• International Journal of Contents
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• v.13 no.2
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• pp.57-65
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• 2017

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1101-1106
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• 2005

The purpose of this study was to develop a process management system to manage ingot fabrication and the quality of the ingot. The ingot is the first manufactured material of wafers. Operating data (trace parameters) were collected on-line but quality data (measurement parameters) were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Thus, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were employed for data generation, and then modeling was accomplished, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to the control parameters. The dynamic polynomial neural network (DPNN) was used for data modeling that used the ingot fabrication data.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.47-52
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• 2010

실세계와 연동되는 컴퓨팅환경이 가속화 될수록 물리적 오류에 대한 중요성은 점차 커지고 있다. 물리적 오류는 자연현상과 밀접하게 관련되어 있기 때문에, 다양한 변수가 존재하며 오류 예측이 어렵다. 따라서 본 연구에서는 이러한 자연현상에서 발생하는 인자를 수집하고, 이를 분석하여 예측할 수 있도록 데이터마이닝을 적용한 시스템을 제안한다. 본 연구의 현실성 입증을 위해 제안한 시스템을 라인트레이서를 모델로 하여 구현해 보았다.

• Smart Structures and Systems
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• v.25 no.4
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• pp.433-446
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• 2020

In this paper the effect of confining pressure and tunnel depth on the ground vertical settlement has been investigated using particle flow code (PFC2D). For this perpuse firstly calibration of PFC2D was performed using both of tensile test and triaxial test. Then a model with dimention of 100 m × 100 m was built. A circular tunnel with diameter of 20 m was drillled in the middle of the model. Also, a rectangular tunnel with wide of 10 m and length of 20 m was drilled in the model. The center of tunnel was situated 15 m, 20 m, 25 m, 30 m, 35 m, 40 m, 45 m, 50 m, 55 m and 60 m below the ground surface. these models are under confining pressure of 0.001 GPa, 0.005 GPa, 0.01 GPa, 0.03 GPa, 0.05 GPa and 0.07 GPa. The results show that the volume of colapce zone is constant by increasing the distance between ground surface and tunnel position. Also, the volume of colapce zone was increased by decreasing of confining pressure. The maximum of settlement occurs at the top of the tunnel roof. The maximum of settlement occurs when center of tunnel was situated 15 m below the ground surface. The settlement decreases by increasing the distance between tunnel center line and measuring circles in the ground surface. The minimum of settlement occurs when center of circular tunnel was situated 60 m below the surface ground. Its to be note that the settlement increase by decreasing the confining pressure.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.73-83
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• 2020

Recently, the importance of self-directed learning has emerged in the fields of public education, private education, lifelong education, and vocational training education, in which learners can actively cope with knowledge in an infusion-oriented way. However, there are various theoretical knowledge such as concepts and strategies for self-directed learning, but the situation is insufficient for a system where learners can easily receive content in the academic field they want, depending on the actual self-directed learning operation plan or learning area. Therefore, since it is important to provide various learning content in this paper, we utilize text mining techniques to obtain appropriate information and refine and categorize the meaning. On-line, they want to study a system that provides a variety of content in the academic field that learners are trying to acquire.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Geophysics and Geophysical Exploration
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• v.12 no.3
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• pp.268-277
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• 2009

Recently as the interest in the development of domestic ore deposits has increased, we can easily find some studies on exploration geophysics-based ore deposit survey in literature. Geophysical surveys have been applied to the investigation of both metallic and non-metallic ore deposit. For metallic ore-deposit survey, the 2D electrical resistivity method has been popularly used, because metallic mineral deposits are generally more conductive than surrounding media. However, geological structures are 3D rather than 2D structures, which may lead to misinterpretation in 2D inversion section. In this study, 3D effects are examined for several 3D structures such as a width-varying dyke model and a wedge-shaped model. We also investigate the effects of the direction of survey line. Numerical results show that the width-varying dyke model yields some low resistivity zone in the deep part, which is independent of real ore-body location. For the wedge-shaped model, even though the survey line is located apart from the ore body, the 2D inversion section still shows low resistivity zone in the deep part. When the survey line is not perpendicular to the strike of the ore body, the low resistivity zone is slightly broader but shallower than that obtained along the survey line perpendicular to the strike. For the survey lines that have an angle smaller than $45^{\circ}$ with the strike of the ore body, the inversion results are totally distorted. From these results, we conclude that 2-D survey and interpretation can lead to misinterpretation of subsurface structures, which may be linked to economical loss. Eventually, we recommend to apply 3-D rather than 2-D electrical resistivity survey for ore-deposit survey.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.23 no.3
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• pp.201-209
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• 2017

