• The Korean Journal of Nuclear Medicine
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• v.38 no.4
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• pp.282-287
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• 2004

Purpose: Since preoperative staging in esophageal cancer is important in both therapy and prognosis, there had been many efforts to improve its accuracy. Recent studies indicate that whole body FDG-PET has high sensitivity in detection of metastasis in esophageal cancer. Therefore, we added FDG-PET to other conventional methods in staging esophageal cancer to evaluate the usefulness of this method. Materials & Methods: Subjects were 142 esophageal cancer patients (average $62.3{\pm}8.3$ yrs) who received CT and PET just before operation. First, we compared N stage and M stage of the CT or PET with those of the post-operative results. Then we compared the stage according to the EUS (T stage) and CT (N and M stage) or SUS (T stage) and CT & PET (N and M stage) to that according to the post-operative results. Results: Among 142 patients, surgical staging of 69 were N0 and 73 were N1. In M staging, 128 were M0 and 14 were M1. Sensitivity, specificity, and accuracy of N staging were 35.6%, 89.9%, 62.0% with CT and 58.9%, 71.0%, 64.7% with PET, respectively. In M staging, 14.3%, 96.9%, 88.7% with CT and 50.0%, 94.5%, 90.1%, with PET, respectively. The concordances of [EUS+CT] and [EUS+CT+PET] with post-operative results were 41.2% and 54.6%, respectively and there was significant improvement of staging with additional PET scan (p<0.005). Conclusion: The concordance of [EUS+CT+PET] with post-operative result was significantly increased compared to that of [EUS+CT]. Thus, the addition of FDG-PET with other conventional methods may enable more accurate preoperative staging.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.4
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• pp.251-261
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• 2003

To measure the GPS position accuracy and its distribution according to the length of the baseline, 30 minutes to 24 hours observations at the fixed location were conducted with two GPS receivers (Ll, 12 channels) on May 29 to June 2, 2002. The GPS data received at the reference station, the rover station and the ordinary times GPS observation station operated by the National Geography Institute in Korea were processed in kinematic and static post-processing methods with a post -processing software. The results obtained are summarized as follows: 1. The number of the satellite that could be observed continuously more than six hours was 16 and most of these satellites were positioned at east-west direction on May 31, 2002. The number of the satellite observed and the geometric dilution of precision (GDOP) determined by the average of every 10 minute for the day were 8 and 3.89, respectively. 2. Both the average GPS positions before and after post-processing were shifted (standalone: 1.17 m, post -processing: 0.43m) to the south and west. The twice distance root mean square (2drms) measured with standalone was 6.65m. The 2drms could be reduced to 33.8% (standard deviation 0=17.2) and 5.3% (0=2.2) of standalone by the kinematic and the static post-processing methods, respectively. 3. The relationship between the length of the baseline x (km) and the 2drms y (m) obtained by the static post-processing method was y=0.00l6x+0.006 $(R^2=0.87)$. In the case of the positioning with the static post-processing method using the GPS receiver, it was found that a positioning within 20cm 2drms was possible when the length of the baseline was less than 100km and the receiving time of the GPS is more than 30 minutes.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.107-123
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• 2020

With stabilization of the recent multi-GNSS infrastructure, and as multi-GNSS has been proven to be effective in improving the accuracy of the positioning performance in various industrial sectors. In this study, in view that SF(Single frequency) GNSS receivers are widely used due to the low costs, evaluate effectiveness of SF Real Time Point Positioning(SF-RT-PP) based on four multi-GNSS surveying methods with RTCM-SSR correction streams in static and kinematic modes, and also derive response challenges. Results of applying SSR correction streams, CNES presented good results compared to other SSR streams in 2D coordinate. Looking at the results of the SF-RT-PP surveying using SF signals from multi-GNSS, were able to identify the common cause of large deviations in the altitude components, as well as confirm the importance of signal bias correction according to combinations of different types of satellite signals and ionospheric delay compensation algorithm using undifferenced and uncombined observations. In addition, confirmed that the improvement of the infrastructure of Multi-GNSS allows SF-RT-SPP surveying with only one of the four GNSS satellites. In particular, in the case of code-based SF-RT-SPP measurements using SF signals from GPS satellites only, the difference in the application effect between broadcast ephemeris and SSR correction for satellite orbits/clocks was small, but in the case of ionospheric delay compensation, the use of SBAS correction information provided more than twice the accuracy compared to result of the Klobuchar model. With GPS and GLONASS, both the BDS and GALILEO constellations will be fully deployed in the end of 2020, and the greater benefits from the multi-GNSS integration can be expected. Specially, If RT-ionospheric correction services reflecting regional characteristics and SSR correction information reflecting atmospheric characteristics are carried out in real-time, expected that the utilization of SF-RT-PPP survey technology by multi-GNSS and various demands will be created in various industrial sectors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.19-26
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• 2017

