• 대한세포병리학회지
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• 제19권2호
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• pp.65-71
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• 2008

In Korea, the quality control(QC) program forcytopathology was introduced in 1995. The program consists of a checklist for the cytolopathology departments, analysis data on all the participating institutions' QC data, including the annual data on cytologic examinations, the distribution of the gynecological cytologic diagnoses, as based on The Bethesda System 2001, and the data on cytologic-histolgical correlation of the gynecological field, and an evaluation for diagnostic accuracy. The diagnostic accuracy program has been performed 3 times per year with using gynecological, body fluid and fine needle aspiration cytologic slides. We report here on the institutional QC data and the evaluation for diagnostic accuracy since 2004, and also on the new strategy for quality control and assurance in the cytologic field. The diagnostic accuracy results of both the participating institutions and the QC committee were as follows; Category 0 and A: about 94%, Category B: 4-5%, Category C: less than 2%. As a whole, the cytologic daignostic accuracy is relatively satisfactory. In 2008, on site evaluation for pathology and cytology laboratories, as based on the "Quality Assurance Program for Pathology Services" is now going on, and a new method using virtual slides or image files for determining the diagnostic accuracy will be performed in November 2008.

• 한국해양공학회지
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• 제35권4호
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• 한국측량학회지
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• 제35권4호
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• pp.261-268
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• 2017

The topographic volume estimation is carried out for the earth work of a construction site and quarry excavation monitoring. The topographic surveying using instruments such as engineering levels, total stations, and GNSS (Global Navigation Satellite Systems) receivers have traditionally been used and the photogrammetric approach using drone systems has recently been introduced. However, these methods cannot be adopted for inaccessible areas where high resolution satellite images can be an alternative. We carried out experiments using Kompsat-3/3A data to estimate topographic volume for a quarry and checked the accuracy. We generated DEMs (Digital Elevation Model) using newly acquired Kompsat-3/3A data and checked the accuracy of the topographic volume estimation by comparing them to a reference DEM generated by timely operating a drone system. The experimental results showed that geometric differences between stereo images significantly lower the quality of the volume estimation. The tested Kompsat-3 data showed one meter level of elevation accuracy with the volume estimation error less than 1% while the tested Kompsat-3A data showed lower results because of the large geometric difference.

• 한국공작기계학회논문집
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• 제13권5호
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• pp.1-6
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• 2004

Measurements of circular motion accuracy of NC lathe have achieved with ball bar systems proposed by Bryan, but the ball bar systems have ifluenced on the measuring data by way of the accuracy of the balls and the contacts of balls and bar seats. Therefore in this study, error data during of circular motion of ATC(Automatic Tool Changer) of NC lathe will be acquired by reading zx plane coordinates using two optical linear scales. Two optical linear scales of measuring unit are fixed on z-x plane of NC lathe, and the moving part is fixed to ATC and then is made to receive data of coordinates of the ATC at constant time intervals using tick pulses comming out from computer. And then, error data files of radial direction of circular motion are calculated with the data read, and the aspect of circular motion are modeled to plots, and are analysed by means of statistical treatments of circularity, means, standard deviations etc.

• 환경영향평가
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• 제18권1호
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

• 항공우주기술
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• 제1권2호
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• pp.141-148
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• 2002

본 연구는 KOMPSAT-1 EOC 영상의 기하정확도를 향상시키고자 수행되었다. 많은 EOC 영상의 지상위치오차를 분석하였고, 그 오차가 시스템내의 시간 부정확성과 자세데이터의 오차로 인한 것임을 알 수 있었다. 그 개선방안으로 Ancillary 데이터의 시간데이터와 자세데이터를 재구성하였고, 이를 적용하여 영상처리한 결과에서 EOC 영상데이터의 기하정확도가 향상되는 것을 확인하였다.

• Journal of Multimedia Information System
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• 제6권2호
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• pp.75-80
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• 2019

In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.341-345
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• 2010

It is known that the accuracy of DEM is related with terrain morphology, sampling density, and interpolation method. However, the theoretical reasons for these correlations have rarely been accounted for so far. This study aimed to verify a theoretical basis that DEM accuracy can be assessed based on approximation theory when we generate a DEM using lots of precise and accurate source data such as digital maps and LIDAR data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.