• Korean Journal of Remote Sensing
• /
• v.33 no.1
• /
• pp.1-13
• /
• 2017

This study investigates the geometric and spatial image quality analysis of KOMPSAT-3A stereo pair. KOMPSAT-3A is, the latest satellite of KOMPSAT family, a Korean earth observation satellite operating in optical bands. A KOMPSAT-3A stereo pair was taken on 23 November, 2015 with 0.55 m ground sampling distance over Terrassa area of Spain. The convergence angle of KOMPSAT-3A stereo pair was estimated as $58.68^{\circ}$. The investigation was assessed through the evaluation of the geopositioning analysis, image quality estimation and the accuracy of automatic Digital Surface Model (DSM) generation and compared with those of KOMPSAT-3 stereo pair with the convergence angle of $44.80^{\circ}$ over the same area. First, geopositioning accuracy was tested with initial rational polynomial coefficients (RPCs) and after compensating the biases of the initial RPCs by manually collected ground control points. Then, regarding image quality, relative edge response was estimated for manually selected points visible from two stereo pairs. Both of the initial and bias-compensated positioning accuracy and the quality assessment result expressed that KOMPSAT-3A images showed higher performance than those of KOMPSAT-3 images. Finally, the accuracy of DSMs generated from KOMPSAT-3A and KOMPSAT-3 stereo pairs were examined with respect to the reference LiDAR-derived DSM. The various DSMs were generated over the whole coverage of individual stereo pairs with different grid spacing and over three types of terrain; flat, mountainous and urban area. Root mean square errors of DSM from KOMPSAT-3A pair were larger than those for KOMPSAT-3. This seems due to larger convergence angle of the KOMPSAT-3A stereo pair.

• The Korean Journal of Community Living Science
• /
• v.19 no.4
• /
• pp.537-546
• /
• 2008

The purposes of the study was to identify customers' satisfaction factors regarding foods and services in food courts in big department stores and discount stores in Seoul and Cheonan. A survey of 235 customers was conducted regarding customer satisfaction levels and factors of food and services. Customers' perceived level of attributes were identified into eight underlying dimensions by factor analysis as follows: factor 1 was "cleanliness": factor 2 "service quality": factor 3 "accuracy": factor 4 "atomosphere": factor 5 "food quality": factor 6 "menu information": factor 7 "price" and the eighth factor was "food result". Regression analysis indicated that "cleanliness" was found to be the most important factor contributing to customers' overall satisfaction. There were significant differences in customers' perceived satisfaction level of "food quality"(p<0.01), "accuracy", and "price" factors(p<0.05) between department stores and discount store. The customers' perceived satisfaction levels of "accuracy", "food quality" and "price" factor at a large store food court are higher than those of department store food court. Comparing location of food court, there were significant differences in customers' perceived satisfaction level of "accuracy" and "price" factors between in Seoul and Cheonan(p<0.001). The customers' perceived satisfaction levels of "accuracy" and "price" at the discount store in Seoul are lower than those of food court at Cheonan. It is suggested that the management should pay attention to the sanitation of their dinning halls, kitchens, hygienic dishes, hygienic water fountain, employee hygiene, and a proper place to put used dishes to increase the customers' satisfaction.

• Journal of the Korea Society for Simulation
• /
• v.20 no.3
• /
• pp.59-67
• /
• 2011

This paper proposes a model to analyze the fault factors of air-to-ground weapon delivery utilizing software fault localization methods. In the previous study, to figure out the factors to affect the accuracy of air-to-ground weapon delivery, the FBEL (Factor-based Error Localization) method had been proposed and the fault factors were analyzed based on the method. But in the study, the correlation between weapon delivery accuracy and the fault factors could not be revealed because the firing accuracy among several factors was fixed. In this paper we propose a more precise fault analysis model driven through a study of the correlation among the fault factors of weapon delivery, and a method to estimate the possibility of faults with the limited number of test cases utilizing the model. The effectiveness of proposed method is verified through the simulation utilizing real delivery data. and weapons delivery testing in the evaluation of which element affecting the accuracy of analysis that was available to be used successfully.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.8
• /
• pp.653-660
• /
• 2017

