• Korean Journal of Environment and Ecology
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• v.16 no.4
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• pp.433-440
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• 2003

A vegetation structure by attitudinal changes from Jeongryongchi to Gogiri section of Baekdudaegan were investigated by sample plots(nine 500$m^2$). Using TWINSPAN and DCA techniques, vegetation structure was analyzed. In the results from the analysis of both techniques, Quercus mongolica is dominant species and attitudinal zonations were divided into 2 groups such as above 900m area and lower one by subspecies in subtree and shrub layers. Similarity index analyses of elevational ranges showed discontinuities between above and lower elevation area. In the analysis of species diversity, there was no significant difference due to altitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• Journal of Plant Biotechnology
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• v.49 no.1
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• pp.15-29
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• 2022

Cyclophilins (CYPs) are highly conserved ubiquitous proteins belong to the peptidyl prolyl cis/trans isomerase (PPIase) superfamily. These proteins are present in a wide range of organisms; they contain a highly conserved peptidyl-prolyl cis/trans isomerase domain. A comprehensive database survey identified a total of 35 genes localized in all cellular compartments of Solanum lycopersicum L., but largely in the cytosol. Sequence alignment and conserved motif analyses of the SlCYP proteins revealed a highly conserved CLD motif. Evolutionary analysis predicted the clustering of a large number of gene pairs with high sequence similarity. Expression analysis using the RNA-Seq data showed that the majority of the SlCYP genes were highly expressed in mature leaves and blooming flowers, compared with their expression in other organs. This study provides a basis for the functional characterization of individual CYP genes in the future to elucidate their role(s) in protein refolding and long-distance signaling in tomatoes and in plant biology, in general.

• Journal of Science Education
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• v.42 no.2
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• pp.256-271
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• 2018

The study aimed to investigate how the science gifted connect and integrate science concepts in the process of problem finding. Research subject was sampled from 228 applicants for a science gifted education center affiliated with a university in 2015. A creative problem solving test (CPST) in science, which administered as an admission process, was utilized as a reference to sample two groups. Sixty-seven students from top 30% in test scores were selected for the upper group and 64 students from bottom 30% in test scores were selected for the lower group. The CPST, which was developed by researchers, included one item about how to connect two science concepts among eight science concepts, sound, electricity, weight, temperature, respiration, photosynthesis, weather, and earthquake extracted from elementary science curriculum. As results, there were differences in choosing two concepts among four science major areas. The ways of connecting science concepts were characterized by three categories, relation-based, similarity-based, and dissimilarity-based. In addition, relation-based was characterized by attributes, means, influences, predictions, and causes; similarity-based was by attributes, objects, scientific principles, and phenomena, and dissimilarity-based was by parallel, resource, and deletion. There were significant (p<.000) differences in ways of connecting science concepts between the upper and the lower groups. The upper group students preferred connecting science concepts of inter-science subjects while the lower group students preferred connecting science concepts of intra-science subject. The upper group students showed a tendency to connect the science concepts based on similarity. In contrast, the lower group students frequently showed ways of connecting the science concepts based on dissimilarity. In particular, they simply parallelled science concepts.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.562-571
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• 2008

For the purpose of building domain ontology, this paper proposes a methodology for building core ontology first, and then enriching the core ontology with the concepts and relations in the domain thesaurus. First, the top-level concept taxonomy of the core ontology is built using domain dictionary and general domain thesaurus. Then, the concepts of the domain thesaurus are classified into top-level concepts in the core ontology, and relations between broader terms (BT) - narrower terms (NT) and related terms (RT) are classified into semantic relations defined for the core ontology. To classify concepts, a two-step approach is adopted, in which a frequency-based approach is complemented with a similarity-based approach. To classify relations, two techniques are applied: (i) for the case of insufficient training data, a rule-based module is for identifying isa relation out of non-isa ones; a pattern-based approach is for classifying non-taxonomic semantic relations from non-isa. (ii) For the case of sufficient training data, a maximum-entropy model is adopted in the feature-based classification, where k-NN approach is for noisy filtering of training data. A series of experiments show that performances of the proposed systems are quite promising and comparable to judgments by human experts.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.35-51
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• 2012

