• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.83-91
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• 2016

Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.181-193
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• 2004

A life cycle cost analysis model for public water supply systems should be different from the ones for other civil and architectural facilities as the operation and the maintenance cost of the water supply systems mainly come from the various mechanical systems and the pipeline systems of the collecting/treating/distributing facilities. This paper presents a cost classification scheme and a probabilistic life cycle cost analysis (PLCCA) model for public water supply systems. A value analysis (VA) procedure that is well suited for practical purposes is also presented. The presented probabilistic life cycle model and the value analysis procedure were applied to a real world project, and this case study is discussed in the paper. The model and the procedure presented in this study can greatly contribute to the value-oriented design alternative selection, the estimation of the maintenance cost, and the allocation of budget for water supply system construction projects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1444-1452
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• 2009

Probabilistic distribution functions based data generation method is very effective. Probabilistic distribution functions are defined under the assumption that daily routine contexts are mainly depended on a time-based schedule. However, daily life contexts are frequently determined by previous contexts because contexts have consistency and/or sequential flows. In order to refect previous contexts effect, a situation propagation network is proposed in this paper. As proposed situation propagation network make parameters of related probabilistic distribution functions update, generated contexts can be more realistic and natural. Through the simulation study, proposed context-data generation model generated general outworker's data about 11 daily contexts at home. Generated data are evaluated with respect to reduction of ambiguity and confliction using newly defined indexes of ambiguity and confliction of sequential contexts. In conclusion, in case of combining situation propagation network with probabilistic distribution functions, ambiguity and confliction of data can be reduced 6.45% and 4.60% respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.435-438
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• 2007

움직이는 객체를 검출하기 위해서 정확한 배경을 사용하기 위해 널리 사용되는 방법으로는 가우시안 혼합 모델이다. 가우시안 혼합 모텔은 확률적 학습 방법을 사용하는데, 이 방법은 움직이는 배경일 경우와 이동하던 물체가 정지하는 경우 배경을 정확히 모델링하지 못한다. 본 논문에서는 확률적 모델링을 통해 혼잡한 배경을 모델링하고 객체의 계층적 처리를 통해 보다 정확한 배경으로 갱신할 수 있는 학습 방법을 제안한다.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.151-160
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• 2003

The stability of the designed rock slope is analysed based on two kinds of shear strength model. Besides the deterministic analysis, a probabilistic approach on Monte Carlo simulation is proposed to deal with the uncertain characteristics of the discontinuity and the results obtained from two models are compared to each other. To carry out the research of characteristics of the discontinuity, BIPS, DOM Scanline survey data and direct shear test data are used, and chi-square test is used for determining the probability distribution function. The rock slope is evaluated to be stable in the deterministic analysis, but in the probabilistic analysis, the probability of failure is more than 5%, so, it is considered that the rock slope is unstable. In the shear strength models, the probability of the failure based on the Mohr-Coulomb model(linear model) is higher than that of the Barton model. It is supported by the fact that the Mohr-Coulomb model is more sensitive to block size than the Barton model. In fact, there is no reliable way to estimate the unit cohesion of the Mohr-Coulomb model except f3r back analysis and in the case of small block failure in the slope, Mohr-Coulomb model may excessively evaluate the factor of the safety. So, the Barton model of which parameters are easily acquired using the geological survey is more reasonable for the stability of the studied slope. Also, the selection of the proper shear strength model is an important factor for slope failure analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.381-389
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• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.556-558
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• 2001

전체 영상을 이용하지 않고 영상 안에 포함된 특정 객체 혹은 영역만을 이용하는 "영역에 의한 질의(query-by-region)" 방법은 내용기반 영상 검색 중 상위개념의 방법 이지만, 영상 분할의 한계, 여러개로 분할된 영역을 모두 검색하기 위한 인덱싱 문제, 유사성 측정 시 선형적으로 분리되지 않는 특징 값들에 대한 무리한 선형 조합으로 인한 검색 오류와 같은 많은 문제점을 안고 있다. 따라서 본 논문에서는 영역 기반 영상 검색 시스템인 FRIP에 대하여 영상 분할의 한계를 극복하고, 사용자의 주관성을 영상 검색에 적용하기 위해 확률적 연관성 학습 모델(MPFRL)을 유사성 측정 단계에서 적용 하였고, 아울러 검색 모델로는 기존에 일반적으로 사용되어 오던, 선형 모델을 사용하지 않고 선형 모델보다 유연한 검색 결과를 보여주는 확률적 이접 부울 모델(PDB)을 사용하였다. 또한, 검색 시간을 단축 시키기 위해, 선형 검색 방법에 부울 AND 연산자를 적용 시킴으로써, 검색 시간을 상당부분 단축 할 수 있었다. 실험 결과, 본 논문에서 제안하는 방법(MPFRL+PDB)을 사용할 경우 검색 결과가 선형 조합 보다 향상되는 것을 알 수 있었다. 아울러 사용자 피드백을 통해 사용자가 특징 가중치를 일일이 조절하지 않더라도 단순한 몇 번의 클릭만으로 사용자의 주관성을 반영하고 보다 정확한 검색 결과를 보여 줄 수 있는 시스템을 설계 할 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.163-172
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• 1993

A probabilistic approach for evaluation of prediction of the strains using Lade's single surface constitutive model was employed, based on first-order approximate mean and variance. Several experiments such as isotropic compression and drained triaxial compression tests were conducted to examine the variabilities of soil parameters for Lade's model. By taking into account the results of the experimental data such as mean values and standard deviations of soil parameter's, a new probabilistic approach, which explains the uncertainty of computed strains, is applied. The magnitude of the COV for each parameter and the correlation coefficient between the two parameters can be effectively used for reducing the number of the parameters for the model. It is concluded that Lade's single surface constitutive model is surperior model for the prediction of the strain, because the COV of strains is under the "0.51".

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.447-458
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• 2023

In this study, a comparison model considering the stochastic characteristics of the load and member resistance of the shield tunnel segment lining as well as the variability of the boundary condition was selected and reliability analysis was performed, and the adequacy of the limit state design was analyzed by calculating the probability of failure and reviewing the structural safety. For the analysis considering the probability characteristics of these ground constants, the ground spring coefficient was considered as the mean value by calculating the quantitative value by applying the Muirwood formula, and the coefficient of variation was selected based on the existing research data to review the models according to the change of ground boundary conditions. Through the structural analysis of these models and the reliability analysis using MCS technique, the failure probability and reliability index were calculated to examine the changes in the failure probability due to changes in ground boundary conditions.