• Journal of the military operations research society of Korea
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• v.32 no.1
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• pp.51-72
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• 2006

There are many occasions that the performance of a corps is influenced not only by its own efforts but by the commander of the next higher unit in a vertical organizational structure. When the direction of the commander in the next higher organization is different from that of the actual evaluation agency, the unit under evaluation may get rated lower than what it should deserve. This study suggests an alternative method to evaluate the performance of military units in the situation that there exist critical environmental factors which affect the performance. This method employes DEA, a non parametric method, and Constrained Canonical Correlation Analysis(CCCA), a parametric method which is used to estimate a efficient frontier with multiple dependent variables and constraints. This article also exploits a set of categorical environmental variables in the CCCA to improve the fairness of performance evaluation. It is shown that the introduction of the categorical variables helps evaluating the true performance of individual units such as battalions subordinated to different next higher commanders.

• Journal of the Economic Geographical Society of Korea
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• v.5 no.2
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• pp.187-207
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• 2002

This study investigates the wage determination process of regional labor markets in order to understand the regional dimension of labor market processes in Korean metropolitan cities. Since the financial crisis in late 1997, the interplay between labor market restructuring such as unemployment and skill polarization and income disparity has been shaped by the labor market process in the metropolitan cities. This is also closely related to the fact that both industrial restructuring and expanding information technologies in the metropolitan region have reshaped the labor demand structure and finally resulted in structural unemployment due to skill mismatch and spatial mismatch and wage inequality across different occupations. In addition, since wage determination process clearly has a regional dimension, wage determination and its influence on income profile in a certain regional labor market need to be understood by investigating its labor market characteristics including labor supply and demand structure, industrial changes, changing unemployment, etc. This is why labor market policy as a regional policy needs to be redefined and it can be much enhanced by geographical investigation on regional labor market.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• The Journal of Information Technology
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• v.3 no.3
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• pp.1-9
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• 2000

In this paper, we propose the design and implementation of Multi-media Title for the similar figure learning. The algebra problems of mathematics are usually solved by algorithm, but the diagram problems of geometry have various solution methods. This made us educate the students by memorizing the principle and property of diagram. In this paper, we applied the courseware to the students and analyzed the result. Therefore we could notice the possibilities, "f we teach students by using the courseware, we can improve their learning achievement."

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1657-1658
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• 2013

본 연구에서는 기계 시스템의 위험도 진단 및 관리를 위해, 3차원 기반의 진단결과 가시화 기술에 관한 연구를 소개한다. 계층구조와 인과관계에 의한 신뢰성 분석 기술을 3차원 디지털 목업에 적용하여, 요소 부품 및 전체 시스템의 위험도를 직관적으로 파악하고, 이를 유지 보수 과정에 활용할 수 있는 소프트웨어 플랫폼의 설계 및 풍력발전 시스템 적용을 위한 디지털 목업 개발 등을 소개한다.

• Journal of Navigation and Port Research
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• v.35 no.8
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• pp.667-675
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• 2011

As the annual growth rate of port handling container volume in Korea has faced slowdown, this paper attempts to find strategies for the domestic container terminal operators in order to enhance competitiveness. For this, the model of using AHP and the fuzzy set theory is used for evaluating competitiveness between domestic and global container terminal operators. The evaluation model hierarchy is developed based on SERM(subject-environment-resource-mechanism) theory of business strategy. The results show domestic operators are ranked lower than global player so that they particularly pay attention to the areas of scale of economy, business diversification, and globalization strategy.

