• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.459-464
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• 2008

In this paper, we present the model-based fuzzy controller for container cranes which effectively performs set-point tracking control of trolley and anti-swaying control under system parameter and disturbance changes. The first part of this paper focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear model of a container crane. In the second part, we present a design methodology of the model-based fuzzy controller. Sub-controllers are designed using LQ control theory for each subsystem in fuzzy model and then the proposed controller is performed with the combination of these sub-controllers by fuzzy IF-THEN rules. In the results of simulation, the fuzzy model showed almost similar dynamic characteristics compared to the outputs of the nonlinear container crane model. Also, the model-based fuzzy controller showed not only the fast settling time for the change in parameter and disturbance, but also stable and robust control performances without any steady-state error.

• Clinical and Experimental Pediatrics
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• v.64 no.10
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• pp.504-510
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• 2021

Population screening of newborns is an extremely important and informative diagnostic approach that allows early identification of babies who are predisposed to the development of a number of serious diseases. Some of these diseases are known and have effective treatment methods. Neonatal screening enables the early diagnosis and subsequent timely initiation of therapy. This helps to prevent serious complications and reduce the percentage of disability and deaths among newborns and young children. Primary immunodeficiency diseases and primary immunodeficiency syndrome (PIDS) are a heterogeneous group of diseases and conditions based on impaired immune system function associated with developmental defects and characterized by various combinations of recurrent infections, development of autoimmune and lymphoproliferative syndromes (genetic defects in apoptosis, gene mutation Fas receptor or ligand), granulomatous process, and malignant neoplasms. Most of these diseases manifest in infancy and lead to serious illness, disability, and high mortality rates. Until recently, it was impossible to identify children with PIDS before the onset of the first clinical symptoms, which are usually accompanied by complications in the form of severe coinfections of a viral-bacterial-fungal etiology. Modern advances in medical laboratory technology have allowed the identification of children with severe PIDS, manifested by T- and/or B-cell lymphopenia and other disorders of the immune system. This review discusses the main existing strategies and directions used in PIDS screening programs for newborns, including approaches to screening based on excision of T-cell receptors and kappa-recombination excision circles, as well as the potential role and place of next-generation sequencing technology to increase the diagnostic accuracy of these diseases.

• Journal of the Korean Wood Science and Technology
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• v.49 no.6
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• pp.574-590
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• 2021

International efforts to prevent illegally logged wood are expanding around the world. The "Legal Wood Trade Promotion System" was enacted in Korea in 2019 to strengthen the legal import and distribution of commercial wood in Korea. Since then, this system has promoted and ensured that the imported wood and wood products are legal with respect to the country of origin, wood species, and harvested area. As verification methods, DNA analysis technology and anatomical analysis using a microscope are mainly used in conjunction. Therefore, in this study, wood species of plywood were identified by analyzing the anatomical characteristics of various wood products for the first time. Douglas fir (Pseudotsuga menziesii (Mirb.) Franco.) and larch (Larix kaempferi (Lamb.) Carriere) plywoods (7, 9 ply) were obtained from four companies that supply plywood in Korea. After cutting each company's plywood to a size of about 1 cm³, the layers from top to bottom were separated into single layers, and three sections were observed using an optical microscope. The results revealed that the plywood was composed of a mixture of softwood and hardwood wood species, pine wood species, poplar wood species, or a mixture of larch and pine wood species. Identification of wood species using microscopy is important and can enable the scientific analysis and verification of various wood products, including plywood, imported from countries where the likelihood of indiscriminate distribution of illegal wood and illegal logging is high.

• Journal of Microbiology and Biotechnology
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• v.32 no.8
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• pp.967-975
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• 2022

Kombucha mutualistic community (KMC) is composed by acetic acid bacteria and yeasts, producing fermented tea with health benefits. As part of the BIOlogy and Mars EXperiment (BIOMEX) project, the effect of Mars-like conditions on the KMC was analyzed. Here, we analyzed metagenome-assembled genomes (MAGs) of the Komagataeibacter, which is a predominant genus in KMC, to understand their roles in the KMC after exposure to Mars-like conditions (outside the International Space Station) based on functional genetic elements. We constructed three MAGs: K. hansenii, K. rhaeticus, and K. oboediens. Our results showed that (i) K. oboediens MAG functionally more complex than K. hansenii, (ii) K. hansenii is a keystone in KMCs with specific functional features to tolerate extreme stress, and (iii) genes related to the PPDK, betaine biosynthesis, polyamines biosynthesis, sulfate-sulfur assimilation pathway as well as type II toxin-antitoxin (TA) system, quorum sensing (QS) system, and cellulose production could play important roles in the resilience of KMC after exposure to Mars-like stress. Our findings show the potential mechanisms through which Komagataeibacter tolerates the extraterrestrial stress and will help to understand minimal microbial composition of KMC for space travelers.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.473-479
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• 2023

