• 한국디지털정책학회:학술대회논문집
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• 2003.12a
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• pp.191-210
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• 2003

In modern dynamic management environment, there is growing recognition that information & knowledge management systems are essential for CEO's Efficient/effective decision making, As a key component to cope with this current, we suggest the management performance analysis system based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view, The relationship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On -Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.55-70
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• 2013

This paper deals with the Bayesian analysis of the failure data of a repairable mechanical system subject to minimal repairs and periodic overhauls. The effect of overhauls on the reliability of the system is modeled by a proportional age reduction model and the failure process between two successive overhauls is assumed to be 2-parameter Engelhardt-Bain process (2-EBP). Power Law Process (PLP) model has a disadvantage which 2-EBP can overcome. On the basis of the observed data and of a number of suitable prior densities, point and interval estimation of model parameters, as well as quantities of relevant interest are found. Also hypothesis tests on the effectiveness of performed overhauls have been developed using Bayes factor. Sensitivity analysis of improvement parameter is carried out. Finally, a numerical application is used to illustrate the proposed method.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.153-159
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• 2005

Microarray gene expression technology has applications that could refine diagnosis and therapeutic monitoring as well as improve disease prevention through risk assessment and early detection. Especially, microarray expression data can provide important information regarding specific genes related with metastasis through an appropriate analysis. Various methods for clustering analysis microarray data have been introduced so far. We used twostep clustering fot ascertain metastasis related gene through t-test. Through t-test between two groups for two publicly available medulloblastoma microarray data sets, we intended to find significant gene for metastasis. The paper describes the process in detail showing how the process is applied to clustering analysis and t-test for microarray datasets and how the metastasis-associated genes are explorated.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.221-226
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• 2024

This study used text mining, a big data analysis technique, to explore XR trends in South Korea. For this research, I utilized a big data platform called BigKinds. I collected data focusing on the keyword 'XR', spanning approximately 14 years from 2010 to 2024. The gathered data underwent a cleansing process and was analyzed in three ways: keyword trend analysis, relational analysis, and word cloud. The analysis identified the emergence and most active discussion periods of XR, with XR devices and manufacturers emerging as key keywords.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.63-80
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• 2022

In this paper, we study structural changes and performance gains in condition-based maintenance process redesign when mobile IoT technology is embedded into urban railway rolling stock. We first develop condition-based maintenance To-Be process model in accordance with the IoT deployment scheme. Secondly, we draw upon theoretical framework of redesign process analysis to develop performance evaluation method suitable to predictive maintenance shift from As-Is ordinary maintenance practice. Subsequently, To-Be process performance evaluations are conducted adopting both the quantitative and qualitative method for time, cost, and dependability dimensions. The results ascertain the considerable benefits captured through detection abnormality prior to actual rolling stock failure occurrence, and details of performance improvements and enhancement of standardization level is revealed. The procedures and results presented in this paper offers useful insights in the fields of IoT economic analysis, condition based maintenance, and business process redesign.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.569-578
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• 2016

With the recent advancement of surveying and technology, the spatial data acquisition rates and precision have been improved continually. As the updates of spatial data are rapid, and the size of data increases in line with the advancing technology, the LOD (Level of Detail) algorithm has been adopted to process data expressions in real time in a streaming format with spatial data divided precisely into separate steps. The existing GRID analysis utilizes the single DEM, as it is, in examining and analyzing all data outside the analysis area as well, which results in extending the analysis time in proportion to the quantity of data. Hence, this study suggests a method to reduce analysis time and data throughput by acquiring and analyzing DEM data necessary for GRID analysis in real time based on the area of analysis and the level of precision, specifically for streaming DEM data, which is utilized mostly for 3D geographic information service.

• Korean Institute of Interior Design Journal
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• v.20 no.3
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• pp.135-142
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• 2011

There occurs subtle shaking in our eyes while in looking at objects and this study sets up the standard of reconciliation from the property of observation and organizes the property of data reconciliation by the observation range to secure the feasibility of reconciliation range and method of the original data obtained from observation experiment and its analysis process. The results from above study can be concluded as in the followings: First, it made clear the process to exclude eye blink and data out of image range from the original data so to set up the range of available data. Second, on the basis of existing theory, it was possible to define the minimum attention time as 0.1 second (3 times of observation) and the visual understanding time of space as 0.3 second (9 times of observation) in the study on the property of observation, and this definition of observation time of sight fixation becomes an important indicator in the analysis of observation data. Third, based on the observation theory of continuity securing and attention, it was able to arrange the standard of reconciliation by carrying out reconciliation works only when fixed data with more than three times of observation showed consecutively before and behind the data with intermittent movements. Fourth, In the sector whether visual understanding occurred (more than 9 times), it increased by 12% for the frequency of observation and by 7.8% for the times of observation compared with the ones before the reconciliation. These results showed to have a constant change by subjects so that it was able to arrange a foundation to secure objective data in the analysis of the observation range and its extent.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1117-1128
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• 2015

In order to secure an important competitive advantage in manufacturing business, an automation and information system from manufacturing process has been introduced; however, small and medium enterprises have not met the power of information in the manufacturing fields. They have been managing the manufacturing process that is depending on the operator's experience and data written by hand, which has limits to reveal cause of defective goods clearly, in the case of happening of low-grade goods. In this study, we analyze critical factors which affect the quality of some manufacturing process in terms of 4M. We also studied the automobile parts processing of the small and medium manufacturing enterprises controlled with data written by hand so as to collect the data written by hand and to utilize sensor data in the future. Analysis results show that there is no deference in defective quantity in machines, while raw materials, production quality and task tracking have significant deference.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.106-111
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• 2020

In the era of the fourth industrial revolution, the application of big data analysis technology is crucial in various industries. In this regard, considerable research is necessary to improve aquafarming productivity, particularly in fish culture, which is one of the primary industries in the world. In this study, a sample experiment using a flop was conducted to improve oyster productivity in fish farms, and a flush was installed in an environment similar to aquaculture farms. Thereafter, the temperature data of the water environment where the formation of burrows considerably improved were collected; the growth rate of burrow seeds was also measured. The gathered experimental data were examined by time series data analysis. Finally, a system that visualizes the analysis results based on big data is proposed. In accord with the results of this study, it is expected that more advanced research on the productivity improvement of oyster aquafarming will be performed.