• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.13-19
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• 2015

Railway vehicle equipment goes back again to the state just before when failure by the repair. In repairable system, we are interested in the failure interval. As such, a statistical model of the point process, NHPP power law is often used for the reliability analysis of a repairable system. In order to derive a quantitative reliability value of repairable system, we analyze the failure data of the air brake system of the train line 7. The quantitative value is the failure intensity function that was modified, converted into a cost-rate function. Finally we studied the optimal number and optimal interval in which the costs to a minimum consumption point as cost-rate function. The minimum cost point was 194,613 (won/day) during the total life cycle of the braking system, then the optimal interval were 2,251days and the number of optimal preventive maintenance were 7 times. Additionally, we were compared to the cost of a currently fixed interval(4Y) and the optimum interval then the optimal interval is 3,853(won/day) consuming smaller. In addition, judging from the total life, "fixed interval" is smaller than 1,157 days as "optimal interval".

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.185-190
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• 2009

The event sequence of the same type from a set of events having time property can be summarized in one event. But if the event sequence having an interval, It is reasonable to be summarized more than one in independent sub event sequence of each other. In this paper, we suggest a method of temporal data mining that summarizes the interval events based on Allen's interval algebra and finds out interval event association rule from interval events. It provides better knowledge than others by using concept of an independent sub sequence and finding interval event association rules.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2805-2812
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• 2018

The methylation score, expressed as a percentage of the methylation status data derived from the iterative sequencing process, has a value between 0 and 1. It is contrary to the assumption of normal distribution that simply applying the t-test to examine the difference in population-specific methylation scores in these data. In addition, since the result may vary depending on the number of repetitions of sequencing in the process of methylation score generation, a method that can analyze such errors is also necessary. In this paper, we introduce the symbolic data analysis and the interval K-S test method which convert observation data into interval data including uncertainty rather than one numerical data. In addition, it is possible to analyze the characteristics of methylation score by using Beta distribution without using normal distribution in the process of converting into interval data. For the data analysis, the nature of the proposed method was examined using sequencing data of actual patients and normal persons. While the t-test is only possible for the location test, it is found that the interval type K-S statistic can be used to test not only the location parameter but also the heterogeneity of the distribution function.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.369-377
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• 2007

Histogram is one of tools that efficiently summarize data, and it is widely used for selectivity estimation and approximate query answering. Existing histogram construction algorithms are applicable to point data represented by a set of values. As often as point data, we can meet interval data such as daily temperature and daily stock prices. In this paper, we thus propose the histogram construction algorithms for interval data by extending several methods used in existing histogram construction algorithms. Our experiment results, using synthetic data, show our algorithms outperform naive extension of existing algorithms.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.301-314
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• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.129-140
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• 2004

In this paper, we conduct the research about optimal aggregation interval of travel time data on interrupted traffic flow and verify the reliability of AVI collected data by using car plate matching method in RTMS for systematic collection and analysis of link travel time data on interrupted traffic flow rural arterial. We perform Kolmosorov-Smirnov test on AVT collected sample data and on entire population data, and conclude that the sample data does not represent pure random sampling and hence includes sample collection error. We suggest that additional review is necessary to investigate the effectiveness of AVI collected sample data as link representative data. We also develop statistical model by applying two estimation techniques namely point estimation and interval estimation for calculating optimal aggregation interval. We have implemented our model and determine that point estimate is preferable over interval estimate for exactly selecting and deciding optimal aggregation interval. Our final conclusion is that 5-minute aggregation interval is optimal to estimate travel time in RTMS, as is currently being used our investigation is based on AVI data collected from Yang-ji to Yong-in $42^{nd}$ National road.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.235-243
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• 2007

Sensor network is used to obtain sensing data in various area. The interval to sense the events depends on the type of target application and the amounts of data generated by sensor nodes are not constant. Many applications exploit long sensing interval to enhance the life time of network but there are specific applications that requires very short interval to obtain fine-grained, high-precision sensing data. If the number of nodes in the network is increased and the interval to sense data is shortened, the amounts of generated data are greatly increased and this leads to increased amount of packets to transfer to the network. To transfer large amount of packets fast, it is necessary that the delay between successive packet transmissions should be minimized as possible. In Sensor network, since the Operating Systems are worked on the event driven, the Timer Event is used to transfer packets successively. However, since the transferring time of packet completely is varies very much, it is very hard to set appropriate interval. The longer the interval, the higher the delay and the shorter the delay, the larger the fail of transfer request. In this paper, we propose ESTEO which reduces the delay between successive packet transmissions by using SendDone Event which informs that a packet transmission has been completed.In ESTEO, the delay between successive packet transmissions is shortened very much since the transmission of next packet starts at the time when the transmission of previous packet has completed, irrespective of the transmission timee. Therefore ESTEO could provide high packet transmission rate given large amount of packets.

• Journal of Korean Society on Water Environment
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• v.28 no.2
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• pp.247-254
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• 2012

Soil and Water Assessment Tool (SWAT) model has been widely used in estimation of flow and water quality at various watersheds worldwide, and it has an auto-calibration tool that could calibrate the flow and water quality data automatically from thousands of simulations. However, only continuous measured day flow/water quality data could be used in the current SWAT auto-calibration tool. Therefore, 8-day interval flow and water quality data measured nationwide by Korean Ministry of Environment (MOE) could not be used in SWAT auto-calibration even though long-term flow and water quality data in the Korean Total Maximum Daily Load (TMDL) watersheds available. In this study, current SWAT auto-calibration was modified to calibrate flow and water quality using 8-day interval flow and water quality data. As a result of this study, the Nash and Sutcliffe Efficiency (NSE) values for flow estimation using auto-calibration are 0.77 (calibration period) and 0.68 (validation period), and NSE value for water quality (T-P load) estimation (using the 8-day interval water quality data) is 0.80. The enhanced SWAT auto-calibration could be used in the estimation of continuous flow and water quality data at the outlet of TMDL watersheds and ungaged point of watersheds. In the next study, the enhanced SWAT auto-calibration will be integrated with Web based Load Duration Curve (LDC) system, and it could be suggested as methods of appraisal of TMDL in South Korea.

• Journal of Biomedical Engineering Research
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• v.6 no.2
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• pp.53-62
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• 1985

The position and time interval of wave components of the electrocardiogram are used as important data for physician's diagnosis. In case of using the existing definition of the onset of the wave component of the electrocardiogram, they have some problems of defining the precise position of the isoelectric line, of defining the limit of the gradient accepted as the onset, and of the gradient being changed by noise. Therefore, in this paper all time intervals and positions of wave components needed for data of diagnosis were obtained correctly by turning point data reduction algorithm and linear regression intersection algorithm, and the viability of the method of intersecting lines was established in comparison to the four methods of calculating the PR interval.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.895-902
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• 2011

This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.