In this study, the dose of photoneutrons generated during radiotherapy of prostate cancer using high energy was measured using a photo-stimulated luminescence dosimeter. In addition, this study was intended to study the probability of side effects occurring in the abdomen. A medical linear accelerator capable of generating 15 MV energy, True Beam STx (Varian Medical Systems, USA) and a radiation treatment planning system (Eclipse, Varian Medical Systems, USA) were used. A human body phantom was installed on the couch of the linear accelerator, and an Albedo Neutron Optical Stimulation Luminescence Neutron Detector (Landauer Inc., IL, USA) was used to measure the photoneutron dose. The photoneutron dose value in the abdomen of VMAT and 3C-CRT was 52.8 mSv, more than twice as high as VMAT compared to 3D-CRT. During radiotherapy of prostate cancer, the probability of causing side effects in the abdomen due to light neutron dose was calculated to be 3.2 per 1,000 for VMAT and 1.4 for 3D-CRT. By studying the abdomen, which has a major side effect that can occur during radiotherapy of prostate cancer, it is expected that it will be used as a meaningful study to study the quality of life and stochastic effect of prostate cancer patients

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.116-125
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• 2024

The COVID-19 pandemic is not over despite the emergency use authorization as can see recent COVID-19 daily confirmed cases. The viruses are not only difficult to diagnose and treat due to random mutations, but also pose threat human being because they have the potential to be exploited as biochemical weapons by genetic manipulation. Therefore, it is inevitable to the rapid antibody-based therapeutic platform to quickly respond to future pandemics by new/re-emerging viruses. Although numerous researches have been conducted for the fast development of antibody-based therapeutics, it is sometimes hard to respond rapidly to new viruses because of complicated expression or purification processes for antibody production. In this study, a novel rapid antibody-based therapeutic platform using single B cell sorting method and mRNA-antibody. High immunogenicity was caused to produce antibodies in vivo through mRNA-antigen inoculation. Subsequently, antigen-specific antibody candidates were selected and obtained using isolation of B cells containing antibody at the single cell level. Using the antibody-based therapeutic platform system in this study, it was confirmed that novel antigen-specific antibodies could be obtained in about 40 days, and suggested that the possibility of rapid response to new variant viruses.

• Journal of Food Hygiene and Safety
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• v.31 no.6
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• pp.393-398
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• 2016

The objective of this study was to isolate pathogenic Escherichia coli from fresh vegetables with selective media and Petrifilm, and identify a suspicious colony using biochemical identification. Twenty gram of lettuce, twenty gram of cabbage and ten gram of sprout were prepared, and a 5-strain mixture of pathogenic E. coli (Enterohemorrhagic E. coli NCCP11142, Enterotoxigenic E. coli NCCP14037, Enteropathogenic E. coli NCCP14038, Enteroaggregative E. coli NCCP14039, Enteropathogenic E. coli NCCP15661) was inoculated to obtain 1, 2 and 3 log CFU/g. Eighty to ninety milliliter of buffered peptone water (BPW) was placed and pummeled for 60 s. As a results, the Petrifilm method was all positive, but enrichment method of qualitative analysis was negative except for 3-log CFU/g inoculated lettuce. Regarding biochemical identification of pathogenic E. coli, the identification rates were dependent on type of methods and vegetables; lettuce: API 20E 100% (44/44), Microgen GNA 100% (44/44) and Food System 66.7% (10/15), cabbage: API 20E 64.7% (22/34), Microgen GNA 50% (16/32) and Food System 60% (9/15), sprout: API 20E 65.1% (28/43), Microgen GNA 62.3% (27/43) and Food System 53.3% (8/15). These results could be useful in determining an appropriate method to detect pathogenic E. coli in fresh vegetables.

• The Journal of Internal Korean Medicine
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• v.36 no.4
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• pp.547-561
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• 2015

Objectives In this study, we divided Korean asthma patients into excess syndrome or deficiency syndrome groups according to clinical phenotype. Genetic analysis was conducted to investigate the association of exonic SNPs in the CD46 gene polymorphism with the clinical phenotype based on the differentiation syndrome of the bronchial asthma patients.Methods There were 95 healthy patients (control group) and 53 asthma patients. (The deficiency syndrome group included 24 and the excess syndrome group 29). We searched the exonic areas of the CD46 gene in the NCBI website SNPs with <0.01 minor allele frequency (MAF) and <0.01 heterozygosity. We finally selected two SNPs: rs138843816, Ser13Phe and rs7144, 3’-UTR. Hardy-Weinberg equilibrium was calculated using SNPStats.Results There were significant differences in the codominant 1 model and the dominant model between the healthy group and the asthma group. There were significant differences between deficiency syndrome group and the excess syndrome group in the genotype frequencies and in the codominant 1 model, the dominant model, and the log-additive model. The allele frequency of rs7144C showed a significant difference between the deficiency syndrome group and the excess syndrome group. Two-SNP haplotype analysis showed a significant difference in frequency in the deficiency syndrome group and in the excess syndrome group. There were significant differences between the healthy group and the excess syndrome group in the codominant 1 model, the dominant model, and the log-additive model. The frequency of the rs7144 C allele exhibited a significant difference in the demonstration. SNP haplotype analysis between the healthy group and the excess syndrome group showed a significant difference in the frequency of the CT haplotype and the CC haplotype.Conclusions The results indicate that two CD46 SNPs (rs138843816, Ser13Phe and rs7144, 3′–UTR) might be associated with the symptomatic excess syndrome in Korean asthma patients.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.