• Computational Structural Engineering
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• v.4 no.2
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• pp.87-94
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• 1991

In this paper, a mixed variational principle applicable to the linear elasticity of inhomogeneous anisotropic materials is presented. For derivation of the general variational principle, a systematic procedure for the variational formulation of linear coupled boundary value problems developed by Sandhu et al. is employed. Consistency condition of the field operators with the boundary operators results in explicit inclusion of boundary conditions in the governing functional. Extensions of admissible state function spaces and specialization to a certain relation in the general governing functional lead to the desired mixed variational principle. In the physical sense, the present variational principle is analogous to the Reissner's recent formulation obtained by applying Lagrange multiplier technique followed by partial Legendre transform to the classical minimum potential energy principle. However, the present one is more advantageous for the application to the general anisotropic materials since Reissner's principle contains an implicit function which is not easily converted to an explicit form.

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

• Journal of the korean Society of Automotive Engineers
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• v.11 no.1
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• pp.14-19
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• 1989

본 해설에서는 이와같은 SOlution-Adaptive-Grid 방법중 1982년 Blackbill과 Salzman에 의하여 제안된 변분법을 이용한 Solution Adaptive Grid 방법에 대한 기본방정식 그리고 계산방법들을 소개하고자 한다. 이 방법은 논리적 구조에 있어서 명확하고 2차원 3차원 문제에 쉽게 확장시켜 적용할 수 잇기 때문에 많은 연구자들에 의하여 채택되고 있다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1196-1202
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• 2004

A meshfree multi-scale method has been presented for efficient analysis of elasto-plastic problems. From the variational principle, problem is decomposed into a fine scale and a coarse scale problem. In the analysis only the plastic region is discretized using fine scale. Each scale variable is approximated using meshfree method. Adaptivity can easily and nicely be implemented in meshree method. As a method of increasing resolution, partition of unity based extrinsic enrichment is used. Each scale problem is solved iteratively. Iteration procedure is indispensable for the elasto-plastic deformation analysis. Therefore this kind of solution procedure is adequate to that problem. The proposed method is applied to Prandtl's punch test and shear band problem. The results are compared with those of other methods and the validity of the proposed method is demonstrated.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.10-15
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• 2011

Histogram equalization is an effective method to enhance the contrast of the image. However, it can result in unwanted artifacts such as excessive contrast enhancement and noise amplification. These artifacts can be reduced by modifying an intensity mapping function which is generated by histogram equalization. In this paper, we present a variational approach to the modification of the intensity mapping function. We define a functional whose minimization produces a modified intensity mapping function. Experimental results show that the intensity mapping function obtained by the proposed method can enhance the contrast of the image without visual artifacts.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.3 no.1
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• pp.33-41
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• 1992

In this paper the propagation problems of waves nomally incident upon an anisotropic medium with arbitrary permittivity tensors are analyzed through the variational finite element method. First, a variational equation is derived from the new approach basd on the induction theorm, reactions, and reciprocity. Next, by using the finite element method, the propagation problems are solved from the obtained functional. Specially, the reflection and transmission coefficient and axis ratio are obtained on the case of normally incident upon a homogeneous and inhomogeneous anisotropic medium such as cold mgnetoplasma slab and showed agreement with those of the previous method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.578-586
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• 2021

We propose a semi-supervised learning method based on Variational AutoEncoder (VAE) and Unsupervised Data Augmentation (UDA) to improve the performance of an end-to-end speech recognizer. In the proposed method, first, the VAE-based augmentation model and the baseline end-to-end speech recognizer are trained using the original speech data. Then, the baseline end-to-end speech recognizer is trained again using data augmented from the learned augmentation model. Finally, the learned augmentation model and end-to-end speech recognizer are re-learned using the UDA-based semi-supervised learning method. As a result of the computer simulation, the augmentation model is shown to improve the Word Error Rate (WER) of the baseline end-to-end speech recognizer, and further improve its performance by combining it with the UDA-based learning method.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1071-1083
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• 2005

By estimating probability distributions of the good solutions in the current population, some researchers try to find the optimal solution more efficiently. Particularly, finite mixtures of distributions have a very useful role in dealing with complex problems. However, it is difficult to choose the number of components in the mixture models and merge superior partial solutions represented by each component. In this paper, we propose a new continuous evolutionary optimization algorithm with distribution estimation by variational Bayesian mixtures of factor analyzers. This technique can estimate the number of mixtures automatically and combine good sub-solutions by sampling new individuals with the latent variables. In a comparison with two probabilistic model-based evolutionary algorithms, the proposed scheme achieves superior performance on the traditional benchmark function optimization. We also successfully estimate the parameters of S-system for the dynamic modeling of biochemical networks.

• Computational Structural Engineering
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• v.7 no.2
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• pp.147-153
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• 1994

A method for shape design sensitivity analysis for axisymmetric shells of general shapes is developed. The basic approach is to divide the structures into many segments : For each of the segments, the formula for a shallow arch or shell can be applied and the results assembled. To interconnect those segments, the existing sensitivity formula, obtained for a variation only in the direction perpendicular to the plane on which the structure is mapped, has been extended to include a variation normal to the middle surface. The method follows the adjoint variable approach based on the material derivative concept as established in the literature. Numerical examples are taken to illustrate the method and the applicability to practical design problems.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.1-13
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• 2020

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.