• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.438-443
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• 2005

In this paper, we introduce the on-line test of the current collection characteristics for HSR 350x(Korea High Speed Train project) that have been performed in the Kyoung-bu high-speed line since 2002. Through the analysis of measured data during on-line test, the variation trends of contact force between pantograph and catenary according to the track conditions and driving patterns are reviewed. This study shows that the track conditions do not affect on the dynamic performance between pantograph and the driving patterns are important factors in determining the good interaction of them.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.56-62
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• 2010

The purpose of this study was to compare the difference of driving pattern between male and female drivers for a straight driving and unexpected situation using driving simulator and motion data. The participants included total 60 university students; 30 males aged 24.3$\pm$1.4 years and 30 females aged 23.2$\pm$1.9 years with 1~3 years of driving experience. The driving task required participants to keep the constant distance (20m, 25m or 30m) with preceding vehicle running at 55~65km/hr speed using driving simulator which was programed unexpected situation for two minutes. Simulator and motion data were acquired. The acquired data was divided in straight driving block for 40 second and unexpected situation block for 2 second. The coefficient of variation (CV) of lane keeping and jerk-cost (JC) function were analyzed for straight driving and unexpected situation blocks. The results show that CV was smaller in males than females for both straight and unexpected situation blocks (p < .05). JC was smaller in females than males for both straight and unexpected situation blocks. As the distance of vehicles become longer, JC was smaller for both male and female (p < .05).

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.107-108
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• 2011

Since the development of railway technology, the current urban Railway the first train line in the country for safe operation control automatic/unattended operation, automatic train operation equipment available (ATO) on time and reliable operation has introduced. ATO Automatic operation controlled by the value (Target velocity) and the feedback value (Actual velocity) by the error between the backing and braking of the train by repeated low energy efficiency. In this paper, given a fixed distance stations between time operation with minimal energy in the driving characteristics and driving trains are modeled. Therefore, in line 5 real route time sectional drive straight sections for experimental data analysis / draft Section / curved and section of the train on that line is selected according to the changing driving patterns to minimize the energy optimal driving patterns were presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.1-12
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• 2009

Recently, as the importance of Economical Driving has been gradually growing up, the needs for research on automatic analysis of driving patterns that will ultimately provide drivers the methods for Economical Driving have been increasingly risen. Based on this purpose, we have executed two things in this paper. First, we have collected overall driving information such as date, distance, driving time, speed, idle time, sudden acceleration/deceleration count, and the amount of fuel consumption. Second, we have analyzed the influences of driving patterns on economical driving by employing the data mining techniques. These results can be applied in preventing bad driving patterns which will have consequently bad effects on Economical Driving in two aspects: by presenting some information on the terminal of the vehicles such as idle time, over-speed time, sudden acceleration/deceleration count continuously and by providing the drivers with alert information when the idle time ratio and the over-speed time ratio are excessive.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.103-109
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• 2012

Digital speedometer which is supposed to provide the basic data for analyzing human factors of drivers has a limitation for human behavior studies of drivers, because it records limited driving information including GPS velocities. Besides, Black Box, which is currently being actively commercialized in the market, records mostly vehicles' risky patterns rather than drivers' behaviors. As a result, it also shows a limit to analyze dangerous driving patterns. This study performed a risky driving study for human factor analysis. This study conducted before and after comparisons for real time warning study using a risky driving judgment device. The analysis was conducted based on Longitudinal acceleration, Lateral acceleration, and Yaw rate of vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.87-97
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• 2018

Bus, a major transportation mode, doesn't have a systematical evaluation system for dangerous driving behavior yet. This paper analyzes the characteristics and pattern of bus driving behavior using Digital Tacho Graph(DTG) data on express bus. 8 types of dangerous driving behavior were considered according to timeslot, the day of week and weather condition. As results, rapid acceleration, rapid left right turn and rapid deceleratio accounted for more than 97% and relatively high percentages were shown in dawn, on Friday and on the clear day, respectively. From the statistical analysis, correlation between the dangerous driving types and difference according the timeslot were found, and 3 groups considering the level of the dangerous driving were suggested. This study contributes to setting an efficient and reliable eduction system for using driving simulators.

• Journal of Korean Academy of Nursing
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• v.20 no.1
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• pp.79-87
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• 1990

Clinical and epedemiologic studies of coronary heart disease(CHD)have from time to time over the last three decades found associations between prevalence of CHD and behavioral attributes and cigarette smoking. The main purpose of this study is reduced to major risk factor of coronary heart disease through prohibition of smoking and control of behavior pattern. The subjects consisted of 120 smokers and 90 non-smokers who were married men older than 30 years working in officers. The officers were surveyed by means of questionnaire September 26 through October 6, 1989. The Instruments used for this study was a self-administered measurement tool composed of 59 items was made through modifications of Jenkuns Activity Survery(JAS). The Data were analysed by SAS(Statistical Analsis System) program personal computer. The statistical technique used for this study were Frequency, x$^2$-test, t-test, ANOVA, Pearson Correlation Coefficient. The 15 items were chosen with items above 0.3 of the factor loading in the factor analysis. In the first factor analysis 19 factors were extracted and accounted for 86% of the total variance. However when the number of factors were limited to 3 in order to derive Jenkins classification, three factors were derived. There names are Job-Involvement, Speed & Impatience, Hard-Driving. Each of them includes 21 items, 21 and 9, respectively. The results of this study were as follow : 1. The score of the smoker group and non-smoker group in Job-Involvement(t＝5.7147, p＜0.0001), Speed & Impatience(t＝4.6756, p＜.0001), Hard-Driving(t＝8.0822, p＜.0001) and total type A behavior pattern showed statistically significant differences(t＝8.1224, p＜.0001). 2. The score of type A behavior pattern by number of cigarettes smoked daily were not statistically significant differences. 3. The score of type A behavior pattern by duration of smoking were not significant differences. It was concluded that the relationship between smokers and non - smokers of type A behavior pattern was statistically significant difference but number of cigarettes smoked daily and duration of smoking were not significant differences. Therefore this study is needed to adequate nursing intervention of type A behavior pattern in order to elevated to educational effect for prohibition of cigarette smoking.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.3
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• pp.110-115
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• 2010

This paper studies the vibration characteristics of a vehicle seat on several driving conditions. Modal test for a vehicle seat is conducted for the three different boundary conditions: on the rigid jig, BIW and the full vehicle. In driving on various road conditions and speeds, vibration level is measured at several locations including seat mounting and seat-back. The vibration pattern for each driving condition is found where the suspension mode and the 1st bending and torsion modes of the seat make the major contribution on it.