Resonance affected scattering: Comparison of two hybrid methods involving filter diagonalization and the Lanczos method

1998 ◽  
Vol 109 (13) ◽  
pp. 5177-5186 ◽  
Author(s):  
Drew A. McCormack ◽  
Geert-Jan Kroes ◽  
Daniel Neuhauser
Keyword(s):  
Hybrid Methods ◽  
Lanczos Method ◽  
Filter Diagonalization ◽  
Two Hybrid

Apply two hybrid methods on the rainfall-induced landslides interpretation

2011 ◽  
Author(s):  
Kuan-Tsung Chang ◽  
Jin-Tsong Hwang ◽  
Jin-King Liu ◽  
Edward-Hua Wang ◽  
Chu-I Wang
Keyword(s):  
Hybrid Methods ◽  
Two Hybrid

A Diameter Distribution Yield Prediction for Teak Stands in Taungoo District, Bago Region of Myanmar

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Yan Myo Naing
Keyword(s):  
Distribution Function ◽  
Hybrid Methods ◽  
Diameter Distribution ◽  
Yield Prediction ◽  
Parameter Recovery ◽  
Moment Methods ◽  
Yield Table ◽  
Error Index ◽  
Two Hybrid ◽  
Recovery Procedure

A parameter recovery procedure was applied to characterize the parameters for the Weibull distribution function based on four percentile methods and two hybrid methods which were the combination of diameter percentiles and moment methods. The procedure was used to develop a diameter distribution yield prediction for teak stands in Taungoo District of Myanmar. All the methods were evaluated by using independent observed data and calculating error indices. Among them, method 1 which involved the 31st and 63rd diameter percentiles produced the lowest error index. Therefore, method 1 was considered to predict yield based on  diameter distribution and selected to construct a yield table for the study area. An example was also provided to show users how to apply this kind of yield prediction.

scholarly journals COMPARISON OF HYBRID METHODS WITH DIFFERENT META MODEL USED IN BRIDGE MODEL-UPDATING

2017 ◽  
Vol 12 (3) ◽  
pp. 193-202 ◽  
Author(s):  
Zhiyuan Xia ◽  
Aiqun Li ◽  
Jianhui Li ◽  
Maojun Duan
Keyword(s):  
Neural Network ◽  
Model Updating ◽  
Hybrid Methods ◽  
Back Propagation ◽  
Element Model ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Meta Model ◽  
The One ◽  
Two Hybrid

Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.

Using Yeast Two-Hybrid Methods to Investigate Large Numbers of Binary Protein Interactions

Genomics ◽  
2010 ◽  
pp. 173-189
Author(s):  
Panagoula Charalabous ◽  
Jonathan Woodsmith ◽  
Christopher M. Sanderson
Keyword(s):  
Protein Interactions ◽  
Hybrid Methods ◽  
Yeast Two Hybrid ◽  
Large Numbers ◽  
Two Hybrid
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