• Geophysics and Geophysical Exploration
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• v.11 no.2
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• pp.116-126
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• 2008

In gas hydrate survey, seismic amplitude and frequency characteristics play a very important role in determining whether gas hydrate exists. According to the variation of source frequency and scatterer size, we study seismic amplitude characteristics using elastic modeling applied at staggered grids. Generally speaking, scattering occurs in proportion to the square of source frequency and the scatterer volume, which has an effect on seismic amplitude. The higher source frequency is, the more scattering occurs in gas hydrate bearing zone. Therefore, BSR is hardly observed in high frequencies. On the other side, amplitude blanking zone and BSR is clearly observed in lower frequencies although the resolution is poor as a whole. Seismic reflections traveling through free-gas layer below gas hydrate bearing zone decay so severely a high frequency component that a low frequency term is dominant. Amplitude anomaly of BSR result from high acoustic impedance contrast due to free-gas, which is a very crucial factor to estimate gas hydrate bearing zone. Seismic frequency analysis is carried out using wavelet transform method that frequency component could be decomposed with time variation. In application of wavelet transform to the seismic physical experiments data, we can observe that reflections traveling through air layer, which corresponds to the free-gas layer, decay a high frequency component.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.687-699
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• 2020

The government-led top-down development approach for weapons system faces the problem of technological obsolescence now that technology has rapidly grown. As a result, the government has gradually expanded the corporate-led bottom-up project implementation method to the defense industry. The key success factor of the bottom-up project implementation is the ability of defense companies to plan their technologies. This paper presented a method of analyzing patent data through data mining technique so that domestic defense companies can utilize it for technology planning activities. The main content is to propose corporate selection techniques corresponding to the defense communication-electronics sectors and conduct principal component analysis and cluster analysis for the International Patent Classification. Through this, the technology was classified into four groups based on the patents of nine companies and the representative enterprises of each group were derived.

• Journal of Korean Medicine for Obesity Research
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• v.21 no.2
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• pp.80-94
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• 2021

Objectives: The purpose of this study is to re-evaluate the importance of questions of obesity syndrome differentiation (OSD) questionnaire based on real-world survey and to explore the possibility of simplifying OSD types. Methods: The OSD frequency was identified, and variance threshold feature selection was performed to filter the questions. Filtered questions were clustered by K-means clustering and hierarchical clustering. After principal component analysis (PCA), the distribution patterns of the subjects were identified and the differences in the syndrome distribution were compared. Results: The frequency of OSD in spleen deficiency, phlegm (PH), and blood stasis (BS) was lower than in food retention (FR), liver qi stagnation (LS), and yang deficiency. We excluded 13 questions with low variance, 7 of which were related to BS. Filtered questions were clustered into 3 groups by K-means clustering; Cluster 1 (17 questions) mainly related to PH, BS syndromes; Cluster 2 (11 questions) related to swelling, and indigestion; Cluster 3 (11 questions) related to overeating or emotional symptoms. After PCA, significant different patterns of subjects were observed in the FR, LS, and other obesity syndromes. The questions that mainly affect the FR distribution were digestive symptoms. And emotional symptoms mainly affect the distribution of LS subjects. And other obesity syndrome was partially affected by both digestive and emotional symptoms, and also affected by symptoms related to poor circulation. Conclusions: In-depth data mining analysis identified relatively low importance questions and the potential to simplify OSD types.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.126-132
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• 2015

Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.601-611
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• 2005

Due to not only proven stability and reliability but a significant investment and years of accumulated -experience and knowledge, legacy systems have supported the core business applications of a number of organizations over many years. While the emergence of Web-based e-business environments requires externalizing core business processes to the Web. This is a competitive advantage in the new economy. Consequently, organizations now need to mine the business value buried in the legacy systems for reuse in new e-business applications. In this paper we suggest a systematic approach to mining components that perform specific business services and that consist of the legacy system's assets to be leveraged on the modem platform. The proposed activities are divided into several tasks. First, use cases that realize the business processes are captured. Secondly, a design model is constructed for each identified use case in order to integrate the use cases with the similar functionalities. Thirdly, we identify component candidates from the design model and then adjust the component candidates by considering common elements among the candidate components. And also business components are divided into three more fine-grained components to deploy them onto J2EE/EJB environments. finally, we define the interfaces of components which provide functionalities of the components as operations.

• Geophysics and Geophysical Exploration
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• v.3 no.2
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• pp.48-52
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• 2000

We have calculated and analyzed the electromagnetic responses of buried conductive pipes due to a horizontal magnetic dipole source on the pound using a three-dimensional (3-D) finite element method to provide useful guidelines for designing electromagnetic pipe locator and for field operation of the system. For single buried pipe, the horizontal component and the horizontal difference of the vertical component of magnetic field show peaks above the pipe. When comparing the width of response curves of both cases around the peak, horizontal difference of vertical component of magnetic field shows much narrower peak, 2 times narrower at a half of maximum amplitude, than that of horizontal component of magnetic field. Accordingly, we can pinpoint the horizontal location of pipe on the ground more accurately by measuring the horizontal difference of vertical component of magnetic fold. Moreover, it will have a merit in determining the depth of pipe, because the equation for depth estimation is defined just above the pipe. When there are two buried pipes separated by two meters with each other, the response of horizontal difference of vertical component of magnetic field has two separate peaks each of which is located above the pipe whereas horizontal magnetic field response has only one peak above the pipe just below the transmitter. Thus, when there exist more than a buried pipe, measuring the horizontal difference of vertical magnetic field can effectively detect not only the pipe under transmitter but also adjacent ones. The width of response curves also indicates higher resolving ability of horizontal difference of vertical component of magnetic field.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.207-216
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• 2000

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system ; ANSIA (Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA is composed of following component layers : 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. In the data mining agent layer, that plays a key role as a system engine, extracts strategy from the historic negotiation is extracted by competitive learning in neural network. In negotiation agent layer, we propose the autonomous negotiation process model that enables to estimate the strategy of opponent and achieve interactive settlement of negotiation. ANISIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.555-565
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• 2005

To Know customers is very important for the company to survive in its cut-throat competition among coimpetitors. Companies need to manage the relationship with each ana every customer, ant make each of customers as profitable as possible. CRM (Customer relationship management) has emerged as a key solution for managing the profitable relationship. In order to achieve successful CRM customer segmentation is a essential component. Clustering as a data mining technique is very useful to build data-driven segmentation. This paper is concerned with building proper customer segmentation with introducing a credit card company case. Customer segmentation was built based only on transaction data which cattle from customer's activities. Two-step clustering approach which consists of k-means clustering and agglomerative clustering was applied for building a customer segmentation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.17-30
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• 2015

Technology-oriented national R&D programs produce intellectual property as their final result. Patents, as typical industrial intellectual property, are therefore considered an important factor when evaluating the outcome of R&D programs. Among the main components of patent evaluation, in particular, the patent right quality is a key component constituting patent value, together with marketability and usability. Current approaches for patent right quality evaluation rely mostly on intrinsic knowledge of patent attorneys, and the recent rapid increase of national R&D patents is making expert-based evaluation costly and time-consuming. Therefore, this study defines a hierarchy of patent right quality and then proposes how to quantify the evaluation process of patent right quality by combining text mining and regression analysis. This study will contribute to understanding of the systemic view of the patent right quality evaluation, as well as be an efficient aid for evaluating patents in R&D program assessment processes.