The Big Data, the element of the Fourth Industrial Revolution, is drawing attention as the 4th Industrial Revolution is mentioned in the 2016 World Economic Forum. Big Data is being used in various fields because it predicts the near future and can create new business. However, utilization and research in the field of packaging are lacking. Today packaging has been demanded marketing elements that effect on consumer choice. Big data is actively used in marketing. In the marketing field, big data can be used to analyze sales information and consumer reactions to produce meaningful results. Therefore, this study proposed a method of applying big data in the field of packaging focusing on marketing. In this study suggest that try to utilize the private data and community data to analyze interaction between consumers and products. Using social big data will enable to understand the preferred packaging and consumer perceptions and emotions in the same product line. It can also be used to analyze the effects of packaging among various components of the product. Packaging is one of the many components of the product. Therefore, it is not easy to understand the impact of a single packaging element. However, this study presents the possibility of using Big Data to analyze the perceptions and feelings of consumers about packaging.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.405-412
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• 2019

SOLAS requires that ECDIS be installed on ships of more than 500 gross tonnage engaged in international navigation until the first inspection arriving after July 1, 2018. Several accidents related to the use of ECDIS have occurred with its installation as a new major navigation instrument. The 12 incident reports issued by MAIB, BSU, BEAmer, DMAIB, and DSB were analyzed, and the cause of accident was determined to be related to the operation of the navigator and the ECDIS system. The text was analyzed using the R-program to quantitatively analyze words related to the cause of the accident. We used text mining techniques such as Wordcloud, Wordnetwork and Wordweight to represent the importance of words according to their frequency of derivation. Wordcloud uses the N-gram model as a way of expressing the frequency of used words in cloud form. As a result of the uni-gram analysis of the N-gram model, ECDIS words were obtained the most, and the bi-gram analysis results showed that the word "Safety Contour" was used most frequently. Based on the bi-gram analysis, the causative words are classified into the officer and the ECDIS system, and the related words are represented by Wordnetwork. Finally, the related words with the of icer and the ECDIS system were composed of word corpus, and Wordweight was applied to analyze the change in corpus frequency by year. As a result of analyzing the tendency of corpus variation with the trend line graph, more recently, the corpus of the officer has decreased, and conversely, the corpus of the ECDIS system is gradually increasing.

• Economic and Environmental Geology
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• v.37 no.5
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• pp.509-519
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• 2004

The ground subsidence that occurred in the abandoned coal mining area, Gaeun, Korea, was observed using 25 JERS-1 SAR interferograms from November 1992 to October 1998. We carried out measurements on a subset of image pixels corresponding to point-wise stable reflectors(PS: permanent scatterer) by exploiting a long temporal series of interferometric phases and compared it with the distribution map of in situ examined crack level. PSs could be identified by means of amplitude dispersion index and coherence of the interferograms and the density of PS was much higher in an urban area than in a mountainous region. The measured subsidence rate represented the average velocity in a period of image acquisition and excluded complex nonlinear displacements such as an abrupt collapse. The mean line-of-sight velocity in the study area is 0.19cm/yr and the estimation error is 0.18cm/yr. The center of the abandoned Gaeun coal mine(0.49cm/yr) and the area opposite Gaeun station(1.66cm/yr) were observed as the most highly subsiding areas.