• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.302-305
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• 2000

This paper proposed an optimal register resource allocation algorithm using graph coloring for minimal register at high level synthesis. The proposed algorithm constructed interference graph consist of the intermediated representation CFG to description VHDL. and at interference graph fur the minimal select color selected a position node at stack, the next inserted spill code and the graph coloring process executes for optimal register allocation. The proposed algorithm proves to effect that result compare another allocation techniques through experiments of bench mark.

• School Mathematics
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• v.11 no.1
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• pp.131-145
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• 2009

This research was designed to investigate the possibility to teach function concept and graph representation of functions in explicit manner toward at elementary level. Eight class-hours instruction was given to four Grade 5(age 11) students, and dynamic geometry software GSP was partially used in the class. Results indicate that the students could conceptualize the function relation, interpret linear function graphs, recognize the meaning of their slopes, and discuss the relationships among linear graphs and real life situation. Results also indicate that GSP helped students to recognize the relation between dots and the linear graph clearly and that GSP-line graph did decisive role for children to understand the meaning of graph representation of function.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.37-47
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• 2013

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2178-2198
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• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.428-430
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• 2010

가상현실에서 실제 사람처럼 행동하는 가상거주자는 스스로 주변의 상황을 판단하고 평가를 내리게 된다. 이러한 상황에 대한 판단은 얼마나 정확하고 다양한 자료가 주어졌느냐에 따라서 달라지게 된다. 본 논문에서는 Spatio-temporal graph(ST graph)를 사용하여 시간과 공간에 대한 데이터를 정의하고, ontology의 개념을 더하여 다양한 상황에 대한 표현이 가능하게 하였다. 이 표현 방법으로 가상거주자는 어떠한 상황을 마주하더라도 주변 환경이나 공간에 대한 데이터를 가지고 분석하여 필요한 행동을 할 수 있게 될 것이다.

• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.141-152
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• 1998

Based on the geometric representation an efficient al-gorithm is designed to find all articulation points of a permutation graph. The proposed algorithm takes only O(n log n) time and O(n) space where n represents the number of vertices. The proposed se-quential algorithm can easily be implemented in parallel which takes O(log n) time and O(n) processors on an EREW PRAM. These are the first known algorithms for the problem on this class of graph.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.3-11
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• 2015

This paper aims to review and discuss a graph-based approach for the representation and analysis of building circulation using BIM models. To propose this approach, the authors survey diverse researches and developments which are related to building circulation issues such as circulation requirements in Korea Building Act, spatial network analysis, as well as BIM applications. As the basis of this paper, UCN (Universal Circulation Network) is the main reference of the research, and the major goal of this paper is to extend the coverage of UCN with additional features we examined in the survey. In this paper we restructured two major perspectives on top of UCN: 1) finding major factors of graph-based circulation analysis based on UCN and 2) restructuring the UCN approach and others for adjusting to Korean Building Act. As a result of the further studies in this paper, two major additions have demonstrated in the article: 1) the most remote point-based circulation representation, and 2) virtual space-based circulation analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.123-129
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• 2007

On the Semantic Web, the content of the documents must be explicitly represented through metadata in order to enable contents-based inference. In this study, we propose a mechanism to convert the Conceptual Graph (CG) into Resource Description Framework (RDF). Quite a large number or representation languages for representing knowledge on the Web have been established over the last decade. Most of these researches are focused on design of independent knowledge description. On the Semantic Web, however, a knowledge conversion mechanism will be needed to exchange the knowledge used in independent devices. In this study, the CG could give an entire conceptual view of knowledge and RDF can represent that knowledge on the Semantic Web. Then the CG-based object oriented PROLOG could support the natural inference based on that knowledge. Therefore, our proposed knowledge conversion mechanism will be used in the designing of Semantic Web-based knowledge representation and inference systems.