• Computational Structural Engineering
• /
• v.1 no.1
• /
• pp.79-85
• /
• 1988

The inverse algorithm of Point-Fitted Paraboloid Approximation is derived and used in reliability-based calculations. The algorithm of Reliability- Conditioned method is modified in the calculation of failure points such that nonlinear performance functions can be treated in like manner as linear cases without new formulations. SOSM/RC combined method results in probability of failure closed to specified one, and partial safety factors become nearly constant for a wide range of load ratio.

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.256-261
• /
• 2007

본 논문은 이미지 완성(Image Completion)을 위한 근사적 에너지 최적화 알고리즘을 제안한다. 이미지 완성이란 이미지의 특정영역이 지워진 상태에서, 그 지워진 부분을 나머지 부분과 시각적으로 어울리도록 완성시키는 기법을 말한다. 본 논문에서 이미지 완성은 유사-확률적(pseudo-probabilistic) 시스템인 Markov Random Field로 모델링된다. MRF로 모델링된 이미지 완성 시스템에서 사후 확률(posterior probability)을 최대로 만드는 MAP(Maximum A Posterior) 문제는 결국 시스템의 전체 에너지를 낮추는 에너지 최적화 문제와 동일하다. 본 논문에서는 MRF의 최적화 알고리즘들 중에서 Belief Propagation 알고리즘을 이용한다. BP 알고리즘이 이미지 완성 분야에 적용될 때 다음 두 가지가 계산시간을 증가시키는 요인이 된다. 첫 번째는 완성시킬 영역이 넓어 MRF를 구성하는 정점의 수가 증가할 때이다. 두 번째는 비교할 후보 이미지 조각의 수가 증가할 때이다. 기존에 제안된 Priority-Belief Propagation 알고리즘은 우선순위가 높은 정점부터 메시지를 전파하고 불필요한 후보 이미지 조각의 수를 제거함으로써 이를 해결하였다. 하지만 우선순위를 정점에 할당하기 위한 최초 메시지 전파의 경우 Belief Propagation의 단점은 그대로 남아있다. 이를 개선하기 위해 본 논문에서는 이미지 완성을 위한 MRF 모델을 피라미드 구조와 같이 층위로 나누어 정점의 수를 줄이고, 계층적으로 메시지를 전파하여 시스템의 적합성(fitness)을 정교화 해나가는 Hierarchical Priority Belief Propagation 알고리즘을 제안한다.

• Communications for Statistical Applications and Methods
• /
• v.17 no.6
• /
• pp.853-863
• /
• 2010

The performance test in a public survey is conducted by a sample survey and the sampling rate of the performance test is a very important factor in the test process. Since the current sampling rate was decided empirically at an earlier time, it has been criticized for two points: the first is that it has a lack of a theoretical background on the decision for the sampling rate and the second is that the sampling rate should be improved in accordance with current test situations. In this paper, we review the present state of performance tests in public surveys in Korea and study the relationship between the rate of the performance test and fitness probability, number of tests, and the success rate in order to create a theoretical background to improve the test rate. In addition, we discuss relationship between the test rate and cost in the performance test.

• The Korean Journal of Applied Statistics
• /
• v.29 no.2
• /
• pp.291-299
• /
• 2016

This paper discusses how to establish estimable functions when there are fixed and random effects in design models. It proves that estimable functions of mixed models are not related to random effects. A fitting constants method is used to obtain sums of squares due to random effects and Hartley's synthesis is used to calculate coefficients of variance components. To test about the fixed effects the degrees of freedom associated with divisor are determined by means of the Satterthwaite approximation.

• Journal of Korea Water Resources Association
• /
• v.42 no.3
• /
• pp.227-234
• /
• 2009

In this study, cross correlations among sample data at each site are calculated to obtain the asymptotic cross correlations among probability weighted moments at each site using Monte Carlo simulation. As a result, the relations between the asymptotic cross correlations among probability weighted moments and the inter-site dependence among sample data at each site are nearly a linear relation with slope 1. The smaller ratio of concurrent data size to entire sample size is, the weaker the relationship grows. Simple power function which the correction term in power function accounts for the differences of the sample size between two sites was fitted to each case to estimate the parameter. It is noted that this result can be used in the various researches which include the estimation of the variance of quantile considering cross correlations.

• Journal of KIISE:Software and Applications
• /
• v.30 no.3_4
• /
• pp.326-338
• /
• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.5 no.6
• /
• pp.55-64
• /
• 2001

Industrial machines are sometimes exposed to the danger of earthquake. In the design of a mechanical system, this factor should be accounted for from the viewpoint of reliability to analyze a complex nonlinear structure system under random excitation is proposed. First, the actual random excitation, such as earthquake, is approximated to the corresponding Gaussian process for the statistical analysis. The modal equations of overall system are expanded sequentially. Then, the perturbed equations are synthesized into the overall system and solved in probabilistic way. Several statistical properties of a random process that are of interest in random vibration are evaluated in each substructure. Comparing with the results of the numerical simulation proved the efficiency of the proposed method.

• Environmental and Resource Economics Review
• /
• v.12 no.3
• /
• pp.453-467
• /
• 2003

Approximating a WTP distribution of the conservation for Hallyue Marine National Park is complicated by zero observations in the sample. To deal with the zero observations, a mixture model is considered to allow a point mass at zero. The model is empirically verified for the data. The conventional model and a spike model are also considered for comparison. Our results portrays the usefulness of the mixture model to analyze SBDC data with zero observations.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.7
• /
• pp.1234-1243
• /
• 1992

A systematic procedure to design fuzzy PID controllers is developed in this paper. The concept of local fuzzy control cell is proposed by introducing both an adequate global control rule and membership functions to simplify a fuzzy logic controller. Fuzzy decision is made by using algebraic product and parallel firing arithematic mean, and a defuzzification strategy is adopted for improving the computational efficiency based on nonfuzzy micro-processor. A direct method, transforming the typical output of quasi-linear fuzzy operator to the digital compensator of PID form, is also proposed. Finally, the proposed algorithm is applied to an DC-servo motor. It is found that this algorithm is systematic and robust through computer simulations and implementation of controller using Intel 8097 micro-processor.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.9
• /
• pp.1060-1069
• /
• 2019

Pedestrian-based camera self-calibration methods are suitable for video surveillance systems since they do not require complex calibration devices or procedures. However, using arbitrary pedestrians as calibration targets may result in poor calibration accuracy due to the unknown height of each pedestrian. To solve this problem in the real surveillance environments, this paper proposes a novel Bayesian approach. By assuming known statistics on the height of pedestrians, we construct a probabilistic model that takes into account uncertainties in both the foot/head locations and the pedestrian heights, using foot-head homology. Since solving the model directly is infeasible, we use variational Bayesian inference, an approximate inference algorithm. Accordingly, this makes it possible to estimate the height of pedestrians and to obtain accurate camera parameters simultaneously. Experimental results show that the proposed algorithm is robust to noise and provides accurate confidence in the calibration.