• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1386-1399
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• 2004

This paper presents correlation-based automatic image captioning. Given a training set of annotated images, we want to discover correlations between visual features and textual features, so that we can automatically generate descriptive textual features for a new unseen image. We develop models with multiple design alternatives such as 1) adaptively clustering visual features, 2) weighting visual features and textual features, and 3) reducing dimensionality for noise sup-Pression. We experiment thoroughly on 10 data sets of various content styles from the Corel image database, about 680MB. The major contributions of this work are: (a) we show that careful weighting visual and textual features, as well as clustering visual features adaptively leads to consistent performance improvements, and (b) our proposed methods achieve a relative improvement of up to 45% on annotation accuracy over the state-of-the-art, EM approach.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.519-529
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• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.737-744
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• 2005

In this paper, we propose a three-level color clustering algerian for the binarization of text regions extracted from natural scene images. The proposed algorithm consists of three phases of color segmentation. First, the ordinary images in which the texts are well separated from the background, are binarized. Then, in the second phase, the input image is passed through a high pass filter to deal with those affected by natural or artificial light. Finally, the image Is passed through a low pass filter to deal with the texture in texts and/or background. We have shown that the proposed algorithm is more effective used gray-information binarization algorithm. To evaluate the effectiveness of the proposed algorithm we use a commercial OCR software ARMI 6.0 to observe the recognition accuracies on the binarized images. The experimental results on word and character recognition show that the proposed approach is more accurate than conventional methods by over $35\%$.

• Korean journal of applied entomology
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• v.60 no.1
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• pp.105-114
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• 2021

Ground-dwelling arthropods are important components in apple orchard providing beneficial ecological services of predation and decomposition as well as herbivory. Groundcovers are managed differentially in organic and conventional apple orchards influencing ground-dwelling arthropod assemblages. We conducted 3-year studies to assess the effects of orchard management relative to weed management on the abundance and diversity of ground-dwelling arthropods using pitfall trapping. Most arthropods were classified as higher taxonomical groups and functional feeding guilds, while carabid beetles were classified into species level. Coleoptera was the dominating taxon of all ground-dwelling arthropods. Abundance of herbivores and predators was significantly higher in organic apple orchards than conventional ones. Higher abundance and diversity of carabid beetles were found in organic orchards than in conventional orchards. The abundance of Araneae, Coleoptera, or carabid beetles was negatively correlated to weed management frequency. It was found that ground-dwelling arthropods were more influenced by weed management practices than the farming systems.

• The Korean Journal of Malacology
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• v.29 no.1
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• pp.51-63
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• 2013

To assess the effect of environmental factors on the sustainability of cultured production shellfish, we investigated the habitat characteristics of tidal flat (Namhae-po in Taean). We measured the physiochemical parameters (temperature, salanity, pH, dissolved oxygen and nutrients) and the geochemical characteristics (chemical oxygen demand, ignition loss, C/N ratio and C/S ratio). Surface sediments were collected from several site of tidal flat to examine the geochemical characteristics of both the benthic environment and heavy metal pollution. The grain size for research area of tidal flat were similar at the ratio of silt and clay in comparison with the other site of it. The C/N ratio was more than 5.0, reflecting the range arising from the mix of marine organism and organic matter. The C/S ratio (about 2.8) showed that survey area had anoxic or sub-anoxic bottom conditions. The enrichment factor (Ef) and index of accumulation rate (Igeo) of the metals showed that those research areas can be classified as heavily polluted, heavily to moderately polluted, or more or less unpolluted, respectively. Adult surf clam (Mactra veneriformis) density was highest at St. 2 (middle part of the Namhae-po), on the other hand, surf clam spat density was highest at St. 3 (lower part of the Namhae-po). Heavy rain, terrigenous suspended clay with fresh water from neighboring agricultural land, and severe high air temperature during summer could be thought as detrimental causes of spat and adult mortality in Namhae-po tidal flat. We suggested that the growth of shellfish in the tidal flat was effected by the various environmental conditions, so an improvement in the cultured method was needed.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.11
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• pp.1161-1169
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• 2008

