• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.166-172
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• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.569-580
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• 2008

Web services are the current most promising technology for service oriented architecture(SOA) implementations. However, in spite of the large scale acceptance of web services, they have been relegated to internal integration projects, and the grand vision of virtual enterprises where partners can be integrated on demand is yet to be realized. The main reason is that the current standards of web services are not very suitable for the dynamic web service discovery and integration. In this paper, we present a novel SemanticBPEL solution that merges the benefit of WS-BPEL, with the advantage of OWL-S for building a semi-automatic web service composition system. In particular, this work proposes a multi-phase search method for solving dynamic discovery and integration problems of web services. The proposed method is compared with the existing keyword based retrieval method. These comparisons show that our approach outperforms the existing method.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.91-104
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• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Journal of Software Engineering Society
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• v.26 no.4
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• pp.93-103
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• 2013

Complexity and maintenance cost of software increase as much as software has been evolved, therefore importance of software maintenance recently arise. There are many signs that are difficulties to maintain software, called bad smell, in a large-scale software. The bad smell should be removed to improve maintainability. Recently, many software refactoring methods have researched to terminate the bad smell. In this paper, we propose how to identify long parameter list, which causes bad smell, and how to solve the problem for increasing software maintainability. In our approach, we classify the parameters for creating new objects by measuring semantic similarity among them. This is evaluated by experienced software developers, and the result is statistically verified.

• Journal of Korea Trade
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• v.24 no.8
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• pp.171-188
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• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.309-319
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• 1999

This study was conducted to get forest scenic beauty management information toward forest working systems; characteristics in Larix forest stands. 35 different alternatives were simulated to visualize on basis of actual forest working methods. The options were illustrated as photos produced by computer software. Each alternatives were evaluated by 3 groups with total 593 respondents after reliability test. Visual preference evaluation was used 1 - 10 point rating scale. The ratings were scaled using SBE analysis program of RMRATE. To compare the relationship of visual preference and image scale, Spatial images of 13 thinning alternatives were measured by Semantic differential scale. In general, the respondents preferred refreshing and ordering forest stand after forest working to natural forest stand before forest working. Visual preference decreased with the increasing intensity of bare ground area and slash area in forest stand. And also, visual preference was high related to tree density, clear length of stem, and ground vegetation. Farm line of small clear cutting area was preferred straight line to curve line. Visual preferences were significant differences in certain socioeconomic variables of the respondents. Results indicate how to conduct forest working system for forest scenic beauty management.

• KIEAE Journal
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• v.10 no.5
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• pp.109-114
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• 2010

There are many business in complex building, and recently noise claims have increased in those buildings. It is one of the most desirable methods reducing the noise level to establish the noise criteria considering the admittable noise level between the source and receiving room, which are to be derived from the subjective response in the real conditions. Because the degree of annoyance and loudness for the intruding noises in complex building could be changed with background noise level, it is suggested to be researched the relationship between the noise levels and subjective response. In this study, the subjective response for three different background levels in receiving business or rooms were investigated for the typical eight business noise source including aerobic music. The results can be summarized as follows. In conditions of low background noise level(40 dB(A)), there is a tendency of intentionally attention situation (listening) in a subjective test. The other hand in conditions of relatively high background noise level(50 dB(A)), there is a unconscious situation (heraing). As the degree of moderately heard or annoyed in SD rating scale means that the noise is easily recognized in neighboring shops, usually the limit of noise criteria were decided on this rating point, so it will be reasonable to be adopted the criteria rating scale as slight heard or annoyed degree (SD criteria 3).

• Journal of Digital Convergence
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• v.17 no.6
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• pp.427-431
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• 2019

The purpose of this study was to investigate whether the mirror environment influences the attitude of the client when counselling. A group of 16 experimenters, 16 controls, a group with a mirror environment, and 32 men and women according to the presence or absence of interventional mirror environments in the control group. There was a statistically significant difference in the 'self - alienation' factor of the Authentic Scale(AS) in the experimental group that provided the mirror environment. There was no significant difference in the Semantic Differential Scale(SDS) between the experimental group and the control group. This study was conducted to investigate whether the presence of the mirror environment affects the client 's authenticity. There were significant differences in some of the factors of the Authentic Scale.