• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.881-891
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• 2017

In this paper, we described the new TLE(Two Line Elements) generation method based on the compansation technique by using past TLEs(Two Line Elements) released by JSpOC(Joint Space Operation Center) in USA to reduce the orbit prediction error for long duration of SGP4(Simplified General Perturbations 4) which is a simplifed and analytical orbit propagator. The orbital residuals the orbital difference between two ephemeris for the first TLE only and for the all TLEs updated by JSpOC for the past some period was applied for this algorithm instead of general orbit determination software. Actually, in these orbital residuals, the trend of orbit prediction error from SGP4 is included. Thus, it is possible to make a simple residual function from these orbital residulas by using the fitting process. By using these residual functions with SGP4 prediction data for the currnet TLE data, the compansated orbit prediction can be reconstructed and the orbit prediction error for long duration of SGP4 is also reduced. And it is possible to generate new TLE data from it. In this paper, we demonstraed this algorithm in simple simulation, and the orbital error is decreased dramatically from 4km for the SGP4 propagation to 2km for it during 7 days as a result.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.6
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• pp.323-332
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• 2018

Recent two moderate earthquakes (2016 $M_w=5.4$ Gyeongju and 2017 $M_w=5.5$ Pohang) in Korea provided the unique chance of developing a set of relations to estimate instrumental seismic intensity in Korea by augmenting the time-history data from MMI seismic intensity regions above V to the insufficient data previously accumulated from the MMI regions limited up to IV. The MMI intensity regions of V and VI was identified by delineating the epicentral distance from the reference intensity statistics in distance derived by using the integrated MMI data obtained by combining the intensity survey results of KMA (Korea Meteorological Administration) and 'DYFI (Did You Feel It)' MMIs of USGS. The time-histories of the seismic stations from the MMI intensity regions above V were then preprocessed by applying the previously developed site-correction filters to be converted to a site-equivalent condition in a manner consistent with the previous study. The average values of the ground-motion parameters for the three ground motion parameters of PGA, PGV and BSPGA (Bracketed Summation of PGA per second for 30 seconds) were calculated for the MMI=V and VI and used to generate the dataset of the average values of the ground-motion parameters for the individual MMIs from I to VI. Based on this dataset, the linear regression analysis resulted in the following relations with proposed valid ranges of MMI. $MMI=2.36{\times}log_{10}(PGA(gal))+1.44$ ($I{\leq}MMI$$MMI=2.44{\times}log_{10}(PGV(kine))+4.86$ ($I{\leq}MMI$$MMI=2.59{\times}log_{10}(BSPGA(gal{\cdot}sec))-1.02$ ($I{\leq}MMI$

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.55-63
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• 2002

It has been known well that Traffic Conflict Technique(TCT) used to evaluate the safety of intersections in the case of shortage of traffic accidents data and surveying time. Because data for using in traffic conflict technique that is collected by trained surveyors, it is rely on the knowledge, experience and the characteristics of them. The data of surveying generate varying result. So, its variance must minimize and then it is considered of calculating in traffic conflict technique however obviously technique to minimize has not developed until now. So, this paper has a focus on the technical method to minimize the variance. For this, it applied the fuzzy reasoning theory to the existed traffic conflict technique that is the most comprehensive method in the country and then developed the new traffic conflict technique model. Fuzzy reasoning theory is a very appropriate method for minimizing the variance among surveyors because it can systematically calculate the uncertainty of surveyors by approximation reasoning structure. The result of analysis from pilot study, the new Procedure in this Paper minimized the variance by 53 Percentiles and it increased the value of conversion factor two times than the exited traffic conflict technique. The method proposed in this paper, it can be used for evaluating the safety of intersection, and before and after analysis of improving Project of black spots.

• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.77-82
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• 2003

Virtual colonoscopy is a non-invasive computerized procedure to detect polyps by examining the colon from a CT data set. To fly through the inside of colons. the extraction of a suitable flight-path is necessary to Provide the viewpoint and view direction of a virtual camera. However. manual path extraction by Picking Points is a very time-consuming and difficult task due 1,c, the long and complex shape of colon. Also, existing automatic methods are computationally complex. and tend to generate an improper and/or discontinuous path for complicated regions. In this paper, we propose a fast flight-path generation algorithm using the distance and order maps. The order map Provides all Possible directions of a path. The distance map assigns the Euclidean distance value from each inside voxel to the nearest background voxel. By jointly using these two maps. we can obtain a proper centerline regardless of thickness and curvature of an object. Also, we Propose a simple smoothing technique that guarantees not to collide with the surface of an object. The phantom and real colon data are used for experiments. Experimental results show that for a set of human colon data, the proposed algorithm can provide a smoothened and connected flight-path within a minute on an 800MHz PC. And it is proved that the obtained flight-Path provides successive volume-rendered images satisfactory for virtual navigation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.31-38
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• 2012

