• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5257-5264
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• 2016

Background: Though people of color (POC) are less likely to become afflicted with skin cancer, they are much more likely to die from it due to delay in detection or presentation. Very often, skin cancer is diagnosed at a more advanced stage in POC, making treatment difficult.The purpose of this research was to improve awareness regarding skin cancers in people of color by providing recommendations to clinicians and the general public for early detection and photo protection preventive measures. Methods: Data on different types of skin cancers were presented to POC. Due to limited research, there are few resources providing insights for evaluating darkly pigmented lesions in POC. Diagnostic features for different types of skin cancers were recorded and various possible risk factors were considered. Results: This study provided directions for the prevention and early detection of skin cancer in POC based on a comprehensive review of available data. Conclusions: The increased morbidity and mortality rate associated with skin cancer in POC is due to lack of awareness, diagnosis at a more advanced stage and socioeconomic barriers hindering access to care. Raising public health concerns for skin cancer prevention strategies for all people, regardless of ethnic background and socioeconomic status, is the key to timely diagnosis and treatment.

• Smart Structures and Systems
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• v.21 no.5
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.615-629
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• 2014

This study analyzed existing patents related to real-time monitoring and detection technology for landslides on natural terrain. The purpose of patent analysis is to understand landslide hazard technology trends and to develop new advanced technology. This study searched patent data using key words related to landslide monitoring and detection in Korea, the USA, Japan, China (Hong Kong), Europe, and Taiwan. The patents were divided into five main categories and five to seven subcategories in each main category and analyzed by year, country, and applicants. The results were utilized to derive a portfolio of promising technologies for each country. The analysis results will also contribute to the development of more effective research strategies and to categorize research findings from previous studies on landslide hazards.

• Korean Journal of Veterinary Research
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• v.50 no.1
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• pp.43-48
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• 2010

Animal disease surveillance system, defined as the continuous investigation of a given population to detect the occurrence of disease or infection for control purposes, has been key roles to assess the health status of an animal population and, more recently, in international trade of animal and animal products with regard to risk assessment. Especially, for a system aiming to determine whether or not a disease is present in a population sensitivity of the system should be maintained high enough not to miss an infected animal. Therefore, when planning the implementation of surveillance system a number of factors that affecting surveillance sensitivity should be taken into account. Of these parameters sample size is of important, and different approaches are used to calculate sample size, usually depending on the objective of surveillance systems. The purpose of this study was to evaluate the sensitivity of the current national serological surveillance programs for four selected bovine diseases assuming a specified sampling plan, to examine factors affecting the probability of detection, and to provide sample sizes required for achieving surveillance goal of detecting at least an infection in a given population. Our results showed that, for example, detecting low level of prevalence (0.2% for bovine tuberculosis) requires selection of all animals per typical Korean cattle farm (n = 17), and thus risk-based target surveillance for high risk groups can be an alternative strategy to increase sensitivity while not increasing overall sampling efforts. The minimum sample size required for detecting at least one positive animal was sharply increased as the disease prevalence is low. More importantly, high reliability of prevalence estimation was expected with increased sampling fraction even when zero-infected animal was identified. The effect of sample size is also discussed in terms of the maximum prevalence when zero-infected animals were identified and on the probability of failure to detect an infection. We suggest that for many serological surveillance systems, diagnostic performance of the testing method, sample size, prevalence, population size, and statistical confidence need to be considered to correctly interpret results of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1754-1769
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• 2013

For the purpose of enhancing the spectrum efficiency, cognitive radio (CR) technology has been recently proposed as a promising dynamic spectrum allocation paradigm. In CR, spectrum sensing is the key capability of secondary users in a cognitive radio network that aims for reducing the probability of harmful interference with primary users. However, the individual CRs might not be able to carry out reliable detection of the presence of a primary radio due to the impact of channel fading or shadowing. This paper studies the cooperative spectrum sensing scheme as means of optimizing the sensing performance in AWGN and Rayleigh channels. Results generated from simulation provide evidence of the impact of channel condition on the complementary receiver operating characteristic (ROC). Based on the results, it was found that with constant local SNRs at the secondary users, the probability of missed detection ($P_m$) of cooperative spectrum sensing in a cognitive radio network, calculated using a closed form expression, can be significantly minimized. Thus, the paper illustrates that improvement of the detection performance of the CR network can be achieved by establishing a centralized cooperation among neighboring cognitive radio users. Finally, verification of the validity of the fusion schemes utilized for combining the individual CR decisions is provided.

