• 한국어병학회지
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• 제36권2호
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• pp.409-414
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• 2023

Ellipsoidal-shaped spores with two polar capsules were detected in leather carp (Cyprinus carpio nudus) muscle. 18S rDNA gene analysis of the spore showed a 99.58% match to Myxobolus artus, a myxozoan parasite. As a result of phylogenetic analysis using the Bayesian inference model and maximum likelihood model among other Myxobolus species, the isolate in the present study belonged to the M. artus cluster. This is the first case report of M. artus infection detected in domestic aquaculture organisms in Korea.

• 한국국방경영분석학회지
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• 제33권2호
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• pp.101-113
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• 2007

국방부에서 발표한 '국방개혁에 관한 법률'에 따라 2014년까지 현역병들에 대한 복무기간이 단계적으로 단축될 예정이다. 이에 따라 육군에서는 좀 더 효율적인 직무교육 방안의 일환으로 훈련병들에게 '차등제 교육'을 시행하고 있다. 이러한 차등제 교육의 효과를 향상시키기 위해서는 훈련병들의 예상 학업 성취도를 미리 예측하여 성취집단별로 차별화 된 교육과정을 거치게 하는 것이 매우 중요하다. 따라서 본 연구에서는 입교초기에 얻을 수 있는 신병들의 제한된 자료들만을 이용하여 그들의 예상 교육 성취집단을 예측하는 모형을 개발하였다. 본 모형의 목적 변수는 '성취집단'이며 '일반관리 인원' 및 '집중관리 인원'의 두 가지 값을 갖는다. 사용된 기법은 인공신경망(Neural Network) 모형, 의사결정나무(Decision Tree) 모형, SVM 모형, 그리고 Naive Bayesian모형 등 4가지 순수 모형과, 각각의 순수 모형을 k-means군집기법과 혼합한 4가지의 혼합모형 등 총 8개의 모형의 성능을 비교 분석하였다. 실험 결과 k-means군집기법과 인공신경망 기법을 혼합한 모형이 가장 좋은 예측력을 보이는 것으로 나타났다. 이러한 교육 성취집단 예측 모형은 향후 군에서 이루어지는 다양한 교육 프로그램에 효과적으로 이용될 수 있을 것으로 기대된다.

• 한국지리정보학회지
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• 제20권4호
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• pp.89-101
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• 2017

범죄는 장소나 건축물 용도에 따라 발생빈도와 유형이 다르고, 그 장소를 이용하는 사람들의 특성 및 공간 구조 차이에 의해 다양하게 발생한다. 따라서 공간 및 지역특성을 포함한 공간 빅데이터를 활용하여 지역을 분석해 보면 범죄예방 전략을 마련할 수 있다. 아울러 빅데이터와 지능 정보화시대의 도래에 따라 예측적 경찰활동이 새로운 경찰활동의 패러다임으로 등장하고 있다. 이에 보편적인 지방도시 J시를 대상으로 3개년 동안의 7,420건의 실제 범죄사례를 바탕으로 도시공간의 물리 환경적인 특성을 분석하여 범죄발생공간을 규명하고, 위험지역을 예측해 보고자 하였다. 분석에는 다양한 빅데이터 중 범죄를 유발하는 도시 공간 내 물리 환경적 요소에 한하여 공간 빅데이터를 구축하여 공간회귀분석을 실시하였다. 다음으로 분석결과 도출된 가로폭, 평균 층수, 용적율, 1층 사용용도(제2종 근린생활시설, 상업시설, 유흥시설, 주거시설)을 변수로 베이지안확률 기반 범죄발생 위험성 예측 모형(CIPM: Crime Incident Prediction Model)을 개발하였다. 개발된 모델은 실제 범죄발생 지역과의 중첩분석 및 모델의 정확도를 판단하는 Roc curve 분석을 통해 AUC 값이 0.8로 모델이 적합한 것으로 나타났다. 개발된 모델을 토대로 사례지역의 범죄 위험도를 분석한 결과 범죄발생은 상업 및 유흥시설이 밀집된 지역과 건물층수가 높은 지역, 그리고 상업 및 유흥시설과 주거가 혼재해 있는 블록이 범죄발생 확률이 높은 것으로 나타났다. 본 연구는 단순히 범죄의 공간적 분포와 범죄발생 영향요인을 탐색하는 기존의 연구와 달리 범죄발생 예측모델을 확률론적 관점에서 개발하는 영역으로 한 단계 진전되었다는 점에 의의가 있다.

