• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.204-213
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• 2016

A differential game theory based approach is used to develop an automated maneuver generation algorithm for Within Visual Range (WVR) air-to-air combat of unmanned combat aerial vehicles (UCAVs). The algorithm follows hierarchical decisionmaking structure and performs scoring function matrix calculation based on differential game theory to find the optimal maneuvers against dynamic and challenging combat situation. The score, implying how much air superiority the UCAV has, is computed from the predicted relative geometry, relative distance and velocity of two aircrafts. Security strategy is applied at the decision-making step. Additionally, a barrier function is implemented to keep the airplanes above the altitude lower bound. To shorten the simulation time to make the algorithm more real-time, a moving horizon method is implemented. An F-16 pseudo 6-DOF model is used for realistic simulation. The combat maneuver generation algorithm is verified through three dimensional simulations.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• Journal of the Korean Society for information Management
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• v.19 no.4
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• pp.349-381
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• 2002

The 21st century survive only who creating, taking, and managing to knowledge. Digital contents industry becomes to the core in the future. That is, to win in a competition have to the digitalizing knowledge contents to all knowledge and information resources. Therefore, it needs to the digital contents management and the distribution framework. This study aims to propose a model stages in the knowledge contents-decision process, KM Matrix, and KMI Score founded on digital contents framework and diffusion of innovation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.221-232
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• 2023

In this paper, we propose a recommendation system based on the latent factor model using matrix factorization, which is one of the most commonly used collaborative filtering algorithms for recommendation systems. In particular, by introducing the concept of creating a list of recommended content and a list of non-preferred recommended content, and removing the non-preferred recommended content from the list of recommended content, we propose a method to ultimately increase the satisfaction. The experiment confirmed that using a separate list of non-preferred content to find non-preferred content increased precision by 135%, accuracy by 149%, and F1 score by 72% compared to using the existing recommendation list. In addition, assuming that users do not view non-preferred content through the proposed algorithm, the average evaluation score of a specific user used in the experiment increased by about 35%, from 2.55 to 3.44, thereby increasing user satisfaction. It has been confirmed that this algorithm is more effective than the algorithms used in existing recommendation systems.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6609-6613
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• 2015

Background: Matrix metalloproteinases (MMPs) are a family of zinc metalloproteinases capable of degrading components of connective tissues. MMP-10 is frequently expressed in human cancers. The aim of this study was to immunohistochemically evaluate its expression in oral squamous cell carcinoma (OSCC) and verrucous carcinoma (OVC). Materials and Methods: A retrospective analysis of 73 samples (31 OSCC, 22 OVC and 20 non-neoplastic epithelium) was performed. All samples were immunohistochemically stained with monoclonal MMP-10 antibody and expression levels and staining intensity were evaluated with respect to microscopic features. Data were analyzed by SPSS (V.21), Mann-Whitney and Kruskal Wallis tests. Results: MMP-10 was detected in all OSCC and OVC cases. The expression of MMP-10 in OSCC was intensive (score 3) and in OVC was low and moderate (score 1 and score 2) more frequently. Non- neoplastic epithelium did not show MMP-10 expression. Differences between groups was statistically significant (p<0.05). However, the expression of MMP-10 was not obviously different between various grades of OSCC. Conclusions: According to our study, MMP-10 protein can be important possible factor in the transformation of normal oral epithelium to OVC and OSCC, also the level of MMP-10 expression at invasion front of the lesions can be helpful in the differentiation of OVC and OSCC.

• Journal of Nutrition and Health
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• v.46 no.1
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• pp.26-33
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• 2013

The purpose of this study was to investigate the sodium intake of office workers using 24-hour urine analysis and to analyze the correlation matrix between variables. The sodium intake of the subjects (n = 137), based on a 24-hr sodium excretion period, was male (n = 56) 6072.4 mg and female (n = 81) 5,168.2 mg. Urinary sodium excretion showed significant positive correlation with BMI, frequency of eating out, expenditure of eating out, salty taste assessment and high-salt dietary behavior. Analysis of urinary sodium excretion showed significant positive correlation with intake frequencies of cabbage kimchi, broiled fish, feast noodle and rice with leaf wraps. Based on the results of multiple regression, urinary sodium excretion was found to be related to intake frequencies of cabbage kimchi, broiled fish, rice with leaf wraps and high score of high-salt dietary behavior.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.20-25
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• 2015

Interface management problems inherent in construction projects hamper their successful delivery. Therefore, this study aimed at determining the most important project interfaces in construction works in Nigeria in terms of most significant potential impacts, so that management attention are objectively focused on potential highest impacting project interfaces. From a review of literature, 28 project interfaces management issues were identified and categorized. Structured questionnaires were used to collect data concerning the impact (estimated losses to the project in terms of cost) and probability of occurrence of the identified interfaces. The interfaces were ranked using their computed Matrix Scores (MS). The results reveal that "project-workers interfaces problem manifested in use of inappropriate mixes" is the highest impacting. A ranking of the interface categories also reveal that the interfaces at the execution phase of a project (MS = 1226.79) are those that could result in the highest losses to the project.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.357-366
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• 2001

The present paper develops a method to check the propriety of link functions for binary data. In order to parameterize a certain type of goodness of the link, a family of link functions indexed by a shape parameter is proposed. I first investigate the maximum likelihood estimation of the shape parameter as well as regression parameters and then derive their large sample behaviors of the estimators. A score test is considered to evaluate the goodness of the current link function. For illustration, I employ two families of power transformations, the modulus transformation by John and Draper (1980) and the extended power transformation by Yeo and Johnson (2000), which are appropriate to detect symmetric and asymmetric inadequacy of the selected link function. respectively.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.