Cloud computing is internet-based computing where computing resources are offered over the Internet as scalable and on-demand services. In particular, in case a number of various cloud services emerge in accordance with development of internet and mobile technology, to select and provide services with which service users satisfy is one of the important issues. Most of previous works show the limitation in the degree of user satisfaction because they are based on so called concept similarity in relation to user requirements or are lack of versatility of user preferences. This paper presents cloud service selection reasoning which can be applied to the general cloud service environments including a variety of computing resource services, not limited to web services. In relation to the service environments, there are two kinds of services: atomic service and composite service. An atomic service consists of service attributes which represent the characteristics of service such as functionality, performance, or specification. A composite service can be created by composition of atomic services and other composite services. Therefore, a composite service inherits attributes of component services. On the other hand, the main participants in providing with cloud services are service users, service suppliers, and service operators. Service suppliers can register services autonomously or in accordance with the strategic collaboration with service operators. Service users submit request queries including service name and requirements to the service management system. The service management system consists of a query processor for processing user queries, a registration manager for service registration, and a selection engine for service selection reasoning. In order to enhance the degree of user satisfaction, our reasoning stands on basis of the degree of conformance to user requirements of service attributes in terms of functionality, performance, and specification of service attributes, instead of concept similarity as in ontology-based reasoning. For this we introduce so called a service attribute graph (SAG) which is generated by considering the inclusion relationship among instances of a service attribute from several perspectives like functionality, performance, and specification. Hence, SAG is a directed graph which shows the inclusion relationships among attribute instances. Since the degree of conformance is very close to the inclusion relationship, we can say the acceptability of services depends on the closeness of inclusion relationship among corresponding attribute instances. That is, the high closeness implies the high acceptability because the degree of closeness reflects the degree of conformance among attributes instances. The degree of closeness is proportional to the path length between two vertex in SAG. The shorter path length means more close inclusion relationship than longer path length, which implies the higher degree of conformance. In addition to acceptability, in this paper, other user preferences such as priority for attributes and mandatary options are reflected for the variety of user requirements. Furthermore, to consider various types of attribute like character, number, and boolean also helps to support the variety of user requirements. Finally, according to service value to price cloud services are rated and recommended to users. One of the significances of this paper is the first try to present a graph-based selection reasoning unlike other works, while considering various user preferences in relation with service attributes.

• Journal of Korea Water Resources Association
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• v.44 no.10
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• pp.831-841
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• 2011

In this study, with the assumption that the geographical characteristics of the river basin have selfsimilarity, fractal dimensions are used to quantify the complexity of the terrain. For this, Area exponent and hurst exponent was applied to estimate the fractal dimension by using spatial analysis. The result shows that the value of area exponent and hurst exponent calculated by the fractal dimension are 2.008~2.074 and 2.132~2.268 respectively. Also the $R^2$ of area exponent and hurst exponent are 94.9% and 87.1% respectively too. It shows that the $R^2$ is relatively high. After analyzing the spatial self-similarity parameter, it is shown that traditional urban area's moderate slope geographical characteristic closed to 2D fractal in Ara water way. In addition, the relation between fractal dimension and geographical elements are identified. With these results, fractal dimension is the representative value of basin characteristics.

• Korean Journal of Ichthyology
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• v.30 no.1
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• pp.36-45
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• 2018

The temporal and depth-related variations in the species composition and abundance of demersal fish assemblage were studied in the coastal waters off Hupo, East Sea. Fish samples were collected seasonally between 2011 and 2017 at two stations of study area using trammel net and bottom gill net. In total, 46 fish species belonging to 17 families were collected during study period, with 36 and 22 species occurring in depths of ~80 m (site A) and ~140 m (site B), respectively. Glyptocephalus stelleri, Cleisthenes pinetorum and Gymnocanthus herzensteini were abundant at shallower site, and Dasycottus setiger at deeper site. The number of species, abundance, biomass and diversity fluctuated with water depth, but not temporally (both seasonally and annually). Analysis of similarity (ANOSIM) revealed that the fish assemblage structures were significantly different with water depth, but not by year or season. Non-metric multidimensional scaling (MDS) ordination plot emphasized visually in spatial difference of fish assemblages, and it was due to differential contributions of dominant species in relation to water depth and temperature.