scholarly journals Algorithmization of Reliability-Based Optimization

2014 ◽  
Vol 14 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Ondřej Slowik ◽  
Drahomír Novák
Keyword(s):  
Optimization Method ◽  
Double Loop ◽  
Multilevel Sampling ◽  
Reliability Based Optimization ◽  
Different Levels

Abstract The paper presents newly developed university software FNPO designed for reliability-based optimization. The program works with a newly proposed optimization method called Aimed Multilevel Sampling (AMS) in the optimization cycle of reliability-based optimization. For simulation at different levels of the algorithm AMS and reliability calculations program uses cyclic calls of program FReET - so called double-loop approach. The developed software enables to optimize model of general complexity with consideration of deterministic and/or reliability constraints.

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method

2020 ◽  
Vol 28 (6) ◽  
pp. 1037-1054
Author(s):  
Temitope E. Komolafe ◽  
Qiang Du ◽  
Yin Zhang ◽  
Zhongyi Wu ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Breast Tissue ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Dual Energy ◽  
Hybrid Optimization ◽  
Signal To Noise ◽  
Breast Ct ◽  
Different Levels

BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

A Bayesian Approach to Reliability-Based Optimization With Incomplete Information

2006 ◽  
Author(s):  
Subroto Gunawan ◽  
Panos Y. Papalambros
Keyword(s):  
Incomplete Information ◽  
Piston Ring ◽  
Probability Distributions ◽  
Cylinder Liner ◽  
Optimization Method ◽  
Uncertain Variables ◽  
Additional Information ◽  
Finite Samples ◽  
Estimate Reliability ◽  
Reliability Based Optimization

In engineering design, information regarding the uncertain variables or parameters is usually in the form of finite samples. Existing methods in optimal design under uncertainty cannot handle this form of incomplete information; they have to either discard some valuable information or postulate existence of additional information. In this article, we present a reliability-based optimization method that is applicable when information of the uncertain variables or parameters is in the form of both finite samples and probability distributions. The method adopts a Bayesian Binomial inference technique to estimate reliability, and uses this estimate to maximize the confidence that the design will meet or exceed a target reliability. The method produces a set of Pareto trade-off designs instead of a single design, reflecting the levels of confidence about a design’s reliability given certain incomplete information. As a demonstration, we apply the method to design an optimal piston-ring/cylinder-liner assembly under surface roughness uncertainty.

scholarly journals An Experimental Analysis on Beam Structural with Taguchi Orthogonal Array

2019 ◽  
Vol 8 (12S) ◽  
pp. 441-445
Keyword(s):  
Orthogonal Array ◽  
Optimization Method ◽  
Statistical Technique ◽  
Optimization Techniques ◽  
Beam Structure ◽  
Taguchi Optimization ◽  
Taguchi Optimization Method ◽  
Taguchi Orthogonal Array ◽  
Different Levels

Taguchi optimization method is a statistical technique to optimize the selected factors and will improvise the quality of compositions. The aim of this paper is to define effective optimization techniques to identify the effective orthogonal array combination using experiments in the beam structure. The Taguchi L9 array has experimented in this study with three different levels and four parameters. After the completion of the experiments, the results are compared with fully factorial methods. The output will be in the form of S/N ratio and graphs. The best-optimized combination is found for minimizing the number of experiments. The size of the beam structure is 1250mm*150mm*150mm.

scholarly journals Dictionary adaptation and variational mode decomposition for gyroscope signal enhancement

2020 ◽  
Author(s):  
Krzysztof Brzostowski ◽  
Jerzy Świa̧tek
Keyword(s):  
Great Improvement ◽  
Augmented Lagrangian ◽  
Optimization Method ◽  
Future Research ◽  
Signal Denoising ◽  
Variational Mode Decomposition ◽  
Signal Degradation ◽  
Mode Decomposition ◽  
Almost All ◽  
Different Levels

Abstract The paper proposes an approach to signal denoising based on a combination of Variational Mode Decomposition with the Split Augmented Lagrangian Shrinkage Algorithm. In our research, we found that the proposed approach gives a great improvement of denoising gyroscopic signals. In turn, the results for the synthetic signals are not straightforward. For the bumps synthetic signals, the proposed algorithm gives the best results for different levels of signal degradation. While for the Doppler and blocks synthetic signals the reference methods give better results. However, for heavisine test signal the proposed algorithm gives better results in almost all cases. A weak point of the presented algorithm is its time complexity. The proposed approach is based on the Split Augmented Lagrangian Shrinkage Algorithm, which is the iterative optimization method since the time of computation strongly depends on the number of iterations. The presented results show that the proposed approach gives a great improvement in signal denoising and it is a promising direction of future research.

Reliability-Based Optimization of Spin-Transfer Torque Magnetic Tunnel Junction Implication Logic Gates

2013 ◽  
Vol 854 ◽  
pp. 89-95 ◽  
Author(s):  
Hiwa Mahmoudi ◽  
T. Windbacher ◽  
V. Sverdlov ◽  
S. Selberherr
Keyword(s):  
Tunnel Junction ◽  
Error Probability ◽  
Magnetic Tunnel Junction ◽  
Logic Gates ◽  
Optimization Method ◽  
Spin Transfer Torque ◽  
Boolean Logic ◽  
Stability Factor ◽  
Gate Circuit ◽  
Reliability Based Optimization

Recently, magnetic tunnel junction (MTJ)-based implication logic gates have been proposed to realize a fundamental Boolean logic operation called material implication (IMP). For given MTJ characteristics, the IMP gate circuit parameters must be optimized to obtain the minimum IMP error probability. In this work we present the optimization method and investigate the effect of MTJ device parameters on the reliability of IMP logic gates. It is shown that the most important MTJ device parameters are the tunnel magnetoresistance (TMR) ratio and the thermal stability factor Δ. The IMP error probability decreases exponentially with increasing TMR and Δ.

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