The geo-referencing accuracy of the images acquired by a UAV based multi-sensor system is affected by the accuracy of the mounting parameters involving the relationship between a camera and a GPS/INS system as well as the performance of a GPS/INS system. Therefore, the estimation of the accurate mounting parameters of a multi-sensor system is important. Currently, we are developing a low altitude multi-sensor system based on a UAV, which can monitor target areas in real time for rapid responses for emergency situations such as natural disasters and accidents. In this study, we suggest a system calibration method for the estimation of the mounting parameters of a multi-sensor system like our system. We also generate simulation data with the sensor specifications of our system, and derive an effective flight configuration and the number of ground control points for accurate and efficient system calibration by applying the proposed method to the simulated data. The experimental results indicate that the proposed method can estimate accurate mounting parameters using over five ground control points and flight configuration composed of six strips. In the near future, we plan to estimate mounting parameters of our system using the proposed method and evaluate the geo-referencing accuracy of the acquired sensory data.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.139-150
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• 2020

The purpose of this study was to understand the influence of social network site (SNS) addiction on the ability of nursing students to adjust to university life and to generate the basic data to develop programs that could improve this ability. The data was collected from questionnaires that were filled out by 255 nursing students in two universities located in Jeollanam-do and Gyeonggi-do from May 16, 2020 to May 20, 2020. The data was analyzed using the SPSS 23.0 program (frequency, ANOVA, Pearson's correlation, multiple regression). The mean scores of SNS addiction and adjustment to university life were 2.16±0.54 (range:1-5) and 3.13±0.39 (range:1-5) respectively. SNS addiction accounts for 27% of the variance in adjustment to university life. The study concluded that SNS addiction negatively affects adjustment to university life among nursing students. To ensure better adjustment a program should be developed to treat SNS addiction early. Also, a study will have to be conducted to determine the time when tendency toward SNS addiction becomes apparent, to initiate treatment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.878-883
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• 2020

Cryptographic algorithms are used to prevent the illegal manipulation of data. They are divided into public-key cryptosystems and symmetric-key cryptosystems. Public-key cryptosystems require considerable time for encryption and decryption compared to symmetric-key cryptosystem. On the other hand, key management, and delivery are easier for public-key cryptosystems than symmetric-key cryptosystems because different keys are used for encryption and decryption. Furthermore, hash functions are being used very effectively to verify the integrity of the digital content, as they always generate output with a fixed size using the data of various sizes as input. This paper proposes a method using RSA public-key cryptography and a hash function to determine if a digital image is deformed or not and to detect the manipulated location. In the proposed method, the entire image is divided into several blocks, 64×64 in size. The watermark is then allocated to each block to verify the deformation of the data. When deformation occurs, the manipulated pixel will be divided into smaller 4×4 sub-blocks, and each block will have a watermark to detect the location. The safety of the proposed method depends on the security of the cryptographic algorithm and the hash function.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.189-194
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• 2016

A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the $Fr{\acute{e}}chet$ distance measure is used to calculate the distance between a pair of trajectories. However, the $Fr{\acute{e}}chet$ distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete $Fr{\acute{e}}chet$ distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete $Fr{\acute{e}}chet$ distance.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.251-271
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• 2007

This paper studies the problem of classifying documents with labeled and unlabeled learning data, especially with regards to using document similarity features. The problem of using unlabeled data is practically important because in many information systems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. There are two steps In general semi-supervised learning algorithm. First, it trains a classifier using the available labeled documents, and classifies the unlabeled documents. Then, it trains a new classifier using all the training documents which were labeled either manually or automatically. We suggested two types of semi-supervised learning algorithm with regards to using document similarity features. The one is one step semi-supervised learning which is using unlabeled documents only to generate document similarity features. And the other is two step semi-supervised learning which is using unlabeled documents as learning examples as well as similarity features. Experimental results, obtained using support vector machines and naive Bayes classifier, show that we can get improved performance with small labeled and large unlabeled documents then the performance of supervised learning which uses labeled-only data. When considering the efficiency of a classifier system, the one step semi-supervised learning algorithm which is suggested in this study could be a good solution for improving classification performance with unlabeled documents.