In this paper, the author analyzes direction finding accuracy based on phase comparisons to estimate elevation and azimuth angles of arrival signals. This paper considers the uniform array configurations using four and three elements. In that direction finding structures, I present the analytic expressions for estimated elevation and azimuth angles and then analyze the direction finding errors. And one presents the design guideline of direction finding system in comparison with aspects of accuracy, structure, the number of channels in that structures. The analysis result is similar with simulation one and has difference within $1.2^{\circ}RMS$. From the proposed analysis results, one knows that when SNR is 20 dB and the baseline is half of wavelength, the estimated elevation accuracy of the uniform array using four elements is 1.15 times better than the one of the uniform array using three elements and the estimated azimuth accuracy is same each other. In addition, one knows coning error is eliminated in 2-D direction finding structure.

• Journal of Ocean Engineering and Technology
• /
• v.35 no.4
• /
• pp.273-286
• /
• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.2
• /
• pp.109-118
• /
• 2022

GPR (Ground Penetrating Radar), developed as a technology for geotechnical investigations such as sinkhole exploration, was used limitedly as a method to resolve undetectable lines in underground facility exploration. To improve the accuracy of underground facility data, the government made it possible to explore underground facilities using a non-metallic pipeline probe from July 2022. However, GPR has a problem in that the exploration rate is lowered in the soil with high moisture content, such as soft soil, such as clay layer, and there is a lot of variation in long-term accuracy. In this study, as a way to improve the accuracy of exploration considering the characteristics of GPR and the environment of underground facilities, we propose a GPR exploration method for underground facilities using permittivity constant correction and pattern analysis of GPR image data. Through this study, the accuracy of underground facility exploration and high reproducibility were derived as a result of field verification applying GPR frequency band and heterogeneous GPR.

• Journal of Environmental Health Sciences
• /
• v.48 no.4
• /
• pp.216-226
• /
• 2022

Background: The amount of smoking in adolescence increases with a younger age of smoking initiation and affects physical health. To establish and evaluate smoking-related policies, it is important to determine actual smoking status. Validation of self-reported questionnaires can identify the accuracy of the questionnaire data reflecting smoking status. Objectives: The purpose of this study was to evaluate the validity of self-reported smoking status and identify factors affecting the accuracy of self-reported smoking in South Korean adolescents. Methods: This study investigated the consistency between cotinine concentrations and self-reported questionnaire data through the analysis of urine samples collected from 922 adolescents aged 13~18 among the participants of Cycle 3 of the Korean National Environmental Health Survey. Smoking status was classified using the cotinine cut-off point of 39.85 ㎍/L in adolescents, and factors affecting the accuracy were analyzed through multiple logistic regression analysis. Results: The smoking rates according to the self-reported questionnaire and cut-off point-based cotinine concentrations among adolescents were 3.1% and 5.1%, respectively. The results found 97.1% consistency between self-reported smokers and smokers according to cotinine concentration. Factors affecting the discrepancy showed a significant relationship, including gender, secondhand smoke, and use of e-cigarettes. Conclusions: The results can be used as basic data to establish a smoking policy for adolescents through continuous monitoring and improvement of questionnaire items of factors affecting the discrepancy.

• Journal of Internet Computing and Services
• /
• v.23 no.4
• /
• pp.45-55
• /
• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.159-165
• /
• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.4
• /
• pp.26-32
• /
• 2020

There are many airline customer evaluation data, but they are insufficient in terms of predicting customer satisfaction in practice. In particular, they are generally insufficient in case of verification of data value and development of a customer satisfaction prediction model based on customer evaluation data. In this paper, airline customer satisfaction analysis is conducted through an experiment of correlation analysis between customer evaluation data provided by Google's Kaggle. The difference in accuracy varied according to the three types, which are the overall variables, the top 4 and top 8 variables with the highest correlation. To build an airline customer satisfaction prediction model, they are applied to three classification algorithms of Random Forest, SVM, DNN and conduct a classification experiment. They are divided into training data and verification data by 7:3. As a result, the DNN model showed the lowest accuracy at 86.4%, while the SVM model at 89% and the Random Forest model at 95.7% showed the highest accuracy and performance.