• 한국항해학회지
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• 제22권1호
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• pp.1-13
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• 1998

Radiobeacons that carry corrections for global satellite navigation systems are currently being planned or installed in many countries. In early 1996, it was begun to send DGPS correction message from a marine radiobeacon station located in Changgi Got Lighthouse. It was the first test broadcast of DGPS correction data based on medium frequency of marine radiobeacon where transmission power and rate are 300W and 100bps respectively in Korea. However, there has not been any scientific study on the characteristic of the accuracy of a marine radiobeacon/DGPS. Accordingly, this paper investigates the accuracy of the system, which is currently operating in 310kHz. To do this , the real time differential correction in RTCM data was collected in an implemented system. And then the accuracy was analyzed related to the coverge of the radiobeacon/DGPS. As a result, it is verified that the differential positioning accuracy using the marine radiobeacon is sufficient to ensure the safety of marine activities around the coast of Korea.

• Korean Journal of Geomatics
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• 제2권2호
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• pp.99-105
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• 2002

The accuracy of the Digital Photologging System, designed for the construction of the road Facility Database, highly depends on the positions and attitudes of the cameras from GPS/INS integration. In this paper, the development of a loosely coupled GPS/INS is presented. The performance of the system is verified through a simulation as well as a real test data processing. Since the IMU used in this study shows large systematic errors, the possible accuracy of the positions and attitudes of this low-performance IMU when combined with precise GPS positions are assigned. Currently, the integrated system shows the positional accuracy better than 5cm in real data processing. Although the accuracy of attitude based on real test could not be assigned at this time, it is expected that better than 0.5 degrees and 1.8 degrees for horizontal and down component are achievable according to the simulation result.

• Journal of Advanced Marine Engineering and Technology
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• 제20권4호
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• pp.90-100
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• 1996

To maintain high spacial accuracy and rapid CPU time in interpolating data from grid to random position or inversely in PIV, proposed many technuques are compared and discussed mainly in terms of interpolating error and computing time. And artificial PIV atmosphere data is furnished by CFD result. First, for interpolation from grid to random position, multiquadric method gives the highest accuracy with the longest CPU time and Taylor series expansion methods give reasonable accuracy with less calculating load. Secondly, the sub-pixel resolution analysis in estimating the coordinates of the maximum correlation coefficients essential in the grey level correlation PIV reveal that 8-neighbours 2nd-order least square interpolation gives utmost accuracy in terms of the real flow conditions.

• Journal of Advanced Marine Engineering and Technology
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• 제20권4호
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• pp.412-412
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• 1996

To maintain high spacial accuracy and rapid CPU time in interpolating data from grid to random position or inversely in PIV, proposed many technuques are compared and discussed mainly in terms of interpolating error and computing time. And artificial PIV atmosphere data is furnished by CFD result. First, for interpolation from grid to random position, multiquadric method gives the highest accuracy with the longest CPU time and Taylor series expansion methods give reasonable accuracy with less calculating load. Secondly, the sub-pixel resolution analysis in estimating the coordinates of the maximum correlation coefficients essential in the grey level correlation PIV reveal that 8-neighbours 2nd-order least square interpolation gives utmost accuracy in terms of the real flow conditions.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.173-178
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• 2021

In this paper, we find a suitable methodology for Korean Sentiment Analysis through a comparative experiment in which methods of embedding and natural network models are learned at the highest accuracy and fastest speed. The embedding method compares word embeddeding and Word2Vec. The model compares and experiments representative neural network models CNN, RNN, LSTM, GRU, Bi-LSTM and Bi-GRU with IKEA review data. Experiments show that Word2Vec and BiGRU had the highest accuracy and second fastest speed with 94.23% accuracy and 42.30 seconds speed. Word2Vec and GRU were found to have the third highest accuracy and fastest speed with 92.53% accuracy and 26.75 seconds speed.

• 대한상한금궤의학회지
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• 제14권1호
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.388-393
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• 2002

Remote sensing data is collected and analyzed to enhance understanding of the terrestrial surface. Since Landsat satellite was launched in 1972, many researches using multispectral data has been achieved. Recently, with the availability of airborne and satellite hyperspectral data, the study on hyperspectral data are being increased. It is known that as the number of spectral bands of high-spectral resolution data increases, the ability to detect more detailed cases should also increase, and the classification accuracy should increase as well. In this paper, we classified the hyperspectral and multispectral data and tested the classification accuracy. The MASTER(MODIS/ASTER Airborne Simulator, 50channels, 0.4~13$\mu$m) and Landsat TM(7channels) imagery including Yeong-Gwang area were used and we adjusted the classification items in several cases and tested their classification accuracy through statistical comparison. As a result of this study, it is shown that hyperspectral data offer more information than multispectral data.

• 한국환경과학회지
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• 제26권12호
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• pp.1307-1319
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• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• 한국측량학회지
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• 제25권3호
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• pp.223-230
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• 2007

도시의 집중화로 지하시설물이 폭발적으로 증가하였으며, 그에 따른 지하시설물도의 중요성이 부각되고 있다. 하지만, 부정확한 지하시설물도와 지하시설물에 대한 관리부재로 인하여 크고 작은 사고가 계속하여 발생하고 있는 실정이다. 또한 지하시설물도는 자료의 최신성과 정확성이 확보되어야 하나, 현재의 지하시설물도는 각 기관별 작성 및 갱신체계를 가지고 있어, 자료의 최신성과 정확성을 검증하기 힘든 상황이다. 따라서, 본 논문에서는 서울특별시에 소재한 6대 유관기관의 지하시설물도 위치정확도를 분석하기 위해 3년간 지하시설물에 대한 현장 조사/탐사 작업과 기준점에 기반한 위치측량을 수행하였다. 이를 바탕으로 연차별, 기관별 지하시설물도의 위치정확도를 비교분석하였으며, 그 결과 지하시설물도의 평균 73cm(2004), 78cm(2005) 그리고 75cm(2006)의 위치정확도 결과값을 획득하였다.