• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.821-831
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• 2013

As a result of recent research, it is reported that the periodic replacements criterion of rails is able to extend as grinding rail surface and using the continuous welded rail (CWR). In this study, we carried out fatigue tests on existing laid rails. Based on the test results, an S-N curve expressing the remaining life of laid rails at a fracture probability of 50% was obtained using weighted probit analysis suitable for small-sample fatigue data sets. As rails used for testing had different histories in terms of accumulated tonnage, the test data were corrected to average out the accumulated tonnage. We estimated the remaining service lives for laid rails on the urban railway using equations developed in the past to estimate rail base bending stress and that surface irregularities into consideration. Therefore, estimating the remaining service life of laid rails showed that the rail replacement period could be extended over 200 MGT, although it is necessary to remove longitudinal rail surface irregularities at welds by grinding. Also, the fatigue test results under fatigue limit, Haibach's rule appling half slope of S-N curve under the fatigue limit was considered more reasonable than modified Miner's rule for estimating rail fatigue life.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.6
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• pp.599-609
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• 2008

Development of computer and electronic technology leads innovative progress in spatial informatics and successful commercialization. Geospatial information technology plays an important role in decision making in various applications. However, information display media are two-dimensional plane that limits visual perception. Understanding human visual processing mechanism to percept stereo vision makes possible to implement three-dimensional stereo image display. This paper proposes on-the-fly stereo image generation methods that are involved with various exterior and camera parameters including exposure station, viewing direction, image size, overlap and focal length. Collinearity equations and parameters related with stereo viewing conditions were solved to generate realisitc stereo imagery. In addition stereo flying simulation scenery was generated with different viewing locations and directions. The stereo viewing is based on the parallax principle of two veiwing locations. This study implemented anaglyphic stereogram, polarization and lenticular stereo display methods. Existing display technology has limitation to provide visual information of three-dimensional and dynamic nature of the real world because the 3D spatial information is projected into 2D plane. Therefore, stereo display methods developed in this study improves geospatial information and applications of GIS by realistic stereo visualization.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.307-317
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were 8.1cm, 24.7cm, 35.9cm, respectively.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.593-603
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• 2010

Most spatial data acquisition systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly reduced by employing an omni-directional camera that is capable of acquiring images in every direction. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by extending the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images. The proposed mathematical model includes three main parts; observation equations based on the collinearity equations newly derived for omni-directional images, stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four different types of mathematical models. With the type where only GCPs are used as the constraints, the proposed BBA can provide the most accurate results, ${\pm}5cm$ of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.193-201
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• 2021

This study is a study of factors affecting pre-service teachers' learning immersion in online science classes. The results of a survey on the responses of 88 pre-primary teachers to online science classes were interpreted. In online science class, independent variables were set as ease, usefulness, and social presence, and dependent variables were set as learning immersion. Online science classes were conducted based on the university's LMS system. The results of the study were interpreted by regression analysis for t-test, correlation between factors, and multicollinearity test in pre-post-responses of pre-service teachers for ease, usefulness, and social presence. The results of this study are as follows. First, there was a significant effect in the before-and-after tests of factors in the online science class. Second, the correlation coefficient between factors in online science class is .306 for sense of community and mutual support and concentration at the significance level of .01, and .354 for learning immersion and open communication, indicating that there is a correlation. Third, considering the effective results of the pre-post test of each factor in the online science class, it cannot be interpreted that a particular factor had an effect, but it is interpreted that learning was immersed in ease, usefulness, and a sense of social presence.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.887-894
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• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.93-101
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• 2019

The purpose of this study is to identify the control effect of empowerment from the effect to the task satisfaction of the nursing staff of the manager of followership, to study the relationship between the followership of nursing manager, task-satisfaction and empowerment that the nurse recognized. The data collection was conducted for nurses working at four general hospitals with 500 beds or more in Jeollanam-do from March 2 to March 17, 2019. The data were analyzed with the general characteristics of the subjects, and the degree of measurement variables were determined by frequency analysis and technical statistical analysis. The differences in the relevant factors according to the general characteristics were analyzed by t-test and ANOVA, and the correlation and multicollinearity among the measurement variables were analyzed as Pearson's correlation coefficiency, and Moderator regression analysis was performed to verify the adjustment effect of the empowerment. The results of the study showed significant results nursing managers' followership, empowerment and job satisfaction by the educational level. In addition, difference from work experience has shown statistically significant differences in job satisfaction. It has been shown that there is a static correlation between the followership of nursing manager that the subject recognized, empowerment and task satisfaction, the influence between followership and job satisfaction has been found to act as adjustment variables on empowerment. The conclusion of this study is meaningful in that it has verified its influence by applying recently emerging followership to nursing field. From now on, extensive research is important for the members to develop the leadership of members and followership.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.73-85
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• 2021

This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.83-98
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• 2021

This study identified the influence of employees' hybrid start-up intention (intention to start a business while maintaining a job) on the employees' self-determination motivation (intrinsic motivation, extrinsic motivation) and prior knowledge through the Model of Goal-directed Behavior (MGB). We used a PLS-SEM called SmartPLS 3.0 for 126 valid samples collected by judgement extraction for office workers throughout June 13, 2020 to July 3, 2020, and empirically evaluated the measurement model (internal consistency reliability, convergent and discriminant validity) and the structural model (multicollinearity, determination coefficient, effect size, predictive relevance, etc.). Only the intrinsic motivation for realizing the hybrid start-up goal of office workers had a significant impact on the hybrid start-up attitude and subjective norms, and the prior knowledge of hybrid start-up had a significant impact on the hybrid start-up desire and the hybrid start-up intention. In order to induce hybrid start-ups for workers with unstable employment, we need systems and programs that can inspire employees with intrinsic motivation and knowledge about hybrid start-up, so follow-up researches are necessary to analyze about government systems and consulting support that can promote hybrid start-up.