed in order to estimation of biomass carbon stock of Danyang area using Monte Carlo simulation method. More specifically, the k-Nearest Neighbor(kNN) algorithm was used for basic estimation. In this procedure, random and systematic errors were added on the location of Sample plot, and effects on estimation error were analyzed by checking the changes of RMSE. As a result of random error simulation, mean RMSE of estimation was increased from 24.8 tonC/ha to 26 tonC/ha when 0.5~1 pixel location errors were added. However, mean RMSE was converged after the location errors were added 0.8 pixel, because of characteristic of study site. In case of the systematic error simulation, any significant trends of RMSE were not detected in the test data.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.563-574
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• 2009

The merits of random substitutions are various applicability and security guarantee on the view point of privacy breach. However there is no research to improve the accuracy of random substitutions. In this paper we propose an algorithm for improving the accuracy of random substitutions by an advanced theoretical analysis about the standard errors. We examine that random substitutions have an unpractical accuracy level and our improved algorithm meets the theoretical results by some experiments for data sets having uniform and normal distributions. By our proposed algorithm, it is possible to upgrade the accuracy level under the same security level as the original method. The additional cost of computation for our algorithm is still acceptable and practical.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1211-1215
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• 2003

Accurate mathematical models of spacecraft components are an essential of spacecraft attitude control system design, analysis and simulation. Gyro is one of the most important spacecraft components used for attitude propagation and control. Gyro errors may seriously degrade the accuracy of the calculated spacecraft angular rate and of attitude estimates due to inherent drift and bias errors. In order to validate this model, nominal case simulation has been performed and compared for the low range mode and high range mode, respectively. In this paper, a mathematical model of gyro containing the relationships for predicting spacecraft angular rate and disturbances is proposed.

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.453-458
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• 2006

It is well known fact for the iid data that the limiting standard errors of sample autocorrelations are all unity for all time lags and they are asymptotically independent for different lags (Brockwell and Davis, 1991). It is also usual practice in time series modeling that this fact continues to be valid for white noise series which is a sequence of uncorrelated random variables. This paper contradicts this usual practice for white noise. We consider a sequence of martingale differences which belongs to white noise time series and derive exact joint asymptotic distributions of sample autocorrelations. Some implications of the result are illustrated for conditionally heteroscedastic time series.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.237-244
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• 2001

On this study, a 50-years-old earth dam was measured by the static method of GPS for deformation monitoring. The reference network was measured by the vector between points in twice times and the monitored points were observed in four times at test field, i.e. an embankment which was restored by mortar, In addition, gross errors in the measurement were estimated and eliminated by data snooping method and random errors were adjusted by least square method. Finally, the amount of displacement was estimated from variance-covariance matrix. Also, precision of points were showed by the confidence ellipse(95%), and the amount of displacement was figured.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.17-26
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• 1993

Process fault diagnosis is a complicated matter because quality control problems can result from a variety of causes. These causes include problems with electrical components, mechanical components, human errors, job justification errors, and air conditioning influences. In order to make the system run smoothly with minimum delay, it is necessary to suggest heuristic remedies for the detected faults. Hence, this paper describes a heuristic methodology of fault diagnosis that is performed using statistical patterns generated by quality characteristics The proposed methodology is described briefly as follows: If a sample pattern generated by random variables is similar to the number of prototype patterns, the sample pattern may be matched by any prototype pattern among them to be resembled. This concept is based on the similarity between a sample pattern and the matched prototype pattern. The similarity is calculated as the weighted average of squared deviation, which is expressed as the difference between the relative values of standard normal distribution to be transformed by the observed values of quality characteristics in a sample pattern and the critical values of the corresponding ones in a matched prototype pattern.