• 한국임상약학회지
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• 제23권2호
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.65-70
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• 2003

The weight-of-evidence model one of the Bayesian probability model was applied to the task of evaluating landslide susceptibility using GIS. Using the location of the landslides and spatial database such as topography, soil, forest, geology, land use and lineament, the weight-of-evidence model was applied to calculate each factor's rating at Boun area in Korea where suffered substantial landslide damage fellowing heavy rain in 1998, The factors are slope, aspect and curvature from the topographic database, soil texture, soil material, soil drainage, soil effective thickness, and topographic type from the soil database, forest type, timber diameter, timber age and forest density from the forest map, lithology from the geological database, land use from Landsat TM satellite image and lineament from IRS satellite image. Tests of conditional independence were performed for the selection of the factors, allowing the 43 combinations of factors to be analyzed. For the analysis, the contrast value, W$\^$＋/and W$\^$-/, as each factor's rating, were overlaid to map laudslide susceptibility. The results of the analysis were validated using the observed landslide locations, and among the combinations, the combination of slope, curvature, topographic, timber diameter, geology and lineament show the best results. The results can be used for hazard prevention and planning land use and construction

• Communications for Statistical Applications and Methods
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• 제24권4호
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• pp.325-337
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• 2017

In this paper, we proposed a new long-term lifetime distribution with four parameters inserted in a risk competitive scenario with decreasing, increasing and unimodal hazard rate functions, namely the Weibull-Poisson long-term distribution. This new distribution arises from a scenario of competitive latent risk, in which the lifetime associated to the particular risk is not observable, and where only the minimum lifetime value among all risks is noticed in a long-term context. However, it can also be used in any other situation as long as it fits the data well. The Weibull-Poisson long-term distribution is presented as a particular case for the new exponential-Poisson long-term distribution and Weibull long-term distribution. The properties of the proposed distribution were discussed, including its probability density, survival and hazard functions and explicit algebraic formulas for its order statistics. Assuming censored data, we considered the maximum likelihood approach for parameter estimation. For different parameter settings, sample sizes, and censoring percentages various simulation studies were performed to study the mean square error of the maximum likelihood estimative, and compare the performance of the model proposed with the particular cases. The selection criteria Akaike information criterion, Bayesian information criterion, and likelihood ratio test were used for the model selection. The relevance of the approach was illustrated on two real datasets of where the new model was compared with its particular cases observing its potential and competitiveness.

• Journal of Animal Science and Technology
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• 제59권2호
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• pp.3.1-3.7
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• 2017

Background: The modelling of lactation curve provides guidelines in formulating farm managerial practices in dairy cows. The aim of the present study was to determine the suitable non-linear model which most accurately fitted to lactation curves of five lactations in 134 Gir crossbred cows reared in Research-CumDevelopment Project (RCDP) on Cattle farm, MPKV (Maharashtra). Four models viz. gamma-type function, quadratic model, mixed log function and Wilmink model were fitted to each lactation separately and then compared on the basis of goodness of fit measures viz. adjusted $R^2$, root mean square error (RMSE), Akaike's Informaion Criteria (AIC) and Bayesian Information Criteria (BIC). Results: In general, highest milk yield was observed in fourth lactation whereas it was lowest in first lactation. Among the models investigated, mixed log function and gamma-type function provided best fit of the lactation curve of first and remaining lactations, respectively. Quadratic model gave least fit to lactation curve in almost all lactations. Peak yield was observed as highest and lowest in fourth and first lactation, respectively. Further, first lactation showed highest persistency but relatively higher time to achieve peak yield than other lactations. Conclusion: Lactation curve modelling using gamma-type function may be helpful to setting the management strategies at farm level, however, modelling must be optimized regularly before implementing them to enhance productivity in Gir crossbred cows.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1999년도 KOBA 방송기술 워크샵 KOBA Broadcasting Technology Workshop
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• pp.49-56
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• 1999

In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived. The generalized formula is implemented with the plane configuration model and applied to multiple baselined stereograms. The probabilistic plane configuration model consists of independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model reduces the computation and guarantees the discontinuity at the object boundary region. The similarity model preserves the continuity or the high correlation of disparity distribution. In addition, we propose a hierarchical scheme of disparity compensation in the application to multiple-view stereo images. According to the experiments, the derived formula and the proposed estimation algorithm outperformed other ones. The proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n(D)) from O(n(D)4) of the generalized formula. And, the hierarchical scheme of disparity compensation with multiple-view stereos improves the performance without any additional overhead to the decoder.

• Structural Engineering and Mechanics
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• 제72권1호
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• pp.31-41
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• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.