• Kyungpook Mathematical Journal
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• 제55권4호
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• pp.933-951
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• 2015

In this paper, we consider the problem of convergence of an iterative algorithm for a general system of variational inequalities, a nonexpansive mapping and an ${\eta}$-strictly pseudo-contractive mapping. Strong convergence theorems are established in the framework of real Banach spaces.

• 전자공학회논문지A
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• 제29A권2호
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• pp.88-99
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• 1992

This paper presents an efficient method of mapping Boolean equations to a set of library gates. The proposed system performs technology mapping by graph covering. To select optimal area cover, a new cost function and local area optimization are proposed. Experimental results show that the proposed algorithm produces effective mapping using given library.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제14권4호
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• pp.283-287
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• 2007

An extension of the contraction mapping theorem is provided in a Banach space setting to approximate fixed points of operator equations. Our approach is justified by numerical examples where our results apply whereas the classical contraction mapping principle cannot.

• 충청수학회지
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• 제19권3호
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• pp.263-274
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• 2006

In this paper, we prove the Hyers-Ulam-Rassias stability of the quadratic mapping in generalized quasi-Banach spaces, and of the quadratic mapping in generalized p-Banach spaces.

• 대한원격탐사학회지
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• 제36권5_4호
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• pp.1209-1220
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• 2020

산림지역의 경우 도심지에 비해 무인항공사진측량 작업과정 중 하나인 지상기준점(ground control point, GCP) 측량이 제한적이며, 높은 산림 때문에 비가시권 비행으로 인해 안전문제가 발생한다. 이를 보완하기 위해 드론의 위치를 실시간으로 보정하는 RTK(real time kinematic) 드론과 지형정보를 기반으로 비행하는 3차원 비행 방식이 개발되고 있다. 본 연구는 산림지역에서 재난피해조사를 위한 드론의 활용방안을 제시하기 위해 1) GCP 측량을 통한 드론 맵핑(normal drone mapping), 2) 지형정보를 기반으로 비행하는 드론 맵핑(3D flight drone mapping), 3) RTK 드론을 이용한 드론 맵핑(RTK drone mapping) 3가지 방법을 통해 위치정확도를 평가하였으며, 그 결과 평면 위치오차는 2 cm, 높이오차는 13 cm 이내로 나타났다. 위치정확도 평가 후, 산사태 발생 면적을 계산하여 체적 값을 비교했을 때 세 가지 방법 모두 유사한 결과를 보였다. 본 연구에서는 3D flight drone mapping, RTK drone mapping을 통해 산림지역에서 드론 맵핑이 가지는 한계를 극복하고 재난피해조사의 활용 가능성을 확인하였다. 향후 다양하게 발생하는 재난상황을 감안하였을 때 재난지역의 여건에 따라 3가지 방법을 적절히 활용하면 보다 효과적인 피해조사를 수행할 수 있을 것으로 기대된다.

• 한국정보과학회논문지:시스템및이론
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• 제30권12호
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• pp.708-713
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• 2003

인터넷 서비스에 대한 요청의 기하학적인 증가와 그 요구의 양과 질에 대한 다양성은 이질적인 능력을 지닌 서버들로 구성된 웹 서버 클러스터를 구축하게 되었다. 클러스터 환경에서 가장 결정적인 역할을 하는 요청 대 서버의 맵핑 기법이 최근 활발히 논의되고 있다. 기존의 맵핑 방식은 요청의 수를 기반으로 서버들이 동일한 양의 부하를 할당받는 것을 목적으로 한다. 하지만, 최근의 다양한 서비스의 증가로 인해 단순한 부하의 균등화만으로 적절한 지연 시간을 기대할 수 없게 되었다. 이에 요구되는 내용을 기반으로 맵핑을 수행하여 응답 시간을 단축하고 전체 서버들의 캐시 적중률도 높이는, 내용-기반 맵핑(content-based mapping)이 최근의 인터넷 환경에서 매우 긍정적으로 평가받고 있다. 이에 본 논문에서는 내용-기반 맵핑 방식을 이질적 환경에 적합하도록 개선시킨 우선 순위 최상 임의 가중치 맵핑(Prioritized Highest Random Weight Mapping, PHRW Mapping)을 제안하였다. 요구되는 내용을 기반으로 맵핑하며, 이질적 서버들에 대해 능력에 따라 우선 순위를 부여하여 높은 성능의 서버가 많은 요청들을 처리하는 방식이다. 이를 통해 이질적인 서버들로 구성된 클러스터에서 매우 효과적으로 적용하며, 특히 지연 시간에 제한을 갖고 있는 실시간 데이타 서비스의 지연 시간을 효과적으로 감소시킨다. 알고리즘과 시뮬레이션을 통하여, 제안된 기법을 사용하는 경우 짧은 지연 시간을 보장하여 전체 시스템의 성능이 향상됨을 증명하였다.

