• 한국전자통신학회논문지
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• 제17권1호
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• pp.41-48
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• 2022

본 논문은 통합 베이즈 티코노프 정규화 방법을 총변이 정규화에 대한 해법으로 제시한다. 통합된 방법은 총변이 항을 가중된 티코노프 정규화 항으로 변형하여 정규화 모수를 구하는 공식을 제시한다. 정규화 모수를 구하고 이를 바탕으로 새로운 가중인수를 구하는 것을 복원된 영상이 수렴하기까지 반복한다. 실험결과는 영상 복원 문제에 대하여 제안하는 방법의 효능을 보여준다.

• 한국전자통신학회논문지
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• 제11권11호
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• pp.1129-1134
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• 2016

본 논문은 영상 복원 문제에 대한 정규화 모수를 찾는 새로운 방법을 제시한다. 사전 정보가 없으면 티코노프(Tikhonov) 정규화 모수를 선택하기 위한 일반화된 교차 검증법이나 L자형 곡선 검정 등의 별도의 최적화 함수가 필요하다. 본 논문에서는 티코노프 정규화에 대한 통합된 베이즈 해석을 소개하고 영상 복원 문제에 적용한다. 티코노프 정규화 모수와 베이즈 하이퍼 모수들의 관계를 정립하고 최대 사후 확률과 근거 프레임워크를 사용한 정규화 모수를 구하는 공식을 제시한다. 실험결과는 제안하는 방법의 효능을 보여준다.

• 한국전자통신학회논문지
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• 제15권1호
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• pp.161-166
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• 2020

본 논문은 통합 베이즈 티코노프 정규화 방법을 확장하는 것을 제시한다. 통합된 방법은 티코노프 정규화 모수와 베이즈 하이퍼 모수들의 관계를 정립하고 최대 사후 확률과 근거 프레임워크를 사용한 정규화 모수를 구하는 공식을 제시한다. 데이터 행렬의 차원이 m by n (m >= n)일 때, total misfit는 기존의 m에서 m ± n로 확장된다. 따라서 탐색 범위도 1에서 2n+1개의 정수로 확장된다. 선형 탐색보다는 황금분할 탐색으로 시간을 줄인다. 상대오차를 최적화하는 새로운 벤치마크를 제안하고 이를 목표로 하는 새 모델 선택 판정기준을 소개한다. 실험결과는 영상 복원 문제에 대하여 제안하는 방법의 효능을 보여준다.

• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.311-319
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• 2001

Statistical reconstruction methods in the context of a Bayesian framework have played an important role in emission tomography since they allow to incorporate a priori information into the reconstruction algorithm. Given the ill-posed nature of tomographic inversion and the poor quality of projection data, the Bayesian approach uses regularizers to stabilize solutions by incorporating suitable prior models. In this work we show that, while the quantitative performance of the standard filtered backprojection (FBP) algorithm is not as good as that of Bayesian methods, the application of spline-regularized smoothing to the sinogram space can make the FBP algorithm improve its performance by inheriting the advantages of using the spline priors in Bayesian methods. We first show how to implement the spline-regularized smoothing filter by deriving mathematical relationship between the regularization and the lowpass filtering. We then compare quantitative performance of our new FBP algorithms using the quantitation of bias/variance and the total squared error (TSE) measured over noise trials. Our numerical results show that the second-order spline filter applied to FBP yields the best results in terms of TSE among the three different spline orders considered in our experiments.

• IEIE Transactions on Smart Processing and Computing
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• 제3권5호
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• pp.271-274
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• 2014

Bayesian based Multi-Frame Super-Resolution (MF-SR) has been used as a popular and effective SR model. On the other hand, the texture region is not reconstructed sufficiently because it works on the spatial domain. In this study, the MF-SR method was extended to operate on the frequency domain to improve HF information as much as possible. For this, a spatially weighted bilateral total variation model was proposed as a regularization term for a Bayesian estimation. The experimental results showed that the proposed method can recover the texture region more realistically with reduced noise, compared to conventional methods.

• Smart Structures and Systems
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• 제15권4호
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4209-4214
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• 2022

Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the appropriate architecture of the FFNN-BR with the relevant input features is utilized to learn the modelled ranges and to estimate the new ranges for the new cases. The notable achievements of the proposed approach are: 1- The range of ions in different materials is given as quickly as possible and the time required for estimating the ranges can be neglected (i.e. less than 0.01 s by a typical personal computer). 2- The proposed approach can generalize its ability for estimating the new untrained cases. 3- There is no need for a pre-made lookup table for the estimation of the range values.

• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• 전자공학회논문지
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• 제51권6호
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• pp.209-220
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• 2014

압축센싱은 성긴 (sparse) 신호에 대해 Nyquist rate 미만의 샘플링으로도 신호 획득이 가능하다는 것을 수학적으로 증명한 새로운 개념이다. 그동안 영상분야 압축센싱을 위한 수많은 복원 알고리즘들이 제안되어 왔으나, 낮은 측정률 하에서는 복원 화질 측면에서 아직 개선할 점이 많다. 일례로, 자연 영상의 압축센싱 복원 화질 향상을 위해, 영상과 관련한 사전 정보들로부터 정규화 식을 도출하여 복원에 적용해 볼 수 있을 것이다. 따라서, 본 논문에서는 Dantzig selector 및 평활 필터(가우시안 필터 및 nonlocal 평균 필터)기반의 평활 잔차 오류 정규화 방법을 제안한다. 또한, 복원 영상의 객체 및 배경에서 발생하는 edge 정보를 우수하게 보전하는 것으로 알려진 Total variation 기반 최소화 알고리즘에 적용하여 복원 영상의 화질을 향상시키는 방법을 제안한다. 제안하는 구조는 잔차신호의 평활화를 활용한다는 측면에서 새로운 압축센싱 복원 방식이라고 할 수 있다. 실험 결과, 제안방법은 기존 방법들에 비해 객관적 및 주관적 화질 측면에서 더 높은 성능 향상을 보여주었으며, 특히 기존 Bayesian 압축센싱 복원 방식과 비교 시 최대 9.14 dB 성능이 향상되었다.