• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.397-405
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• 2008

The position accuracy is primarily dependent on the satellite position and signal delay caused by several elements. To know the effect of the delay on the estimated positions, we simulated GPS raw data (RINEX) with GPS errors using Bernese ver5.0. GPS errors used in this paper are Ionospheric delay, Cycle slip, Troposphere, DOP and Random error. If the baseline is short, the position error according to TEC is not large, since the ionospheric delay effect can be removed by ion-free combination. However, if the baseline is long, 3 dimensional position error up to 10cm is occurred. The 3D position error of coordinates with cycle slip is hardly ever changed up to 60% of cycle slip. Because the simulated cycle slips are equally distributed on satellites, the positioning was not seriously affected by the cycle slip. Also, if percentage of cycle slip is 60%, three dimensional error is sharply increased over 1m. The position error is calculated by using the observation data (2 hours) which was selected by DOP less than 3. And its accuracy is more improved about $3{\sim}4cm$.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.111-121
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• 1995

In Extracting the dynamic parameters for estimating the load carrying capacity and integrity of bridges, both the instrumentation and the processing the data plays important role . When the fixed point can not be secured, it is difficult and expensive to measure dynamic displacements. Even if the displacement is obtained through the integration of the acceleration data, the results can be quite different from the real behavior, because the main frequency contents can be leaked during discretized data processing. The instrumentation is used for measurements, and every measurement involves error and uncertainty, such as systematic, conformance, environmental, observational, sampling, and ranmom error. Systematic and conformance error can be remedied through the proper sellection and installation of the instruments, but sampling and random errors could not have been corrected properly and it becomes the limitation for using acceleration data. In this paper, the errors which can be occurred in numerical processing of dynamic data are referred, and the method to sellect proper sampling rate for the structural frequency range are proposed. Using the proposed method, the displacement response of the structures can be economically obtained from the measured acceleration record, and this procedure can be used properly to estimate the integrity of the bridges and infrastructures subjected to dynamic loads.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.447-458
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• 2002

In this paper, we design and implement Wireless Transaction Protocol(WTP) and evaluate it for wireless transaction processing in mobile computing environments. The design and implementation of WTP are based on the coroutine model that might be suitable for light-weight portable devices. We test the compatibility between our product and the other products such as Nokia, Kannel and WinWAP For the evaluation of WTP, we use an Internet simulator that can arbitrary generate random wireless errors based on the Gilbert model. In our experiment, the performance of WTP is measured and compared to those of Transmission Control Protocol(TCP) and TCP for Transactions. The experiment shows that WTP outperforms the other two protocols for wireless transaction processing in terms of throughput and delay. Especially, WTP shows much higher performance In ease of high error rate and high probability of burst errors. This comes from the fact that WTP uses a small number of packets to process a transaction compared to the other two protocols and introduces a fixed time interval for retransmission instead of the exponential backoff algorithm. The experiment also shows that the WTP performance is optimized when the retransmission counter is set to 5 or 6 in case of high burst error rate.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.27-34
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• 2016

Purpose : Retrospective evaluation of setup changes using the corrected position during helical tomotherapy Materials and Methods : Head and neck cancer patients were randomly sampled and summarized into 3 groups: Group 1(32) Brain, Group 2 2(28)Maxillar, Nasal cavity, Group 3 (35) Nasopharynx(NPX), Tongue, Tonsil, and Oropharynx(OPX). In 3 groups, the statistical tests based on repeated measurements among 30 times of the duration of treatment by applying X, Y, Z axis errors, roll, weight changes, and vectors as variables. Results : The statistical test results showed that there was no difference between x-axis (p = 0.458) and y-axis (p=0.986) and in roll (p = 0.037), weight change (p <0.001), and the vector (p <0.001). In addition, the pattern between the three groups based on the fraction revealed no difference in x-axis (p = 0.430) and roll (p = 0.299) but a difference in y-axis (.023), weight change (p = 0.001), and vector (p = 0.028). Conclusion : The results of the retrospective evaluation found the change in the group 3 with respect Y, Z, weight, and vector and a larger random error during the treatment including low neck.