• Journal of Computing Science and Engineering
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• 제2권1호
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• pp.44-73
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• 2008

There is general agreement that the problem of data semantics has to be addressed for XML data to become machine-processable. This problem can be tackled by defining a semantic mapping between an XML schema and an ontology. Unfortunately, creating such mappings is a tedious, time-consuming, and error-prone task. To alleviate this problem, we present a solution that heuristically discovers semantic mappings between XML schemas and ontologies. The solution takes as input an initial set of simple correspondences between element attributes in an XML schema and class attributes in an ontology, and then generates a set of mapping formulas. Once such a mapping is created, it is important and necessary to maintain the consistency of the mapping when the associated XML schema and ontology evolve. In this paper, we first offer a mapping formalism to represent semantic mappings. Second, we present our heuristic mapping discovery algorithm. Third, we show through an empirical study that considerable effort can be saved when discovering complex mappings by using our prototype tool. Finally, we propose a mapping maintenance plan dealing with schema evolution. Our study provides a set of effective solutions for building sustainable semantic integration systems for XML data.

• 산업공학
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• 제22권1호
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• pp.17-25
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• 2009

In mobile device's user interface, menu organization is very important as well as menu structure because small display of mobile device. Menu items should be organized based on user knowledge structure to design user-centered interface. Traditionally, MDS (Multidimensional Scaling) have been most often used to expose users' perceived organization of menu items. But, information that MDS reveals is just relative spatial location of concepts and not relevant to concepts connection. Unlike MDS, Trajectory Mapping explicitly finds users' cognitive links between perceived concepts. This study proposes a Trajectory Mapping technique for eliciting knowledge structure, especially a set of cognitive pathways linking menu items, from end user. With twelve participants, MDS and Trajectory Mapping were conducted using cellular phone's menu items. And user knowledge structure was analyzed through Visual Concept Map that combination of results of MDS and Trajectory Mapping. After then, menu items were organized according to users' perceived organization. Empirical usability test was also conducted. The results of usability test showed that usability, in terms of task performance time, number of errors, and satisfaction, for newly organized interface was significantly improved compare to original interface. The methodology of this study is expected to be applicable to design a user-centered interface. In other words, Trajectory Mapping technique can be used as a design tool of user interface for imposing user knowledge structure on the interface.

• 한국언어정보학회지:언어와정보
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• 제15권2호
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• pp.23-45
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• 2011

This paper proposes a statistical-based linguistic methodology for automatic mapping between large-scale heterogeneous languages resources for NLP applications in general. As a particular case, it treats automatic mapping between two large-scale heterogeneous Korean language resources: Sejong Semantic Classes (SJSC) in the Sejong Electronic Dictionary (SJD) and nouns in KorLex. KorLex is a large-scale Korean WordNet, but it lacks syntactic information. SJD contains refined semantic-syntactic information, with semantic labels depending on SJSC, but the list of its entry words is much smaller than that of KorLex. The goal of our study is to build a rich language resource by integrating useful information within SJD into KorLex. In this paper, we use both linguistic and statistical methods for constructing an automatic mapping methodology. The linguistic aspect of the methodology focuses on the following three linguistic clues: monosemy/polysemy of word forms, instances (example words), and semantically related words. The statistical aspect of the methodology uses the three statistical formulae ${\chi}^2$, Mutual Information and Information Gain to obtain candidate synsets. Compared with the performance of manual mapping, the automatic mapping based on our proposed statistical linguistic methods shows good performance rates in terms of correctness, specifically giving recall 0.838, precision 0.718, and F1 0.774.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.1-30
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• 2013

The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge.