• Journal of Korean Society for Quality Management
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• v.44 no.2
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• pp.389-408
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• 2016

Purpose: This study was designed to analyze the behavioral change of knowledge structures and the trends of research topics in the quality management field. Methods: The network structure and knowledge structure of the words were visualized in map form using co-word analysis, cluster analysis and strategic diagram. Results: Summarizing the research results obtained in this study are as follows. First, the word network derived from co-occurrence matrix had 106 nodes and 5,314 links and its density was analyzed to 0.95. Average betweenness centrality of word network was 2.37. In addition, average closeness centrality and average eigenvector centrality of word network were 0.01. Second, by applying optimal criteria of cluster decision and K-means algorithm to word co-occurrence matrix, 106 words were grouped into seven clusters such as standard & efficiency, product design, reliability, control chart, quality model, 6 sigma, and service quality. Conclusion: According to the results of strategic diagram analysis over time, the traditional research topics of quality management field related to reliability, 6 sigma, control chart topics in the third quadrant were revealed to be declined for their study importance. Research topics related to product design and customer satisfaction were found to be an important research topic over analysis periods. Research topic related to management innovation was emerging state and the scope of research topics related to process model was extended to research topics with system performance. Research topic related to service quality located in the first quadrant was analyzed as the key research topic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• 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.

• Structural Engineering and Mechanics
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• v.21 no.5
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• pp.539-551
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• 2005

Finite elements based on isoparametric formulation are known to suffer spurious stiffness properties and corresponding stress oscillations, even when care is taken to ensure that completeness and continuity requirements are enforced. This occurs frequently when the physics of the problem requires multiple strain components to be defined. This kind of error, commonly known as locking, can be circumvented by using reduced integration techniques to evaluate the element stiffness matrices instead of the full integration that is mathematically prescribed. However, the reduced integration technique itself can have a further drawback - rank deficiency, which physically implies that spurious energy modes (e.g., hourglass modes) are introduced because of reduced integration. Such instability in an existing stiffness matrix is generally detected by means of an eigenvalue test. In this paper we show that a knowledge of the dimension of the solution space spanned by the column vectors of the strain-displacement matrix can be used to identify the instabilities arising in an element due to reduced/selective integration techniques a priori, without having to complete the element stiffness matrix formulation and then test for zero eigenvalues.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.321-332
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• 2012

The special organization that our military service is conscripted by the duty of national defense purposes to the period of military service. So couple of soldiers are think as the corresponds to college, or some knowledge layer from the operating management discarding period, lifetime value. But, now a day changed of the military life, it is when the individual soldiers can have some self-development and the lifelong education in terms of a period, and can changeing the footsteps of the new life that the function as a 'bridge' to activate production of barracks life. So that it can be supported themselves need to strive for human resource development. This study is being discussed on the correlation of military human resources and qualified operating and sustainable and promising future eligibility for qualified by taking advantage of the Boston Consulting Group Matrix(Boston Consulting Group Matrix).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.67-70
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• 2007

본 논문은 클러스터링 문제에서 사전 정보에 대한 활용의 효율을 개선시킬 수 있는 방법을 제안한다. 클러스터링에서 사전 정보의 존재 시 이의 활용은 성능을 개선시킬 수 있는 계기가 될 수 있으므로 그의 활용 폭을 늘리기 위한 방법으로 다양한 사용 방법의 적용인 semi-supervised 클러스터링 앙상블을 제안한다. 사전 정보의 활용 방법의 방안으로써 association-rule의 개념을 접목하였다. 클러스터 수를 다르게 적용하더라도 패턴간의 유사도가 높으면 같은 그룹에 속할 확률은 높아진다. 다양한 초기화에 따른 클러스터의 동작은 사전 정보의 활용을 다양화 시키게 되며, 사전 정보에 충족하는 각각의 클러스터 결과를 제시한다. 결과를 총 취합하여 association-matrix를 형성하면 패턴간의 유사도를 얻을 수 있으며 결국 association-matrix를 통해 클러스터링 할 수 있는 방법을 제시한다.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.351-361
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• 2010

The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods are considered the preferred methods. Selecting a suitable preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The prediction of ILU type preconditioners was considered in [27] where support vector machine(SVM), as a data mining technique, is used to classify large sparse linear systems and predict best preconditioners. In this paper, we apply the data mining approach to the sparse approximate inverse(SAI) type preconditioners to find some parameters with which the preconditioned Krylov subspace method on the linear systems shows best performance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.983-994
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• 1989

This paper presents a new variable structure model following control algorithm for control of manipulators. The reference model is a simple double integrators and the acceleration input for the robot manipulator consists of a proportional and derivative controller for the purpose of trajectory tracking. The control algorithm is derived by using Lyapunov stability theory instead of S.S < O, as is usual in the current VSS controller design. This proposed control algorithm does not require good knowledge of the parameter in the inertia matrix and is easily extendable to robot manipulators with a higher number of links. Also, the new algorithm is computationally fast because of not requiring the matrix inversion. The computer simulation was carried out to evaluate the performance of the proposed VSMFC.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.16-19
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• 2006

Humin is the insoluble fraction of humic materials and play an important roles in the irreversible sorption of hydrophobic organic contaminants onto soil particles. However, there have been limited knowledge about the sorption and chemical properties of humin due to the difficulties in its separation from the inorganic matrix(mainly clays and oxides). In this study, do-ashed humin was isolated from a soil sample after removing free lipid and alkali-soluble humic fractions followed by dissolution of mineral matrix with 2% HF, and characterized by elemental analysis, C-13 NMR spectroscopic method. Sorption behavior of 1-naphthol with humin was also investigated from aqueous solution. C-13 NMR spectra indicate that humin molecules are mainly made up of aliphatic carbon including carbohydrate, methylene chain etc.. Sorption intensity for 1-naphthol was increased as organic carbon content of humin increased and log Koc values for the 1-naphthol sorption were determined to be ${\sim}3.12$

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.78-89
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• 2003

Ordering is performed to reduce the amount of fill-ins of the Cholesky factor of a symmetric positive definite matrix. This paper proposes a new ordering algorithm that reduces the fill-ins of the Cholesky factor iteratively by elimination tree rotations and clique separators. Elimination tree rotations have been used mainly to reorder the rows of the permuted matrix for the efficiency of storage space management or parallel processing, etc. In the proposed algorithm, however, they are repeatedly performed to reduce the fill-ins of the Cholesky factor. In addition, we presents a simple method for finding a minimal node separator between arbitrary two nodes of a chordal graph. The proposed reordering procedure using clique separators enables us to obtain another order of rows of which the number of till-ins decreases strictly.