• Journal of Engineering Education Research
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• v.16 no.1
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• pp.35-44
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• 2013

The purpose of this study is to develop an examination system for an e-learning quality manager's certificate which is composed of subjects, criteria, method of examination. The task model of e-learning quality manager was modify and task/knowledge/skill matrix was developed to design the examination system through conferences of DACUM committee and an advisory committee. And a survey was carry out to analyze validity of contents of the examination system. The major findings were as the follow: First, occupational specification, job specification, task specification and task/knowledge/skill matrix were developed. Second, examination subjects were developed based on task/knowledge/skill matrix, which were "Basis of e-Learning and plan of service", "Expulsion and management of e-learning infrastructure", "Development of e-learning contents", "Operation and evaluation of e-learning service". Third, the criteria and methods of examination for an e-learning quality manager's certificate were developed, which is composed of test type, the sum of test items, test time and acceptable standards.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1933-1938
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• 2003

A covariance matrix is a tool that expresses odometry uncertainty of the wheeled mobile robot. The covariance matrix is a key factor in various localization algorithms such as Kalman filter, topological matching and so on. However it is not easy to acquire an accurate covariance matrix because we do not know the real states of the robot. Up to the authors knowledge, there seems to be no established result on the covariance matrix estimation for the odometry. In this paper, we propose a new method which can estimate the covariance matrix from empirical data. It is based on the PC-method and shows a good estimation ability. The experimental results validate the performance of the proposed method.

• Restorative Dentistry and Endodontics
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• v.37 no.1
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• pp.2-8
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• 2012

The limited durability of resin-dentin bonds severely compromises the longevity of composite resin restorations. Resin-dentin bond degradation might occur via degradation of water-rich and resin sparse collagen matrices by host-derived matrix metalloproteinases (MMPs). This review article provides overview of current knowledge of the role of MMPs in dentin matrix degradation and four experimental strategies for extending the longevity of resin-dentin bonds. They include: (1) the use of broadspectrum inhibitors of MMPs, (2) the use of cross-linking agents for silencing the activities of MMPs, (3) ethanol wet-bonding with hydrophobic resin, (4) biomimetic remineralization of water-filled collagen matrix. A combination of these strategies will be able to overcome the limitations in resin-dentin adhesion.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.353-359
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• 1999

We present a matrix-based inference algorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation. 2. Matrix operation: All the subjective knowledge is delineated in a matrix form. so that inference process can proceed based on the matrix operation which is computationally efficient. 3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Journal of Forest and Environmental Science
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• v.30 no.4
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• pp.342-350
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• 2014

Rural people of Sudan are endowed with a deep knowledge concerning the utilization of different tree species. However research on the local knowledge related to tree species utilization still lacks adequate attention. The study objectives were to identify the existing local knowledge related to the utilization of the tree species and the existing threats to the availability of the trees. A total of 300 respondents were selected randomly from Rashad district in Nuba Mountains in 2011. Semi-structured interview, direct observation, group discussion, preference ranking and direct matrix ranking were used to collect the data. The study results revealed that people of Nuba Mountains utilize different tree species for food, medicinal purposes, fodder, firewood, construction and cultural ceremonies. The study results also indicated that the availability of trees is negatively influenced by firewood collection, agricultural expansion, drought, overgrazing and charcoal production. The study concluded that local knowledge has crucial role in tree species utilization in Nuba Mountains. Further researches to document and substantiate the local knowledge on useful tree species are highly recommended.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.17-40
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• 1994

The objectives of this paper are to apply fuzzy cognitive map (FCM)- related techniques to (1) extract causal knowledge from a specific problem-domain and (2) perform a series of causal analysis in complicated decision making area. We propose a set operation-based augmentation (SOBA) algorithm to combine multiple FCMs developed by multiple experts. Based on the SOBA knowledge acquisition algorithm, we can obtain a causal knowledge base fairly representing multiple experts' knowledge about a problem domain. The causal knowledge base built by SOBA algorithm can be described as a matrix form, guaranteeing mathematically compact operation compared with a production (if-then) knowledge base. We applied out method to stock market analysis problem whichis a typical of highly unstructured problems in OR/MS fields.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.353-359
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• 1999

We present a matrix-based inference alorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matric computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

• Mass Spectrometry Letters
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• v.6 no.2
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• pp.27-30
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• 2015

This article reviews the fundamentals of sample preparation used in matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). MALDI is a soft ionization method used to generate analyte ions in their intact forms, which are then detected in MS analysis. MALDI-MS boasts fast analysis times and easy-to-use operation. The disadvantages of MALDI-MS include the occurrence of matrix-associated peaks and inhomogeneous distribution of analyte within the matrix. To overcome the disadvantages of MALDI-MS, various efforts have been directed such as using different matrices, novel matrix systems, various additives, and different sample preparation methods. These various efforts will be discussed in detail. This article will benefit those who would like to obtain basic knowledge of MALDI sample preparation and those who would like to use MALDI-MS in their chemical analyses.