• 노동경제논집
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• 제36권3호
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• pp.29-64
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• 2013

본 논문은 2000년대 임금밀도함수의 변화를 추정하고, 근로자 및 일자리 구성의 변화 등을 이용하여 구성 분해한 결과를 제시한다. 2000년대 변화의 특징은 임금 중위층의 위축, 상위층의 대폭 확대, 하위층의 소폭 확대이며, 이 가운데 1/3 정도를 근로자 구성 변화가 설명하지만, 일자리 구성 변화는 상대적으로 설명력이 약한 것으로 나타났다. 따라서 근로자 구성에 의해 설명되지 못하는 나머지 변화들은 일자리 유형 내에서 고기능 수요의 확대에 공급이 충분히 대응하지 못함을 반영하는 것으로 판단된다. 이러한 양상은 제조업, 대기업, 노조부문, 생산직 등에서 두드러지는데, 즉 이 부문들에서의 수요 변화가 전반적인 임금분포의 변화를 주도하고 있다고 판단된다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
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• pp.357-365
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• 2003

The major problems with the conventional neural network, especially Back Propagation Neural Network, arise from the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, the PNN is used as a pattern classifier to detect the damage of structure to avoid those drawbacks of the conventional neural network. In the PNN-based pattern classification problems, the probability density function for patterns is usually assumed by Gaussian distribution. But, in this paper, several probability density functions are investigated in order to select the most approriate one for structural damage assessment.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
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• pp.340-343
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• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2000년도 추계학술발표논문집
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• pp.250-255
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• 2000

This paper is concerning off-line signature verification using a density function which is obtained by convolving the signature image with twelve-directional 5$\times$5 gradient masks and the weighted fuzzy mean classifier. The twelve-directional density function based on Nevatia-Babu template gradient is related to the overall shape of a signature image and thus, utilized as a feature set. The weighted fuzzy mean classifier with the reference feature vectors extracted from only genuine signature samples is evaluated for the verification of freehand forgeries. The experimental results show that the proposed system can classify a signature whether genuine or forged with more than 98% overall accuracy even without any knowledge of vaned freehand forgeries.

• The Journal of the Acoustical Society of Korea
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• 제17권4E호
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• pp.3-10
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• 1998

This paper concerns the dynamical behavior, in probabilistic sense, of a feedforward neural network performing auto association for novelty. Networks of retinotopic topology having a one-to-one correspondence between and output units can be readily trained using back-propagation algorithm, to perform autoassociative mappings. A novelty filter is obtained by subtracting the network output from the input vector. Then the presentation of a "familiar" pattern tends to evoke a null response ; but any anomalous component is enhanced. Such a behavior exhibits a promising feature for enhancement of weak signals in additive noise. As an analysis of the novelty filtering, this paper shows that the probability density function of the weigh converges to Gaussian when the input time series is statistically characterized by nonsymmetrical probability density functions. After output units are locally linearized, the recursive relation for updating the weight of the neural network is converted into a first-order random differential equation. Based on this equation it is shown that the probability density function of the weight satisfies the Fokker-Planck equation. By solving the Fokker-Planck equation, it is found that the weight is Gaussian distributed with time dependent mean and variance.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.122-136
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• 1995

The objective of this research is to investigate the problem of goodness-of-fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large sample properties of a new test statistic $\hat{\lambda_a}$ is investigated. The test statistic $\hat{\lambda_a}$ is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function th the event that $H_0$ is rejected. The limiting distribution of $\hat{\lambda_a}$ is obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

• 소성∙가공
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• 제1권1호
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• pp.33-41
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• 1992

Sintered P/M connecting rod is forged to increase density and to satisfy dimensional specifications. Flow of the materials is different form that of wrought materials due to pores in the preform. The Mises yield function was modified to. include the first invariant of stress tensor, and the associated flow rule was derived by applying the normality rule to the yield function. Axisymmetric and plane-strain finite element analyes were carried out for the ring and beam portions of the connecting rod, respectively. The flow of the preform and density change of the analysis are presented in this paper. A load-stroke curve was also presented by superimposing analysis results for the ring and beam portions.

• 설비공학논문집
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• 제1권2호
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• pp.162-172
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• 1989

The steady one dimensional heat flow method was used for the measurement of thermal conductivity of kaolin. The effects of the classification, density and moisture content on the thermal conductivity were studied experimentally for the 9 classes of kaolin in Korea. As the results of this study, it was found that the classification did not effect the thermal conductivity, and the conductivity increased as the density and moisture content increased. The correlation equation of the thermal conductivity as a function of the density increase rate was found and the values for the thermal conductivity as a function of moisture content were recommended.

• Computers and Concrete
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• 제8권2호
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• pp.163-176
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• 2011

By virtue of chord-length density function from the field of statistical physics, this paper introduced a quantitative approach to estimate the distribution of cement paste thickness between aggregates in concrete. Dynamics mixing method based on molecular dynamics was employed to generate one model structure, then image analysis algorithm was used to obtain the distribution of thickness of cement paste in model structure for the purpose of verification. By comparison of probability density curves and cumulative probability curves of the cement paste thickness among neighboring aggregates, it is found that the theoretical results are consistent with the simulation. Furthermore, for the model mortar and concrete mixtures with practical volume fraction of Fuller-type aggregate, this analytical formula was employed to predict the influence of aggregate volume fraction and aggregate fineness. And evolution of its mean values were also investigated with the variation of volume fraction of aggregate as well as the fineness of aggregates in model mortars and concretes.

• 대한전자공학회논문지SP
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• 제42권1호
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• pp.51-57
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• 2005

본 논문에서는 실시간 처리론 위한 적응형 콘트라스트 조정 기법을 제안하였다. 제안된 방식은 과도한 영상의 밝기 변화를 제어하기 위하여 활률밀도함수(PDF: Probability Density Function)를 이용하였다. 또한 제안된 알고리즘은 처리된 영상에 영향을 주지 않으면서 최대 콘트라스트를 얻을 수 있었다. 하드웨어의 복잡성을 감소하기 위하여 누적분포함수(CDF: Cumulative Density Function)의 샘플 값을 이용한 선형화 방법을 이용하였다. 제안된 방식에 의한 처리 결과와 원 영상의 화질 평가를 위하여 시각적 검증과 히스토그램 편차를 도입하였다.