• Food Science and Biotechnology
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• 제18권4호
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• pp.823-832
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• 2009

In recent years, special concerns have been raised about the safety assessment of foods and food ingredients derived from genetically modified organisms (GMOs). A growing number of countries establish regulations and laws for GMOs in order to allow consumers an informed choice. In this case, a lot of methods have been developed for the detection of GMOs. However, the reproducibility among methods and laboratories is still a problem. Consequently, it is still in great demand for more effective methods. In comparison with the gel electrophoresis, the capillary electrophoresis (CE) technology has some unique advantages, such as high resolution efficiency and less time consumption. Therefore, some CE-based methods have been developed for the detection of GMOs in recent years. All kinds of CE detection methods, such as ultraviolet (UV), laser induced fluorescence (LIF), and chemiluminescence (CL) detection, have been used for GMOs detection. Microchip capillary electrophoresis (MCE) methods have also been used for GMOs detection and they have shown some unique advantages.

• Journal of information and communication convergence engineering
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• 제14권2호
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• pp.129-135
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• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1419-1422
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• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.710-713
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• 1996

This paper deals with the fault detection problem in uncertain linear multivariable systems having both model mismatch and noise. A robust detection presented by Kwon et al.(1994) for SISO systems has been here extended to the multivariable systems are derived. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• The Plant Pathology Journal
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• 제34권6호
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• pp.490-498
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• 2018

Panicle blight and seed rot disease caused mainly by Burkholderia glumae and Burkholderia gladioli is threatening rice cultivation worldwide. The bacteria have been reported as seed-borne pathogens from rice. Accurate detection of both pathogens on the seeds is very important for limiting the disease dissemination. Novel primer pairs targeting specific molecular markers were developed for the robust detection of B. glumae and B. gladioli. The designed primers were specific in detecting the target species with no apparent cross-reactions with other related Burkholderia species at the expected product size. Both primer pairs displayed a high degree of sensitivity for detection of B. glumae and B. gladioli separately in monoplex PCR or simultaneously in duplex PCR from both extracted gDNA and directly preheated bacterial cell suspensions. Limit of detection was as low as 0.1 ng of gDNA of both species and $3.86{\times}10^2cells$ for B. glumae and $5.85{\times}10^2cells$ for B. gladioli. On inoculated rice seeds, the designed primers could separately or simultaneously detect B. glumae and B. gladioli with a detection limit as low as $1.86{\times}10^3cells$ per rice seed for B. glumae and $1.04{\times}10^4cells$ per rice seed of B. gladioli. The novel primers maybe valuable as a more sensitive, specific, and robust tool for the efficient simultaneous detection of B. glumae and B. gladioli on rice seeds, which is important in combating rice panicle blight and seed rot by early detection and confirmation of the dissemination of pathogen-free rice seeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.2752-2768
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• 2020

Phishing websites can have devastating effects on governmental, financial, and social services, as well as on individual privacy. Currently, many phishing detection solutions are evaluated using small datasets and, thus, are prone to sampling issues, such as representing legitimate websites by only high-ranking websites, which could make their evaluation less relevant in practice. Phishing detection solutions which depend only on the URL are attractive, as they can be used in limited systems, such as with firewalls. In this paper, we present a URL-only phishing detection solution based on a convolutional neural network (CNN) model. The proposed CNN takes the URL as the input, rather than using predetermined features such as URL length. For training and evaluation, we have collected over two million URLs in a massive URL phishing detection (MUPD) dataset. We split MUPD into training, validation and testing datasets. The proposed CNN achieves approximately 96% accuracy on the testing dataset; this accuracy is achieved with URL schemes (such as HTTP and HTTPS) removed from the URL. Our proposed solution achieved better accuracy compared to an existing state-of-the-art URL-only model on a published dataset. Finally, the results of our experiment suggest keeping the CNN up-to-date for better results in practice.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.439-443
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• 2008

It is shown that even if the modulating frequency of the light is too high for direct detection the signal can be extracted by frequency conversion at the same time as the detection by means of the nonlinearity of the APD. When this frequency conversion detection is applied to an optical receiver, the detection bandwidth can be increased while the configuration of the optical detection circuit and the signal processing in the subsequent stages are simplified. A fundamental analysis is carried out with an APD which is confirmed experimentally.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• 한국군사과학기술학회지
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• 제13권3호
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• pp.461-469
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• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.857-860
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• 2009

Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a battlefield. The intrusion detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to characterize the WSN parameters such as node density and sensing range in terms of a desirable detection probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN.