• Journal of Educational Research in Mathematics
• /
• v.10 no.1
• /
• pp.73-84
• /
• 2000

This study, after careful consideration on Piaget's structuralism, showed the relationship between Bourbaki's matrix structure of mathematics and Piaget's structure of mathematical thinking. This, studying the basic characters that structure of knowledge should have, pointed out that 'transformation' and to it, too. Also it revealed that group structure is a 'development' are essential typical one which has very important characters not only of mathematical structure but also general structure, and discussed the problem that learners construct the group structure as a mathematical concept.

• Journal of Korean Clinical Nursing Research
• /
• v.23 no.2
• /
• pp.189-201
• /
• 2017

Purpose: The aim of this study was to develop the nursing management core task and competency matrix for hospital unit managers. The perceived level of importance and performance of identified core competencies by unit managers were also investigated. Methods: Literature review and expert survey identified nursing management core task and competencies. Subsequently, the core task and competency matrix was developed and validated by expert panel. A survey of 196 nurse managers from 3 cities identified perceived importance and performance of core competiences. Results: Thirty-eight nursing management core task and thirty-seven nursing management core competencies were identified comprising five categories; Clinical practice knowledge, Evidence-based practice, Employee development, Strategic planning and Initiative. Based on the core task and competencies, the task and competency matrix for unit managers was developed. In the analysis of importance and performance of core competencies, the mean score of importance ($3.50{\pm}0.30$) was higher than the mean score of performance ($3.03{\pm}0.34$). Conclusion: The development of core task and competencies for unit managers in hospitals provides a guide for the development and evaluation of programs designed to increase competence of unit managers.

• Restorative Dentistry and Endodontics
• /
• v.45 no.3
• /
• pp.31.1-31.20
• /
• 2020

Matrix metalloproteinases (MMPs) are enzymes that can degrade collagen in hybrid layer and reduce the longevity of adhesive restorations. As scientific understanding of the MMPs has advanced, useful strategies focusing on preventing these enzymes' actions by MMP inhibitors have quickly developed in many medical fields. However, in restorative dentistry, it is still not well established. This paper is an overview of the strategies to inhibit MMPs that can achieve a long-lasting material-tooth adhesion. Literature search was performed comprehensively using the electronic databases: PubMed, ScienceDirect and Scopus including articles from May 2007 to December 2019 and the main search terms were "matrix metalloproteinases", "collagen", and "dentin" and "hybrid layer". MMPs typical structure consists of several distinct domains. MMP inhibitors can be divided into 2 main groups: synthetic (synthetic-peptides, non-peptide molecules and compounds, tetracyclines, metallic ions, and others) and natural bioactive inhibitors mainly flavonoids. Selective inhibitors of MMPs promise to be the future for specific targeting of preventing dentin proteolysis. The knowledge about MMPs functionality should be considered to synthesize drugs capable to efficiently and selectively block MMPs chemical routes targeting their inactivation in order to overcome the current limitations of the therapeutic use of MMPs inhibitors, i.e., easy clinical application and long-lasting effect.

• Journal of Internet Computing and Services
• /
• v.14 no.2
• /
• pp.25-34
• /
• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• Journal of the Korean Society for Heat Treatment
• /
• v.21 no.5
• /
• pp.244-250
• /
• 2008

This study has investigated the surface phase change, hardness variation, surface precipitates, nitrogen content and corrosion resistance in STS 431 (17Cr-2Ni-0.2C-0.01Nb) martensitic stainless steel after high temperature gas nitriding (HTGN) treatment at the temperature range between $1050^{\circ}C$ and $1150^{\circ}C$. The HTGN-treated surface layer appeared $Cr_2N$ of rod type, carbo-nitride of round type and fine precipitates in the austenite matrix. On the other hand the interior region where the nitrogen was not permeated, exhibited martensite phase. The surface hardness showed 250~590 HV, depending on the HTGN treatment conditions, while the interior martensitic phase represented 520 HV. The permeation depth of nitrogen increased with increasing the HTGN-treated temperature. The nitrogen concentration of the surface layer appeared approximately ~0.17% at $1100^{\circ}C$. On comparing the corrosion resistance between solution-annealed and HTGN-treated steels, the corrosion resistance of HTGN-treated steel was superior to that of solution-annealed specimens.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.8
• /
• pp.697-705
• /
• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.

• Information Systems Review
• /
• v.21 no.4
• /
• pp.1-29
• /
• 2019

The knowledge resources take a role in promoting the sustainable growth of organization. Therefore, it is important for the members of organization to acquire knowledge consistently so that the company can continue to grow. Knowledge service is the field that provides information and infrastructure which enable the members of organization to acquire new knowledge. As we recognized the importance of knowledge services, we analyzed the level of knowledge service management and development through the impact of knowledge quality on user capabilities. First, the matrix of knowledge patterns was presented based on the type of information and the level of customer interaction. According to patterns, the knowledge service was classified into three types of information providing, information analysis, and infrastructure, and then the results of structural model analysis were presented for each type. It found that the impact of knowledge service quality on user competence was different according to the type of service. The results suggested new indicators for measuring the performance of knowledge services, and provided information for reconstructing services based on the user considering the integrated operation of knowledge service and organizational designing knowledge service.

• Journal of the Korean Society of Safety
• /
• v.32 no.1
• /
• pp.98-107
• /
• 2017

This study is aimed to design for qualification structure and develop the subjects for examination of the school safety manager qualifications. For the study purpose, job analysis and meetings with experts were performed for extract contents such as task range, criteria of examination, subjects of examination and examination methods, etc. The first step, duties and tasks of the school safety manager were figured out through revised job analysis data of the school safety manager developed before. On the second step, job model was established and developed job specification including importance, difficulty and frequency of each task. On the third step, task specification was developed, and Knowledge-Skill Matrix was the most important thing on examination were completed. The fourth step was the selection of examination subject using task-subject matrix and 6 subjects such as The Principles of Safety, Understanding of the School Safety Manual, Law Related on the School Safety, Introduction to Education, Understanding and Dealing with Type of the School Safety, Establishment of the School Safety Plan were derived from previous procedure. The fifth step was development of guidelines for design examination of each subject. The last step was development for skills education program.

• ETRI Journal
• /
• v.39 no.4
• /
• pp.592-604
• /
• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2806-2825
• /
• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.