• Journal of Periodontal and Implant Science
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• v.38 no.sup2
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• pp.335-342
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• 2008

Purpose: Cytolethal distending toxin (CDT) considered as a key factor of localized aggressive periodontitis, endocarditis, meningitis, and osteomyelitis is composed of five open reading frames (ORFs). Among of them, the individual role of CdtA and CdtC is not clear; several reports presents that CDT is an AB2 toxin and they enters the host cell via clathrin-coated pits or through the interaction with GM3 ganglioside. So, CdtA, CdtC, or both seem to be required for the delivery of the CdtB protein into the host cell. Moreover, recombinant CDT was suggested as good vaccine material and antibody against CDT can be used for neutralization or for a detection kit. Materials and Methods: We constructed the pET28a-cdtC plasmid from Aggregatibacter actinomycetemcomitans Y4 by genomic DNA PCR and expressed in BL21 (DE3) Escherichia coli system. We obtained the antibody against the recombinant CdtC in mice system. Using the anti-CdtC antibody, we test the native CdtC detection by ELISA and Western Blotting and confirm the expression time of native CdtC protein during the growth phase of A. actinomycetemcomitans. Results: In this study we reconstructed CdtC subunit of A. actinomycetemcomitans Y4 and generated the anti CdtC antibody against recombinant CdtC subunit expressed in E. coli system. Our anti CdtC antibody can be interacting with recombinant CdtC and native CDT in ELISA and Western system. Also, CDT holotoxin existed at 24h but not at 48h meaning that CDT holotoxin was assembled at specific time during the bacterial growth. Conclusion: In conclusion, we thought that our anti CdtC antibody could be used mucosal adjuvant or detection kit development, because it could interact with native CDT holotoxin.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.957-966
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• 2020

According to the recent statistics of the National Industrial Security Center, about 80% of the confidential leak are caused by former and current employees in the case of domestic confidential leak accidents. Most of the information leak incidents by these insiders are due to poor security management system and information leak detection technology. Blocking confidential leak of insiders is a very important issue in the corporate security sector, but many previous researches have focused on responding to intrusions by external threats rather than by insider threats. Therefore, in this research, we design an internal information leak scenario to effectively and efficiently detect various abnormalities occurring in the enterprise, analyze the key indicators of the leak symptoms derived from the scenarios by using data analytics and propose a model that accurately detects leak activities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.635-638
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• 2005

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. There are two types of shot changes, abrupt and gradual. The major problem of shot change detection lies on the difficulty of specifying the correct threshold, which determines the performance of shot change detection. As to the clustering approach, the right number of clusters is hard to be found. Different clustering may lead to completely different results. In this thesis, we propose a video segmentation method using a color-X$^2$ intensity histogram-based fuzzy c-means clustering algorithm.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Animal cells and systems
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• v.5 no.2
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• pp.153-156
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• 2001

A key aspect of genomic research in the “post-genome era”is to associate sequence variations with heritable phenotypes. The most common variations in the human genome are single nucleotide polymorphisms (SNPs) that occur approximately once in every 500 to 1,000 bases. Although analyzing the phenotypic outcome of these SNPs is crucial to facilitate large-scale association studies of genetic diseases, detection of SNPs from an extended number of human DNA samples is often difficult, labor-intensive and time-consuming. Recent development in SNP detection methods using DNA microarrays and mass spectrophotometry has allowed automated high throughput analyses, but such equipments are not accessible to many scientists. In this study, we demonstrate that a simple PCR-based method using primers with a mismatched base at the 3'-end provides a fast and easy tool to identify known SNPs from human genomic DNA in a regular molecular biology laboratory. Results from this PCR amplification of specific alleles (PASA) analysis efficiently and accurately typed the Q576R polymorphism of human IL4 receptor from the genomic DNAs of 29 Koreans, including 9 samples whose genotype could not be discerned by the conventiona1 PCR-SSCP (single strand conformation polymorphism) method. Given the increasing attention to disease-associated polymorphisms in genomic research, this alternative technique will be very useful to identify SNPs in large-scale population studies.