• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.415-426
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• 2021

The implementation of iris biometrics on smartphone devices has recently become an emerging research topic. As the use of iris biometrics on smartphone devices becomes more widely adopted, it is to be expected that there will be similar efforts in the research community to beat the biometric by exploring new spoofing methods and this will drive a corresponding requirement for new liveness detection methods. In this paper we addresses the problem of presentation attacks (Spoofing) against the Iris Recognition System on mobile devices and propose novel Presentation Attack Detection (PAD) method which suitable for mobile environment.

• Journal of KIISE
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• v.41 no.12
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• pp.1058-1065
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• 2014

In order to assist user's who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.

• The Plant Pathology Journal
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• v.31 no.3
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• pp.212-218
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• 2015

In this study, we developed a species-specific PCR assay for rapid and accurate detection of three Xanthomonas species, X. axonopodis pv. poinsettiicola (XAP), X. hyacinthi (XH) and X. campestris pv. zantedeschiae (XCZ), based on their draft genome sequences. XAP, XH and XCZ genomes consist of single chromosomes that contain 5,221, 4,395 and 7,986 protein coding genes, respectively. Species-specific primers were designed from variable regions of the draft genome sequence data and assessed by a PCR-based detection method. These primers were also tested for specificity against 17 allied Xanthomonas species as well as against the host DNA and the microbial community of the host surface. Three primer sets were found to be very specific and no amplification product was obtained with the host DNA and the microbial community of the host surface. In addition, a detection limit of $1pg/{\mu}l$ per PCR reaction was detected when these primer sets were used to amplify corresponding bacterial DNAs. Therefore, these primer sets and the developed species-specific PCR assay represent a valuable, sensitive, and rapid diagnostic tool that can be used to detect three specific pathogens at early stages of infection and may help control diseases.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.102-110
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• 2023

Wastewater Based Epidemiology (WBE) provides useful information not only on the use of illegal drugs in the community, but also on the presence of hygiene and health products and infectious pathogens in sewage facilities. As a consequence of the SARS-CoV-19 virus epidemic in 2019, monitoring the status of the infection is of utmost importance. SARS-CoV-19 was also detected in sewage, and the number and trend of infections in the community suggest that the application of the WBE system would be useful and appropriate. This study introduces a pre-treatment concentration method including viruses in sewage samples. A total of seven methods which were subdivided into methods for adsorption-extraction, ultra-filtration, PEG precipitation, and ultra-centrifugation, and the results for analyzing the recovery rates were included. Meanwhile, it is necessary to pay attention to rapid detection technologies which analyze infectious pathogens at the site of sewage facilities. These can include ELISA, FTIR, SERS, and biosensor based on the detection principle, and the characteristics, advantages, and disadvantages of each were summarized herein. If rapid detection technologies and accurate quantitative analyses are further developed, the use of sewage mechanics in response to pandemic viruses is expected to expand further.

• Health Policy and Management
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• v.30 no.2
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• pp.221-230
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• 2020

Background: The purpose of this study is to identify the factors affecting the depressive mood experience of adults in their 20s. Methods: This study is a descriptive survey that conducted a secondary analysis using data from the 2017 Community Health Survey, which is conducted annually in Korea. The study targets 21,324 adults in their 20s. Data analysis was conducted after creating a composite sample plan file that reflected layering variables, colony variables, and weights. Results: Factors affecting the depressive mood experience were suicide thought experience, subjective stress level, gender, monthly household income, smoking status, subjective health level, breakfast status, participation in social activities, and whether the Internet, games, and smartphone interfered with daily life (p<0.05). Conclusion: It is necessary to establish and realize a system that enables early detection and support of depression and suicide high-risk groups at the individual, home, community, and national levels.

• Journal of Microbiology and Biotechnology
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• v.17 no.4
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• pp.560-570
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• 2007

The effective and accurate assessment of the total microbial community diversity is one of the primary challenges in modem microbial ecology, especially for the detection and characterization of unculturable populations and populations with a low abundance. Accordingly, this study was undertaken to investigate the diversity of the microbial community during the biodegradation of cis- and trans-dichloroethenes in soil and wastewater enrichment cultures. Community profiling using PCR targeting the l6S rRNA gene and denaturing gradient gel electrophoresis (PCR-DGGE) revealed an alteration in the bacterial community profiles with time. Exposure to cis- and trans-dichloroethenes led to the disappearance of certain genospecies that were initially observed in the untreated samples. A cluster analysis of the bacterial DGGE community profiles at various sampling times during the degradation process indicated that the community profile became stable after day 10 of the enrichment. DNA sequencing and phylogenetic analysis of selected DGGE bands revealed that the genera Acinetobacter, Pseudomonas, Bacillus, Comamonas, and Arthrobacter, plus several other important uncultured bacterial phylotypes, dominated the enrichment cultures. Thus, the identified dominant phylotypes may play an important role in the degradation of cis- and trans-dichloroethenes.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.55-67
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• 2014

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Journal of KIISE
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• v.44 no.10
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• pp.1105-1111
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• 2017

Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.