• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.307-316
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• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• Journal of Distribution Science
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• v.4 no.2
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• pp.5-20
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• 2006

Retail is called location business because it is one of the most important factors to estimate management of stores for retailers who are going to sell products directly to customers. Retailers' management achievements are shown in sale in general. Therefore, retailers tend to focus on ways to increase the numbers of customers in order to raise sales. First of all, in this research, I am going to examine the most fundamental models such as Reilly's retail gravitation, converse model, huff probability model and multiful losit model in selecting stores. Secondly, I am going to provide the process and analyzing ways to predict estimated sales amount with the previous theory model. Also I am going to predict estimated sales amount of the department store L which is located in D metorpolitan city. Lastly, I am going to argue about the problem of this research and the next research subject. Our main goal is to provide ways to complement and inspect sales estimation models, which can be used in fields after taking characters of high class structure of Korea into consideration on the base of previous researches. According to the result of the research, my conclusion is that if the process of analysis and changing factors are complemented, revise model, which can reflect reality of Korea, will be provided. Therefore, in the future study, we have to build up theory models to suit for our retail market through critic reviews about the existing high class structure of Korea.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.95-106
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• 2005

Road accidents are considered as the result of a complex interplay between road, vehicle, environments, and human factors. Little study, however, has been carried out on the attributes of human factor compared to the road geometric conditions and traffic conditions. The previous researches focused on mainly both traffic and geometric conditions on specific location. Therefore, it's hard to explain phenomenon of the high traffic accident rates where road and traffic conditions are good. Because of these reasons, accident analysis has contributed on geometric improvement and has not contributed on traffic management such as selection of attention section, driver napping alert, etc. The freeway incident management is also associated with reliable prediction of incident occurrences on freeway sections. This paper presents a method for estimating the effect of trip length on freeway accident rate. A PAR (Potential Accident Ratio), the new concept of accident analysis, considering TLFDs (Trip Length Frequency Distributions) is suggested in this paper. This approach can help to strengthen freeway management and to reduce the likelihood of accidents.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.119-130
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• 1999

At present, many ground stations all over the world are using NORAD orbit element data in order to track and communicate with Earth orbiting satellites. The North American Aerospace Defense Command (NORAD) observes thousands of Earth orbiting objects on daily basis and provides their orbital information via internet. The orbital data provided by NORAD, which is also called two line element (TLE) sets, allows ground stations to predict the time-varying positions of satellites accurately enough to communicate with the satellites. In order to complete the mission of a high resolution remote sensing satellite which requires very high positional determination and control accuracy, however, a mission control and tracking ground station is dedicated for the observation and positional determination of the satellite rather than using NORAD orbital sets. In the case of KITSAT-3, NORAD orbital elements are currently used for image acquisition planning and for the processing of acquired images due to the absence of a dedicated KITSAT-3 tracking ground system. In this paper, we tested and analyzed the accuracy of NORAD orbital elements and the appropriate prediction model to determine how accurately a satellite acquisites an image of the location of interest and how accurately a ground processing system can generate the catalog of the images.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.382-393
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• 2010

Thermal mixing by steam jets in a pool is dominantly influenced by a turbulent water jet generated by the condensing steam jets, and the proper prediction of this turbulent jet behavior is critical for the pool mixing analysis. A turbulent jet flow induced by a steam jet discharged through a vertical upward single hole into a subcooled water pool was subjected to computational fluid dynamics (CFD) analysis. Based on the small-scale test data derived under a horizontal steam discharging condition, this analysis was performed to validate a CFD method of analysis previously developed for condensing jet-induced pool mixing phenomena. In previous validation work, the CFD results and the test data for a limited range of radial and axial directions were compared in terms of profiles of the turbulent jet velocity and temperature. Furthermore, the behavior of the turbulent jet induced by the steam jet through a horizontal single hole in a subcooled water pool failed to show the exact axisymmetric flow pattern with regards to an overall pool mixing, whereas the CFD analysis was done with an axisymmetric grid model. Therefore, another new small-scale test was conducted under a vertical upward steam discharging condition. The purpose of this test was to generate the velocity and temperature profiles of the turbulent jet by expanding the measurement ranges from the jet center to a location at about 5% of $U_m$ and 10 cm to 30 cm from the exit of the discharge nozzle. The results of the new CFD analysis show that the recommended CFD model of the high turbulent intensity of 40% for the turbulent jet and the fine mesh grid model can accurately predict the test results within an error rate of about 10%. In this work, the turbulent jet model, which is used to simply predict the temperature and velocity profiles along the axial and radial directions by means of the empirical correlations and Tollmien's theory was improved on the basis of the new test data. The results validate the CFD model of analysis. Furthermore, the turbulent jet model developed in this study can be used to analyze pool thermal mixing when an ellipsoidal steam jet is discharged under a high steam mass flux in a subcooled water pool.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.286-297
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• 2018