The removals of TCE and PCE vapor with or without a supply of toluene as a primary substrate were compared in a biofiltration process, and the variations of microbial communities associated with the removal were also investigated. As a result of investigations on the removals of TCE/PCE in a biofilter B within which TCE/PCE-acclimated sludge was attached on the surface of media without a supply of primary substrate, and those in another biofilter A where toluene-acclimated sludge was attached with a supply of toluene as a primary substrate, followings were found: (i) parts of microbes responsible to the decomposition of toluene vapor participate in the removal of chlorinated VOCs such as TCE and PCE, and (ii) effective biological removals of TCE and PCE vapor do not necessarily need cometabolism. Sequencing of 16S rDNA obtained from the band profile of DGGE (Denaturating Gradient Gel Electrophoresis), it was confirmed that: (i) uncultured alpha proteobacterium, uncultured Desulfitobacterium, uncultured Rhodobacteraceae bacterium, Cupriavidus necator, and Pseudomonas putida were found to be toluene-decomposing microbes, (ii) alpha proteobacterium HTCC396 is a TCE-removing microbe, (iii) Desulfitobacterium sp. is a PCE-decomposing microbe, and (iv) particularly, uncultured Desulfitobacterium sp. is probably a microbe decomposable not only toluene but also various chlorinated VOC vapor including TCE and PCE.

• Journal of Wetlands Research
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• v.20 no.2
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• pp.124-130
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• 2018

This study was conducted to identify the characteristics of bird population dynamics using unmanned monitoring system in rice paddy. We compared the similarity of population dynamics of birds between unmanned monitoring system and field survey, and evaluated the spatial and temporal patterns of population dynamics of birds using the unmanned monitoring system. The monitoring using the system was conducted from January 1, 2014 to December 31, 2016 in Cheolwon, Dangjin, Buan, and Heanam. The images from the system were obtained at 10-min intervals from 6:00 to 20:00. The field survey was conducted once a month in Dangjin from January to December 2016. Total 91,980 images were obtained from the unmanned monitoring system. We extracted the number of individuals for herons, shorebirds, and waterfowl from the images. The population dynamics of waterbirds using the unmanned monitoring system were similar to that in field survey. Especially, population dynamics of herons was more similar than other waterbirds. It was identified that the population dynamics of herons using the unmanned monitoring system was different among the Cheolwon, Dangjin, Buan, and Heanam. Furthermore, the unmanned monitoring system was available on various time scale such as month, day, and minute. It is expected that long-term data storage using the unmanned monitoring system can be used to identify in detail and forecast the population dynamics of birds in rice paddy.

• Korean Journal of Ecology and Environment
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• v.39 no.2 s.116
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• pp.219-225
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• 2006

Bacterial community structure in the Juam Reservoir was analysed using fluorescent in situ hybridization (FISH) technique from April 2005 to January 2006. Total bacterial numbers varied in the range of 1.58 ${\sim}\;2.73{\times}\;10^6\;cells\;mL^{-1}$ proportional to the concentration of chi-a and had the minimal value in January. The ratios of ${\alpha}\;{\cdot}\;{\beta}\;{\cdot}\;{\gamma}$-subclass proteobacteria and Cytophaga-Flavobacterium (CF) group to total bacteria ranged from 45.1% to 77.5%, and the ratios of ${\alpha}\;{\cdot}\;{\beta}\;{\cdot}\;{\gamma}$-subclasses to total bacteria in June and September with the concentration of chi-a measured were lower than those ratios in April and January. It suggests that enriched growth of Microcystis aeruginosa may inhibit the metabolic activlty of ${\alpha}\;{\cdot}\;{\beta}\;{\cdot}\;{\gamma}$-subclass proteobacteria. However, the ratio of CF group bacteria represented no large change depending on algal bloom. In terms of nitrifying bacteria, the numbers of ammonia-oxidizing bacteria ranged from 9.9 ${\times}\;10^4\;to\;25.5\;{\times}10^4\;cells\;mL^{-1}$ with sharp fluctuation whereas those of nitrite-oxidizing bacteria varied in 8.7${\sim}9.8{\times}10^4\;cells\;mL^{-1}$ without noticeable change except the maximal value of $20.3{\times}10^4\;cells\;mL^{-1}$ in January maybe due to the high DO.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.