Surface air temperature ($T_{air}$) is a key variable for the meteorology and climatology, and is a fundamental factor of the terrestrial ecosystem functions. Satellite remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides an opportunity to monitor the $T_{air}$. However, the several problems such as frequent cloud cover and mountainous region can result in substantial retrieval error and signal loss in MODIS $T_{air}$. In this study, satellite-based $T_{air}$ was estimated under both clear and cloudy sky conditions in Gangwon Province using Aqua MODIS07 temperature profile product (MYD07_L2) and GCOM-W1 Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature ($T_b$) at 37 GHz frequency, and was compared with the measurements from the Automated Mountain Meteorology Stations (AMOS). The application of ambient temperature lapse rate was performed to improve the retrieval accuracy in mountainous region, which showed the improvement of estimation accuracy approximately 4% of RMSE. A simple pixel-wise regression method combining synergetic information from MYD07_L2 $T_{air}$ and AMSR2 $T_b$ was applied to estimate surface $T_{air}$ for all sky conditions. The $T_{air}$ retrievals showed favorable agreement in comparison with AMOS data (r=0.80, RMSE=7.9K), though the underestimation was appeared in winter season. Substantial $T_{air}$ retrievals were estimated 61.4% (n=2,657) for cloudy sky conditions. The results presented in this study indicate that the satellite remote sensing can produce the surface $T_{air}$ at the complex mountainous region for all sky conditions.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.95-103
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• 2010

Discharge data examine the process of hydrologic cycle and used significantly in water resource planning and irrigation and flood control planning. However, it needs lots of time and money to get the discharge data. So discharge rating curve is usually used in converting discharge data. Therefore reliability of discharge rating curve absolutely depends on quality of discharge data. Many engineers who study hydrologic engineering make high quality discharge data to develop reliable discharge rating curve. And they carry out research on standard and method of discharge measurement, and equipment improvement. Now various flow meters are utilized to make discharge data in Korea. However, accuracy of equipment and experimental research data from measurement are not enough. In this paper, constant discharge flowed through standard concrete channel, and the velocity is measured using various flow meters. Also Discharge is calculated by measured data to compare and analyze. The equipment for the experiment is Price AA(USGS Type AA Current meter), flow meter, ADC, C2 small current meter, flow tracker, Electromagnetic current meter. The discharge got form various flow meters which are widely used for discharge measurement. The various depths of water were examined and compared such as 0.30 m, 0.35 m, 0.40 m, 0.45 m, 0.50 m, 0.55 m. The experiment progresses a round-measurement on 6-case. Wading measurement(one point method : the 60 % height in surface of the water) was applied to improve creditability and accuracy among measurement methods. USGS Type AA current Meter, Flow Meter, ADC, C2 Small Current meter got the certificate of quality guaranteed. So the results of experiment were used to compare discharge. The Results showed the difference based on USGS Type AA current Meter at average discharge and velocity. Electromagnetic current meter made differences over $\pm$ 10 % and Flow Meter made differences under $\pm$ 10 %. Also ADC, Flow Meter, C2 Small Current meter made differences under $\pm$ 5 %.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.29-34
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• 2019

Purpose An examination guide is a useful medium to provide the patient with an overview, pre- and post-test preparation, and precautions of nuclear testing. The design and arrangement of existing written texts and announcements were evaluated to elucidate the comprehension of patients undergoing testing. Materials and Methods Informational material describing bone scanning and $^{201}thallium$ myocardium perfusion single-photon emission computed tomography (SPECT), as a secondary examination, which accounts for the largest portion of gamma imaging at Asan Hospital (Seoul, South Korea), was selected as an improvement target in consultation with a national innovation center. Existing informational material was dispensed to patients scheduled for bone scans from November 2016 to February 2017 and the revised material was issued from March 2017 to May 2017. A survey was conducted of 200 patients who underwent $^{201}thallium$ myocardium perfusion SPECT before and after the revisions (n = 100 each time period) to assess the patients' understanding of the informational material. Results When comparing the use of the conventional vs. revised material, the number of patients who received treatment before bone scanning had decreased from 130 to 60, while the number of those who required additional imaging decreased from 53 to 14. Prior to the revision, 43% of patients underwent testing before preparation and 18% underwent additional testing. The decreased need for additional image acquisition after revision of the informational material resulted in a decrease in acquisition time of about 2 min, from 16.5 to 14.2 min. In the case of $^{201}thallium$ myocardium perfusion SPECT, patient comprehension of all five items surveyed had increased, while the number of patients who had repeatedly asked about various facets of the procedure pre- and post-testing had decreased from 36% to 16% and 31% to 14%, respectively. Conclusion Lower patient comprehension is accompanied by a decrease in image quality due to non-compliance during pre-testing and may lead to repetitive questions from the patient, which may also negatively affect the fatigue and work efficiency of the examiner. Improved readability and visibility of informational material through visualization was correlated with greater patient comprehension as well as improved image quality and acquisition time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.