• Parasites, Hosts and Diseases
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• v.24 no.1
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• pp.49-54
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• 1986

To evaluate the immature eggs of Paragonimus westermani as a source of diagnostic antigen, about a million eggs which were excreted by 104 adult worms were collected; their saline extract(soluble egg antigen; PwSEA) was prepared. The specific IgG and IgM antibody levels were observed in experimental dog paragonimiasis by micro ESISA, using PwSEA as well as whole worm extract of 12 week-old P. westermani(PwWWE). The protein composition of the PwSEA was observed by disc-PAGE. The results could be summarized as follows: 1. Specific IgG antibody to PwSEA begant to increase on 8 weeks after the experimental infection; it maintained its high level until the observation period of 13 weeks. The levels of IgM antibody to PwSEA however, did not show any significant change. 2. Specific IgG antibody to PwWWE began to increase earlier from 2 weeks after the infection and continued to increase until the observation period of 13 weeks. Its level was much higher than that to PwSEA. Specific IgM antibody to PwWWE increased temporarily during 2-8 weeks after the infection. 3. By disc-PAGE, PwSEA showed 2 protein bands of very low motility. The bands of PwSEA corresponded to the frist and second bands in the electrophoretic pattern of PwWWE of the 12 week-old worms. The above results indicated that the PwSEA induced antibody production in dog paragonimiasis, but its antigenicity was weaker than PwWWE to be used as a diagnostic antigen.

• Clinical and Experimental Reproductive Medicine
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• v.14 no.2
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• pp.138-148
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• 1987

5${\alpha}$-dihydrotestosterone(DHT)과 testosterone(T)은 남성 생식기관의 주 생성 호르몬들로 그 구조가 매우 비슷하여 이들 각 개를 특이하게 측정(specific determination)하는 방법이 개발되지 않고 있다. 본 연구는 고속액체 크로마토그라피(HPLC)를 이용하여 이들을 분리한 후 섬광면역측 정법(Luminescence immunoassay, LIA)으로 정량하는 방법을 개발하여 이들의 응용 가능성을 검토하고져 하였다. DHT와 T의 retention time은 각각 10.3min, 17.6min이었다. DHT-LIA와 T-LIA에 서 다른 스테로이드들과의 교차반응도는 방사면역측정법(RIA)과 대동소이하였다. 정도관리(quality control) 시료의 intra-assay variation은 DHT-LIA가 8.7%, T-LIA가 6.0%의 변이계수를 나타내었고, inter-assay variation의 변이계수는 각각 12.0% 및 15.3%이었다. 실측치(y)와 기대치(x)간의 관계를 보면, DHT-LIA경우는 Y=0.94X+0.9(r=0.989), T-LIA는 Y=1.01X+0.06(r =0.988)로 두 측정치 사이에는 통계적으로 유의한 차이가 없었다. 위의-측정방법을 이용하여 DHT-enanthate와 T-enanthate 처리후 혈청내 DHT 및 T의 농도변화를 조사한 실험결과 LIA와 RIA의 값사이에 유의한 차이가 없었다. 위의 결과로 보아 본 실험에서 개발된 DHT와 T의 섬광면역측정법은 정립되었다고 사려된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.1
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• pp.47-54
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• 1993

For the numerical analysis of free oscillation characteristics in a harbor with general boundary and bottom topography, finite element method is applied. The governing Helmholtz equation is transformed into a generalized matrix eigenvalue problem using the standard finite element procedure. A computer code is developed for the numerical evaluation of natural frequencies and free oscillation modes. In the eigensolution process, a shifting strategy is devised for the treatment of numerical singularity. Scaling of coefficient matrix is also found to be effective for the alleviation of numerical ill-conditioning. For the test problems, firstly, analytical and numerical solutions are compared and validity of the code is obtained. Hence the method is successfully applicable for the real-world problems with general geometric boundaries and bottom topography.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.5
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• pp.59-66
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• 2002

Many important scientific and engineering problems require the computation of a small number of eigenvalues for large nonsymmetric matrices. The biorthogonal Lanczos method is one of the methods to solve that problem, but it faces serious breakdown problems. In this paper, we introduce a modified biorthogonal Lanczos method to find a few eigenvalues of a large sparse nonsymmetric matrix. The proposed method generates reduction matrices that are similar to those generated by the standard biorthogonal Lanczos method. We prove that the breakdown conditions of our method are less stringent than the standard method. We then implement the modified biorthogonal Lanczos method on the CRAY machine and discuss the decreased breakdown conditions.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.89-92
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• 2011

