• Journal of Microbiology and Biotechnology
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• v.31 no.12
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• pp.1709-1715
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• 2021

Outbreaks of food poisoning due to the consumption of norovirus-contaminated shellfish continue to occur. Male-specific (F+) coliphage has been suggested as an indicator of viral species due to the association with animal and human wastes. Here, we compared two methods, the double agar overlay and the quantitative real-time PCR (RT-PCR)-based method, for evaluating the applicability of F+ coliphage-based detection technique in microbial contamination tracking of shellfish samples. The RT-PCR-based method showed 1.6-39 times higher coliphage PFU values from spiked shellfish samples, in relation to the double agar overlay method. These differences indicated that the RT-PCR-based technique can detect both intact viruses and non-particle-protected viral DNA/RNA, suggesting that the RT-PCR based method could be a more efficient tool for tracking microbial contamination in shellfish. However, the virome information on F+ coliphage-contaminated oyster samples revealed that the high specificity of the RT-PCR- based method has a limitation in microbial contamination tracking due to the genomic diversity of F+ coliphages. Further research on the development of appropriate primer sets for microbial contamination tracking is therefore necessary. This study provides preliminary insight that should be examined in the search for suitable microbial contamination tracking methods to control the sanitation of shellfish and related seawater.

• 축산식품과학과 산업
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• v.12 no.1
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• pp.46-58
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• 2023

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.19-20
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• 2010

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.605-613
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• 2006

The goal of this study was to propose building strategies of web-based national park monitoring system (WNPMS) using geographic information analysis system. To accomplish this study, at first, this study selected and made integrated management indicators considering physical, ecological, and socio-psychological carrying capacity in national park. Secondly, this study built up an integrated management this system with statistical analysis program for execution of various multivariate analysis and spatial analysis. Finally, WNPMS could identify the relationship among visitors, natural resources, and recreation facilities in national park, and forecast the future management status of each national park in Korea. There results of this study will contribute to prevent the damage of natural resources and facilities, improve visitor's satisfaction, prevent an excess of carrying capacity at national park, and established tailored management strategies of each national park.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.61-66
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• 2006

We propose a new fragment assembly program MLP (mate-based layout with PHRAP). MLP consists of PHRAP, repeat masking, and a new layout algorithm that uses the mate pair information. Our experimental results show that by using MLP instead of PHRAP, we can significantly reduce the difference between the assembled sequence and the original genome sequence.

• Preventive Nutrition and Food Science
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• v.18 no.4
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• pp.242-248
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• 2013

In recent years, manufacturers of animal-based foods with health claims have encountered difficulties in the labeling of their products because of a lack of regulation on defining the functionality of animal-based foods. Therefore, this study was conducted to establish the basic requirements for the development of a definition for functional animal-based foods by investigating consumer and industry awareness. Survey data were collected from 114 industry representatives and 1,100 consumers. The questions of the survey included items on production status and future production plans, functionality labeling, promotion plans, establishment of definition, the role of the government, consumer perception, and selection of products. The results show that both industry representatives and consumers believe that legislation and the provision of scientific evidence should be improved for the development of a functional animal-based foods market. The results obtained from this study will contribute to consumer trust by supplying correct information and can be utilized in the industry as basic data for the development of functional animal-based food products.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.7-13
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• 2013

Recently, defect management have been considered as one of the major issues for more large-sized and complicated in domestic construction industry. However, the defect management have not been performed systematically because of special manpower, excessive amount of documents, 2D based inspection work, unclear traditional checklists, complicated work process and difficulty in communicating construction information. Therefore, the construction field manager could not performed the quality inspection and defect management work on time as well as the reliability of recorded quality and defect factors was decreased. The primary objective of this study is develop a Construction Defect Management Application CDMA) using a mobile (smartphone). The application can be sharing a huge information and communication technology based on RFID (Radio-Frequency Identification), BIM (Building Information Modeling) which enables field mangers to efficiently gather the information of defection in construction on-site.

• Bulletin of the Korean Chemical Society
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• v.25 no.10
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• pp.1571-1574
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• 2004

• Journal of Internet Technology
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• v.22 no.2
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• pp.423-439
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• 2021

Android is one of the most popular platforms for the mobile and Internet of Things (IoT) devices. This popularity has made Android-based devices a valuable target of malicious apps. Thus, it is essential to devise automatic and portable malware detection approaches for the Android platform. There are many studies on detecting mobile malware using machine learning techniques. In these studies, however, the dataset is imbalanced or is not large enough to generalize the machine learning model, or the dimensionality of features is too high to apply nonlinear classifiers. In this article, we propose a machine learning-based Android malware detection scheme that uses API calls and permissions as features. To restrict the dimensionality of features, we propose minimal domain knowledge-based and Gini importance-based feature selection. We construct large and balanced real-world datasets to build a generalized and non-skewed model and verify our model through experiments. We achieve 96.51% classification accuracy using Random Forest classifier with low overhead. In addition, we also provide an analysis on falsely classified samples in detail. The analysis results show that API hiding can degrade the performance of API call information-based malware detection systems.