• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1187-1208
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• 2011

This research aims to propose a systematic approach to identify emerging technology-product portfolio for small and medium enterprises (SMEs). Firstly, operational definition of emerging technology for SMEs is presented. Secondly, research framework is suggested and case study to show usefulness of the newly proposed framwork is analyzed. In detail, reference patent set which represent company's capabilities and business area are constructed. The research constructs patent data set for bibliometric analysis using reference patent set and citing patents to 2nd level. Clustering (expert judgement) and keyword based bibliometric approach are used. Then, cluster activity index (AI) and relevance index (RI) comparing with reference patent set are estimated. With emerging technology-product portfolio using AI and RI, a firm can identify emerging technology-product area and monitoring area.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.397-404
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• 2001

본 논문에서는 질의/응답 시스템의 평가를 위해 구축된 평가집합 (Korean Question Answering Test Collection 2.0: KorQATeC2.0)에 대하여 기술한다. KorQATeC2.0은 총 120개의 질의와 207,067개의 문서로 구성되어 있으며, 120개의 질의는 질의에 대한 정답을 제시하는 방식에 따라 기본 과제 질의, 나열 과제 질의, 문맥 과제 질의, 요약 과제 질의로 나누어진다. 또한 KorQATeCl.0과는 달리 여러 문서를 참조하여 정답을 구성하는 질의와 문서집합에 정답이 존재하지 않는 질의를 포함시킴으로써 질의/응답 시스템의 평가를 다양하게 할 수 있도록 하였다. 본 논문에서 기술하는 평가집합은 질의/응답 시스템의 객관적 평가를 가능하게 한다는 점에서 그 의의가 있다.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.264-269
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• 2021

상호참조해결은 주어진 문서에서 상호참조해결의 대상이 될 수 있는 멘션을 추출하고, 같은 개체를 의미하는 멘션 쌍 또는 집합을 찾는 자연어처리 작업이다. 하나의 멘션 내에 멘션이 될 수 있는 다른 단어를 포함하는 중첩 멘션은 순차적 레이블링으로 해결할 수 없는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 멘션의 시작 단어의 위치를 여는 괄호('('), 마지막 위치를 닫는 괄호(')')로 태깅하고 이 괄호들을 예측하는 멘션 탐지 모델과 멘션 탐지 모델에서 예측된 멘션을 바탕으로 포인터 네트워크를 이용하여 같은 개체를 나타내는 멘션을 군집화하는 상호참조해결 모델을 제안한다. 실험 결과, 4개의 영어 대화 데이터셋에서 멘션 탐지 모델은 F1-score (Light) 94.17%, (AMI) 90.86%, (Persuasion) 92.93%, (Switchboard) 91.04%의 성능을 보이고, 상호참조해결 모델에서는 CoNLL F1 (Light) 69.1%, (AMI) 57.6%, (Persuasion) 71.0%, (Switchboard) 65.7%의 성능을 보인다.

• Journal of Korea Port Economic Association
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• v.33 no.4
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• pp.117-134
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• 2017

The purpose of this research is to analyze the efficiency of the Chinese repair shipbuilding industry using a DEA model with 12 Chinese repair shipbuilding companies. Unlike preceding studies, this study has different research subjects as well as selected input and output variables. The research was conducted with competitive Chinese companies in the market. For the efficiency analysis, input variables included the number of technicians as well as facilities, and output variables were diversified with relevant factors using the number of repaired ships and service ranges as well as sales. The differences were analyzed by including only facilities as an input variable for the DEA model, and then both facilities and technicians. For inefficient DMUs, the strengths and weaknesses were analyzed by finding the causes through a reference group, which was developed into an efficient DMU. Moreover, public and private companies were separated to develop improvement measures.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.40-42
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• 2002