The modeling of thermodynamic non-idealities and transport anomalies is a crucial prerequisite to realistically simulate the mixing and combustion processes of liquid propellants injected above critical pressures. This study has developed a specific set of subroutines to calculate the thermodynamic and transport properties based on the generalized cubic equation of state (EoS) in a coupled manner with the standard chemical kinetics packages (Chemkin). The existing flamelet analysis code is extended with the real-fluid package and applied to numerical investigation of local flame structures of kerosene and liquid oxygen at high pressure conditions relevant to the actual rocket engines.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1177-1190
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• 2009

The traffic operating speed is one of important information to measure a road capacity. When we supply the information of the road of high traffic by using navigation, offering the present traffic information and the forecasted future information are the outstanding functions to serve the more accurate expected times and intervals. In this study, we proposed the traffic speed forecasting model using the accumulated traffic speed data of the road and highway and forecasted the average speed for each the road and high interval and each time interval using Fourier transformation and time series regression model with trigonometrical function. We also propose the proper method of missing data imputation and treatment for the outliers to raise an accuracy of the traffic speed forecasting and the speed grouping method for which data have similar traffic speed pattern to increase an efficiency of analysis.

• Journal of Korean society of Dental Hygiene
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• v.6 no.4
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• pp.311-324
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• 2006

The purpose of this investigation was to evaluate of the specificity of Fusobacterium nucleatum subspecies-specific DNA probes using dot blot hybridization. To confirm whether the clinical isolates were F. nucleatum or not, 16S rDNA of them were cloned and sequenced. The sequencing data were used in homology search with database of GenBank. When the homology was above 98% compared with the nucleotide sequence of a certain bacteria, it was judged as the same species with the bacteria. 23 strains of F. nucleatum were isolates from subgingival plaque of periodontitis patient. The clinical isolates of F. nucleatum were classified into 10 groups using phylogenetic analysis of 16S rDNA sequence. F. nucleatum subspecies nucleatum-specific DNA probe Fu4(1.3 kb) reacted with genomic DNAs from 8 type strains of F. nucleatum and it reacted strongly with those from 8 clinical isolates. The Fp4(0.8 kb) reacted with F. nucleatum subsp. polymorphum ATCC 10953 and one clinical isolates. Fv35(1.9 kb) and Fs17(8.2 kb) probes reacted with genomic DNAs from F. nucleatum subsp. vincentii ATCC 49256 and F. nucleatum subsp. fusiform ATCC 51190, respectively. Our results showed that it is not enough to evaluate the specificity of F. nucleatum subspecies-specific DNA probes with only dot blot hybridization. Therefore, Southern blot analysis will be necessary to confirm the specificity of F. nucleatum subspecies-specific DNA probes.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.99-107
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• 2005

In this paper, wc propose a now missing imputation method for minimizing loss of information linkage disequilibrium-based and haplotype-based imputation method, which estimate missing values of the data based on the specificity of Single Nucleotide Polymorphism(SNP) genotype data. Method for imputing data is needed to minimize the loss of information caused by experimental missing data. In general, missing imputation of biological data has used major allele imputation method. but this approach is not optima]. 1'his method has high error rates of missing values estimation since the characteristics of the genotype data are not considered not take into consideration the specific structure of the data. In this paper, we show the results of the comparative evaluation of our model methods and major imputation method for the estimation of missing values.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.216-223
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• 2019

Fault detection during production processes is one of the required operational tasks to run production processes both safely and consistently. Unexpected operational events or undetected process faults can have a serious impact on the production systems and subsequently on the final products' quality. In addition, such situations may lead to malfunctions or breakdowns of production processes. To reliably detect such abnormalities, a new one-class classification-based detection scheme has recently been developed The proposed method consists of four steps:1) noise filtering, 2) feature selection, 3) nonlinear representation and 4) outlier detection. The performance of the proposed scheme was demonstrated using the multivariate data obtained from a simulation process. The results have shown that the proposed method produced reliable monitoring results and outperforms any existing methods with an average improvement of 25.4%. The use of proper feature selection in the proposed framework yielded better detection performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.157-163
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• 2022

Recently, with the development of automation in the industrial field, research on anomaly detection is being actively conducted. An application for anomaly detection used in factory automation is camera-based defect inspection. Vision camera inspection shows high performance and efficiency in factory automation, but it is difficult to overcome the instability of lighting and environmental conditions. Although camera inspection using deep learning can solve the problem of vision camera inspection with much higher performance, it is difficult to apply to actual industrial fields because it requires a huge amount of normal and abnormal data for learning. Therefore, in this study, we propose a network that overcomes the problem of collecting abnormal data with 72 geometric transformation deep learning methods using only normal data and adds an outlier exposure method for performance improvement. By applying and verifying this to the MVTec data set, which is a database for auto-mobile parts data and outlier detection, it is shown that it can be applied in actual industrial sites.