• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.10a
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• pp.112-120
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• 1992

The use of Pre-processor for building structural analysis used to rely upon filling up fixed format data, which was ineffective and error-prone. This research attempts to integrate structural analysis system with DBMS and CAD system in order to make it easy to exchange data between pre-process, analysis, and post-process stages. Automatic generation of database from pre-process stage allows easy preparation of main input data for other structural analysis programs. CAD system with some sub-programs written in LISP and C works as a graphic user interface. This approach gives an easy, effective and error-free way of inputing data for structural analysis.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.184-187
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• 2005

Forging process is one of the forming process and is used widely in automobile parts and manufacture industry. Especially the gears like spur gear, helical gear, bevel gear were produced by machine tool, but recently they have been manufactured by forging process. The goal of this study is to study forming process with data obtained by comparison between forward extrusion and upsetting simulation results and formability improvement by various heat treatment conditions. By analysis data of 3D FEM by upsetting and forward extrusion forming, the forming process of clutch gear develops using data based on 3D FEM analysis. Through tensile test using specimens by various heat treatment conditions, the optimal heat treatment condition is obtained by comparison the results of tensile test.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• The Journal of Bigdata
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• v.1 no.1
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• pp.41-51
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• 2016

Big data is an emerging issue as large data which was impossible to be processed in the past is possible to be handled with the development of information and communication technology. Manufacturing is the most promising field that big data is applied such that there are abundant data available. It is important to improve an efficiency of manufacturing process for quality control and production efficiency because the processes from production design, sales, productions and so on are mixed intricately. This study proposes big data cloud service for manufacturing analysis using a big data technology and a process mining technique. It is expected for manufacturing corporations to improve a manufacturing process and reduced the cost by applying the proposed service. The service provides various analyses including manufacturing analysis and manufacturing duration analysis. Big data cloud service has been implemented and it has been validated by conducting a case study.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.415-427
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• 2003

Purpose: The purpose of this study was to investigate the degree and pattern of knowledge management process, and to identify the relationship between knowledge management process and organizational effectiveness in clinical nurses. Method: Participants were 665 regular clinical nurses who had worked for over 1 year in general units of 9 tertiary medical hospitals including 2 national university hospitals, 5 university hospitals, and 2 hospitals founded by business enterprises. Data were collected from March to May 2003 through questionnaires. Four structured instruments were used to collect the data: Knowledge Management Process Scale(Jeong, Lee, Lee, & Kim, 2003), cCommitment Questionnaire(Mowday, Steers, & Porter, 1979), General Satisfaction Scale(CooK, Hepworth, Wall, & Warr, 1981), and one for general characteristics. The data were analyzed using factor analysis, reliability analysis, descriptive analysis, cluster analysis, one-way ANOVA, Scheffe test, correlation analysis with the SPSS for Windows 10.0 program. Result: 1) The average score for knowledge management process in nurses was $3.08{\pm}.54$ on a 5-point Likert scale. In order from highest mean score, the elements of knowledge management process, were Knowledge $Utilization(3.35{\pm}.57)$, Knowledge $Sharing(3.07{\pm}.58)$, Knowledge $Creation(2.99{\pm}.63)$, and Knowledge $Storage(2.91{\pm}.82)$. 2) Four knowledge management patterns for nurses, which were derived from cluster analysis, were inactivate pattern, delayed pattern, activate pattern, and high-activate pattern of knowledge management. 3) The degree of knowledge management process activation and 4 elements of knowledge management process, Knowledge Creation, Knowledge Storage, Knowledge Sharing, and Knowledge Utilization, were significantly correlated with nurses' organizational commitment and job satisfaction(p=.000). 4) The nurses' organizational commitment and job satisfaction showed significant differences according to the knowledge management patterns derived from cluster analysis of high-activate pattern, activate pattern, delayed pattern, inactivate pattern(p=.000). Conclusion: These results suggest that there are four knowledge management patterns for nurses, and knowledge management process positively affects the nurses' organizational commitment and job satisfaction. From the above findings, knowledge management process is empirically verified as a useful and effective method to increase organizational effectiveness, and develop the organization.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.457-469
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• 2007

Process Capability Indexes(PCI) are used as the measure for evaluation of process capability analysis and is the statistical method for efficient process control. The fourth generation $PCI(C_{psk})$ is constructed from $C_{pmk}$ by introducing the factor $\mid\mu-T\mid$ in the numerator as an extra penalty for the departure of the process mean from the preassigned target value T And Process Incapability Indexes(PII) are presented by inversing PCI and include the information of PCI. This paper introduces the PII $C_{ss}^*$ provide manager with various information of process and include Gage R&R. PII $C_{ss}^*$ is presented by inversing PCI $C_{psk}$ and include the information of PCI $C_{psk}$.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.175-195
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• 1996

In conventional analysis methods, data and process are loosely coupled for building information systems. Several object oriented approaches have been proposed to integrate data and process. However, object oriented analysis requires a radical paradigm and thus system analysts find difficulties in generating object models direcctly from end users. To alleviate these difficulties, this paper proposes a methodology for deriving an object model by using structured analysis results. Objects are obtianed primarily from entities in Entity-Relationship Diagram. Methods are obtained through the analysis of the relationship between processes and data stores in Data Flow Diagram Methods are assigned to the objects by using object/process matrices. A real-life case is illustrated to demonstrate the usefulness of the methodology.