Optimum Experimental Design by Shape Optimization of Specimens in Linear Elasticity

2018 ◽  
Vol 78 (3) ◽  
pp. 1553-1576 ◽  
Author(s):  
Tommy Etling ◽  
Roland Herzog
2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.


2013 ◽  
Vol 04 (08) ◽  
pp. 789-795 ◽  
Author(s):  
Sayo O. Fakayode ◽  
Ashley M. Taylor ◽  
Maya McCoy ◽  
Sri Lanka Owen ◽  
Whitney E. Stapleton ◽  
...  

2017 ◽  
Vol 25 (5) ◽  
pp. 573-595 ◽  
Author(s):  
Amel Ben Abda ◽  
Emna Jaïem ◽  
Sinda Khalfallah ◽  
Abdelmalek Zine

AbstractThe aim of this work is an analysis of some geometrical inverse problems related to the identification of cavities in linear elasticity framework. We rephrase the inverse problem into a shape optimization one using an energetic least-squares functional. The shape derivative of this cost functional is combined with the level set method in a steepest descent algorithm to solve the shape optimization problem. The efficiency of this approach is illustrated by several numerical results.


1987 ◽  
Vol 71 (455) ◽  
pp. 81
Author(s):  
Rex Watson ◽  
Andrej Pazman

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