In the present study, wave measurements at KOGA-W01 were analyzed and then the numerical wind waves simulations have been conducted to investigate the characteristics of wind waves in the Yellow sea. According to the present analysis, even though the location of the wave stations are close to the coastal region, the deep water waves are prevailed due to the short fetch length. Chun and Ahn's (2017a, b) numerical model has been extended to the Yellow Sea in this study. The effects of tide and tidal currents should be included in the model to accommodate the distinctive effect of large tidal range and tidal current in the Yellow Sea. The wave hindcasting results were compared with the wave measurements collected KOGA-W01 and Kyeockpo. The comparison shows the reasonable agreements between wave hindcastings and measured data, however the model significantly underestimate the wave period of swell waves from the south due to the narrow computational domain. Despite the poorly prediction in the significant wave period of swell waves which usually have small wave heights, the estimation of the extreme wave height and corresponding wave period shows good agreement with the measurement data.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.282-289
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• 2022

Analyzing the impact of climate change on the Korean Peninsula on the forest ecosystem is important for the management of subtropical forest bioresources. In this study, we collected location data and bioclimatic variables of the warm-temperate woody plant species, Myrica rubra and Cyzygium buxifolium, and applied the MaxEnt model based on the collected data to estimate the potential distribution area. Precipitation and temperature seasonality in the warmest quarter were the main environmental factors that determined the distribution of M. rubra, and the main environmental factors for S. buxifolium were precipitation in the warmest quarter and precipitation in the wettest quarter. The results of the MaxEnt model by administrative district, the M. rubra showed an area increase rate of 4.6 - 17.7% in the SSP2-4.5 climate change scenario and 13.8 - 30.5% in the SSP5-8.5 climate change scenario. S. buxifolium showed area increase rates of 4.8 - 32.2% in the SSP2-4.5 climate change scenario and 12.9 - 48.6% in the SSP5-8.5 climate change scenario. This study is meaningful in establishing a database and identifying future potential distribution areas of warm and subtropical plants by applying climate change scenarios.

• Journal of Gastric Cancer
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• v.23 no.2
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• pp.355-364
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• 2023

Background: There are no clear guidelines to determine whether to perform D1 or D1+ lymph node dissection in early gastric cancer (EGC). This study aimed to develop a nomogram for estimating the risk of extraperigastric lymph node metastasis (LNM). Materials and Methods: Between 2009 and 2019, a total of 4,482 patients with pathologically confirmed T1 disease at 6 affiliated hospitals were included in this study. The basic clinicopathological characteristics of the positive and negative extraperigastric LNM groups were compared. The possible risk factors were evaluated using univariate and multivariate analyses. Based on these results, a risk prediction model was developed. A nomogram predicting extraperigastric LNM was used for internal validation. Results: Multivariate analyses showed that tumor size (cut-off value 3.0 cm, odds ratio [OR]=1.886, P=0.030), tumor depth (OR=1.853 for tumors with sm2 and sm3 invasion, P=0.010), cross-sectional location (OR=0.490 for tumors located on the greater curvature, P=0.0303), differentiation (OR=0.584 for differentiated tumors, P=0.0070), and lymphovascular invasion (OR=11.125, P<0.001) are possible risk factors for extraperigastric LNM. An equation for estimating the risk of extraperigastric LNM was derived from these risk factors. The equation was internally validated by comparing the actual metastatic rate with the predicted rate, which showed good agreement. Conclusions: A nomogram for estimating the risk of extraperigastric LNM in EGC was successfully developed. Although there are some limitations to applying this model because it was developed based on pathological data, it can be optimally adapted for patients who require curative gastrectomy after endoscopic submucosal dissection.