• Survey Research
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• v.9 no.3
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• pp.71-91
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• 2008

Workplace Panel Survey(WPS) is the representative panel survey of workplace in Korea. WPS was newly sampled in 2005 and is to be used for the subsequent biennial survey. The main survey is divided into a questionnaire for human resources(HR) manager, a questionnaire for labor relations manager and a questionnaire for representatives of unions. The population of WPS 2005 included workplaces across the country with 30 or more employees. The WPS 2005 was composed of 1,905 workplaces including 290 workplaces in the public sector. The sample was selected by the stratified random sampling. Weighting process for the survey data was introduced to compensate for differential sampling and non-response rates. Personal interviews were conducted using the Computer Assisted Personal Interviewing(CAPI) system during visits by interviewers, along with survey via mail and e-mail concerning employment and financial issues. The CPAI system introduced for the WPS 2005 can by used for automatical detection for errors and inconsistencies which may occur during the survey process. The CAPI system played an important part in enhancing the reliability of the survey data.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.173-190
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• 2015

The purpose of this study is to improve bankruptcy prediction models by using a novel hybrid under-sampling approach. Most prior studies have tried to enhance the accuracy of bankruptcy prediction models by improving the classification methods involved. In contrast, we focus on appropriate data preprocessing as a means of enhancing accuracy. In particular, we aim to develop an effective sampling approach for bankruptcy prediction, since most prediction models suffer from class imbalance problems. The approach proposed in this study is a hybrid under-sampling method that combines the k-Reverse Nearest Neighbor (k-RNN) and one-class support vector machine (OCSVM) approaches. k-RNN can effectively eliminate outliers, while OCSVM contributes to the selection of informative training samples from majority class data. To validate our proposed approach, we have applied it to data from H Bank's non-external auditing companies in Korea, and compared the performances of the classifiers with the proposed under-sampling and random sampling data. The empirical results show that the proposed under-sampling approach generally improves the accuracy of classifiers, such as logistic regression, discriminant analysis, decision tree, and support vector machines. They also show that the proposed under-sampling approach reduces the risk of false negative errors, which lead to higher misclassification costs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1433-1446
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• 2013

In this study, we proposed a methodology to develop Rating Curves for high water level using rainfall generation by the Monte Carlo Simulation (MCS) technique, optimized rainfall-runoff model, and flood routing model in an urban stream. The developed stage discharge Rating Curve based on observed data was contained flow measurement errors and uncertainties. The standard error ($S_e$) for observations was 0.056, and the random uncertainty ($2S_{mr}$) was analyzed by ${\pm}1.43%$ on average, and up to ${\pm}4.27%$. Moreover, it was found that the Rating Curve extensions by way of logarithmic and Stevens methods were overestimated to compare with the urban basin scale. Finally, we confirmed that the high water level extension by random generation of hydrological data using MCS can be reduced uncertainty of the high water level, and it will consider as a more reliable approach for high water level extension. In the near future, this results can be applied to real-time flood alert system for urban streams through construction of the high water level extension system using MCS procedures.

• Journal of Educational Research in Mathematics
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• v.22 no.4
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• pp.495-515
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• 2012

This study is to diversely analyze teachers' Pedagogical Content Knowledge (PCK) regarding to the area of plane figures and discuss the consideration for the materialization of the effective class in learning the area of plane figures by identifying the improvements based on problems indicated in PCK. The subjects of inquiry are what the problems with teachers' PCK regarding to the area of plane figures are and how they can be improved. In which is the first domain of PCK, teachers need to fully understand the concept of the area and the properties and classification of the area and length, recognized the sequence structure as a subject of guidance and improve the direction which naturally connects the flow of measurement by using random units in guidance of the area. In which is the second domain of PCK, teachers need to establish understanding of the concept for the area and understanding of a formula as a subject matter object and improve the activity, discovery and research oriented class for students as a guidance method by escaping from teacher oriented expository class and calculation oriented repetitive learning. They also need to avoid the biased evaluation of using a formula and evenly evaluate whether students understand the concept of the area as a performance evaluation method. In which is the third domain of PCK, teachers need to fully understand the concept of the area rather than explanation oriented correction and fundamentally teach students about errors by suggesting the activity to explore the properties of the area and length. They also need to plan a method to reflect student's affective aspects besides a compliment and encouragement and apply this method to the class. In which is the fourth domain of PCK, teachers need to increase the use of random units by having an independent consciousness about textbooks and supplementing the activity of textbooks and restructure textbooks by suggesting problematic situations in a real life and teaching the sequence structure. Also, class groups will need to be divided into an entire group, individual group, partner group and normal group.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.222-233
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• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.