• Journal of the Korea Society for Simulation
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• v.24 no.3
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• pp.77-87
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• 2015

Defense M&S (Modeling & Simulation) is utilized as a realistic method to analyze MOE (Measure of Effectiveness) of weapon systems by modeling weapons and their operational environment on the computer, and simulating them under various war scenarios. As weapon systems become complex in their structure and dynamics, model engineering are experiencing difficulties to construct simulation models on a computer. A model repository helps model developers to save model development time and cost by systematically storing predefined and already validated models. However, most repositories for Defense M&Shave not been successful partly due to limited accessability, vulnerability to security threats, and low level of dependability. In this paper, we propose W-Cloud (Weapon Cloud), a cloud model repository for reusing predefined weapon models. Clients can access W-Cloud on any platforms and various devices, yet security and confidentiality concerns are guaranteed by employing multi-tier information protection mechanism.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.89-96
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• 2007

Various classification methods have been developed to reuse components. These classification methods enable the user to access the needed components quickly and easily. Conventional classification approaches include the following problems: a labor-intensive domain analysis effort to build a classification structure, the representation of the inter-component relationships, difficult to maintain as the domain evolves, and applied to a limited domain. In order to solve these problems, this paper describes a composite cluster analysis approach for component classification. The cluster analysis approach is a combination of a hierarchical cluster analysis method, which generates a stable clustering structure automatically, and a non-hierarchical cluster analysis concept, which classifies new components automatically. The clustering information generated from the proposed approach can support the domain analysis process.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.209-212
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• 2017

고도화된 머신러닝과 딥러닝 기술은 영상처리, 자연어처리 등의 분야에서 많은 문제를 해결하고 있다. 특히 사용자가 입력한 문장을 분석하고 그에 따른 문장을 생성하는 자연어처리 기술은 기계 번역, 자동 요약, 자동 오류 수정 등에 널리 이용되고 있다. 딥러닝 기반의 자연어처리 기술은 학습을 위해 여러 계층의 신경망을 구성하여 단어 간 의존 관계와 문장 구조를 학습한다. 그러나 학습 과정에서의 계산양이 방대하여 모델을 구성하는데 시간과 비용이 많이 필요하다. 그러나 Word2Vec 모델은 신경망과 유사하게 학습하면서도 선형 구조를 가지고 있어 딥러닝 기반 자연어처리 기술에 비해 적은 시간 복잡도로 고차원의 단어 벡터를 계산할 수 있다. 따라서 본 논문에서는 Word2Vec 모델을 활용하여 한국어 문장을 생성하는 방법을 제시하였다. 본 논문에서는 지정된 문장 템플릿에 유사도가 높은 각 단어들을 적용하여 문장을 구성하는 Word2Vec 모델을 설계하였고, 서로 다른 학습 데이터로부터 생성된 문장을 평가하고 제안한 모델의 활용 방안을 제시하였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.209-212
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• 2017

고도화된 머신러닝과 딥러닝 기술은 영상처리, 자연어처리 등의 분야에서 많은 문제를 해결하고 있다. 특히 사용자가 입력한 문장을 분석하고 그에 따른 문장을 생성하는 자연어처리 기술은 기계 번역, 자동 요약, 자동 오류 수정 등에 널리 이용되고 있다. 딥러닝 기반의 자연어처리 기술은 학습을 위해 여러 계층의 신경망을 구성하여 단어 간 의존 관계와 문장 구조를 학습한다. 그러나 학습 과정에서의 계산양이 방대하여 모델을 구성하는데 시간과 비용이 많이 필요하다. 그러나 Word2Vec 모델은 신경망과 유사하게 학습하면서도 선형 구조를 가지고 있어 딥러닝 기반 자연어처리 기술에 비해 적은 시간 복잡도로 고차원의 단어 벡터를 계산할 수 있다. 따라서 본 논문에서는 Word2Vec 모델을 활용하여 한국어 문장을 생성하는 방법을 제시하였다. 본 논문에서는 지정된 문장 템플릿에 유사도가 높은 각 단어들을 적용하여 문장을 구성하는 Word2Vec 모델을 설계하였고, 서로 다른 학습 데이터로부터 생성된 문장을 평가하고 제안한 모델의 활용 방안을 제시하였다.