• Proceedings of the KSR Conference
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• 2009.05a
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• pp.91-98
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• 2009

For maintaining railroad, accumulated passing tonnage is a determinant factor of appropriate rail replacement time. Recently, Seoul Metro's rail maintaining system and technology is being improved from previous years, which increasing a standard of rail replacement. Thus, this brings importance of estimating and managing for accumulated passing tonnage. In case of light weighted train such as subway, current method of calculating accumulated passing tonnage has defaults of misrepresenting accumulated passing tonnage data. Because current method is based on the weight of passengers and train., and operation data. In addition, currently there is no mechanical and electronic system that could represent and support the accurate data between heavy and non-heavy traffic area, and accumulated passing tonnage is calculated inaccurately by estimating average value each line. The current method of calculating accumulated passing tonnage misleads to unpredictable data that represent inappropriate rail replacement period, which leads to under or over analyzed replacement period. If accumulated passing tonnage is over estimated, rail replacement leads to waste of budget. Hence, it is necessary to construct reliable actual measurement system to manage rail's life safely and efficiently, and in this study the accumulated passing tonnage measurement device is installed with using rail pad of optical fiber sensors and its effect is analyzed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.94-100
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• 2005

Abnormal symptoms on operating conditions of power transformer are monitored by a preventive diagnostic system which prevents the sudden power failure in case of quick progress of abnormal situation. The preventive diagnostic system helps plan the proper maintenance method according to the transformer conditions via accumulated data. KEPCO has adopted the preventive diagnostic system at nine of 345kV substations since 1997. Application techniques of the diagnostic sensors were settled, but diagnostic algorithm and practical use of accumulated data are not yet established. To build up the diagnostic algorithm and effective use of the preventive diagnostic system, the reliability of the data which were accumulated in a server computer is very important. This paper describes the data analysis in the server in order to advance the reliability of the accumulated data of the preventive diagnostic system. The principles and data flows of the diagnostic sensors were analyzed, and the data discrepancy between sensors and server were calibrated.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.534-541
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• 2005

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.131-133
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• 2017

To predict the future, the accumulated data can be fundamental basic. While many prediction methods based on contingency theory have been used, the prediction of LOTTO number can not be based on the contingency theory. But, this research attempts to suggest the method to predict LOTTO numbers through using the change of the prediction capability on accumulated data.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.73-77
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• 2019

Data mining techniques can also be used to increase the efficiency of production in the tax sector, which requires professional skills. As tax-related computerization was carried out, large amounts of data were accumulated, creating a good environment for data mining. In this paper, we have developed a system that can help tax accountant who have existing professional abilities by using data mining techniques on accumulated tax related data. The data mining technique used is random forest and improved by using f1-score. Using the implemented system, data accumulated over two years was learned, showing high accuracy at prediction.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.214-218
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• 1999

The aim of our study is to develop new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggest the techniques of its continuous detection. So as to detect daytime sea fog/stratus(00UTC, May 10, 1999), visible accumulated histogram method and surface albedo method are used. The characteristic value during daytime showed A(min) > 20％ and DA < 10％ when visble accumulated histogram method was applied. And the sea fog region which detected is of similarity in composite image and surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), infrared accumulated histogram method and maximum brightness temperature method are used, respectively. Maximum brightness temperature method(T_max method) detected sea fog better than IR accumulated histogram method. In case of T_max method, when infrared value is larger than T_max, fog is detected, where T_max is an unique value, maximum infrared value in each pixel during one month. Then T_max is beneath 700hpa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which detected by T_max method was similar to the result of National Oceanic and Atmosheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference). But inland visibility and relative humidity didn't always agreed well.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.317-324
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• 2005

Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how In its own. It is very useful to extract knowledge or information from the accumulated existing data by using datamining technique. This paper treats an evolutionary computation method based on genetic programming (GP), which can be one of the components to realize datamining.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.41-60
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• 2004

A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases. becomes increasingly large over time. To mine information from this massive data. it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise. if we use only recent or part of a whole data to mine information or pattern. there can be loss of information. which may be useful. To avoid this problem. we suggest a method that efficiently accumulates information. in the form of rule sets. over time. It takes much smaller storage compared to traditional mining methods. These accumulated rule sets are used as prediction models in the future. Based on theories of ensemble approaches. combination of many prediction models. in the form of systematically merged rule sets in this study. is better than one prediction model in performance. This study uses a customer data set that predicts buying power of customers based on their information. This study tests the performance of the suggested method with the data set alone with general prediction methods and compares performances of them.

• Journal of The Korean Society of Grassland and Forage Science
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• v.7 no.1
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• pp.37-41
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• 1987

The purpose of this study is to clarify the effect of top dressing on the thatch losses in bentgrass (Agrostis palustris). Top dressing materials used were clay loam, sand, zeolite, and sawdust. Data were taken on July 10 ($T_1$), Aug. 7 (($T_2$ ) and Sept. 4 (($T_3$) respectively. The results are summarized as follows: 1. The dry weight of accumulated thatch was significantly different between treatments and dates of survery, and for the interaction of treatment x date of survey. 2. The dry weight of accumulated thatch showed a tendency to decrease as growth progressed in all treatments. (Table 1) The dry weight of accumulated thatch was the smallest at sand but the largest at clay loam in each date of survey. 3. The losses rate of accumulated thatch showed a tendency to slightly increase as affected by top dressing materials. (Table 2) Sand showed a significantly higher losses rate of accumulated thatch than that of other treatments. 4. The dry weight of accumulated thatch showed a significant negative correlation (p<0.01) with the losses rate of accumulated thatch. (Fig. 1) 5. Turf coverage was significant difference between treatments and dates of survey. 6. Turf coverage showed a tendency to increase as growth progressed in all treatments. (Table 3) 7. Turf coverage indicated significant negative correlation (p<0.001) with the dry weight of accumulated thatch. (Fig. 2)

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.