데이터 웨어하우스는 물리적으로 여러 사이트에 위치한 분산된 데이터 소스로부터 추출한 온라인 분석 정보를 유지하는 실체 뷰 의 집합으로 구성된다. 따라서 데이터 소스에 변경 사항이 발생하면 데이터 웨어하우스와 일관성을 유지하기 위해 뷰에도 그 변경사항을 반영하는 뷰 관리가 필요하다 동시에 변경되는 여러 데이터 소스와 뷰의 상태 사이에 일관성을 보장하기 위해서는 각 소스의 변경 사항을 순서대로 뷰에 반영해야 한다. 이때 각 소스의 변경 사항을 뷰 정의와 관련된 다른 소스들과 조인을 수행해야 하는 등 뷰 갱신을 위해 많은 비용이 소요된다. 이러한 뷰 갱신 비용을 줄이는 방법중의 하나로 병렬처리 기법을 활용하는 연구가 시도되고 있다. 따라서 이 논문에서는 뷰의 일관성을 보장하기 위해 수행해야 하는 서브질의론 병렬로 처리하는 알고리즘을 제시하였다. 이 방법에서는 서브질의의 조인 연산들을 소스 렐레이션들 간의 참조 무결성 제약 조건을 이용하여 병렬로 처리한다. 질의의 조인 처리를 병렬화 하기 위해 소스 릴레이션간의 참조 무결성 제약조건의 툭송울 이용하여, 여러 릴레이션을 참조하는 릴레이션에서 발생하는 변경 사항에 대해 참조하는 릴레이션의 수만큼 병렬로 조인 연산을 수행하는 알고리즘을 제시하였다. 이렇게 함으로써 여러 소스 릴레이션의 조인으로 구성된 실체 뷰를 갱신하는 시간을 크게 단축하여 효율적으로 뷰를 관리하도록 하였으며, 소스의 증가에 따른 뷰 갱신 시간의 증가를 줄일 수 있도록 하였다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.69-76
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• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.83-90
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• 2000

DEA is a newly developed analyzing tool to measure efficiency evaluation of decision making units (DMU). It compares DMU by radial Projection on the efficient frontier. The purpose of this study is to show reference point approach used for searching solution in multiple objective linear Programming can be usefully used to determine flexible efficient frontier of each DMU In reference point approach, the minimization of ASF Produces an efficient points in frontier and enhances the usefulness of DEA by Providing flexibility in DEA and optimally allocating resources to DMU. Various DEA models can be supported by reference point method by changing the projection direction in order to choose the targets units, standards costs and management benching-marking.

• Journal of Software Assessment and Valuation
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• v.7 no.1
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• pp.29-39
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• 2011

To evaluate the quality of clone detection tools, we should know how many clones the tool misses. Hence we need to have the standard code-clone reference corpus for a carefully chosen set of sample source codes. The reference corpus available so far has been built by manually collecting clones from the results of various existing tools. This paper presents a tree-pattern-based clone detection tool that can be used for automatic generation of reference corpus. Our tool is compared with CloneDR for precision and Bellon's reference corpus for recall. Our tool finds no false positives and 2 to 3 times more clones than CloneDR. Compared to Bellon's reference corpus, our tools shows the 93%-to-100% recall rate and detects far more clones.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.

• School Mathematics
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• v.19 no.4
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• pp.691-712
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• 2017

It is an important to develop teachers' statistical reasoning or thinking by teacher education. In this study, the "comparing two data sets" tasks is focused as a way to develop pre-service elementary teachers' reasoning about core ideas of statistics such as distribution, variability, center, and spread. 6 teams of each 4 pre-service elementary teachers participated on the tasks and their presentations are analyzed based on Pfannkuch's (2006) teachers' inference model in comparing two data sets. As a result, they paid attention to the distribution and variability in the statistical problem solving by the "comparing two data sets" tasks, and used their contextual knowledge to make a statistical decision. In addition, they used some statistics and graphs as the reference for statistical communication, which is expected to provide implications for improving statistical education. The finding implies that the "comparing two data sets" tasks can be used to develop statistical reasoning of pre-service elementary teachers. Some recommendations are suggested for teacher education by these tasks.