The Know-Net Method

2003 ◽  
pp. 37-57 ◽  
Author(s):  
Gregoris Mentzas ◽  
Dimitris Apostolou ◽  
Andreas Abecker ◽  
Ron Young
Keyword(s):  
Net Method

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

scholarly journals A fixed net method to determine the vertical migration of plankton

1976 ◽  
Vol 21 (6) ◽  
pp. 920-922 ◽  
Author(s):  
Richard V. Bovbjerg ◽  
Judy Freitag ◽  
Dana McHaney
Keyword(s):  
Vertical Migration ◽  
Net Method

Measurement of Spinous Process Angles on Ultrasound Spine Images using HR-Net Method

2021 ◽  
Author(s):  
Wenjie Shao ◽  
Hongye Zeng ◽  
Yuchong Gao ◽  
Kang Zhang ◽  
Rui Zheng
Keyword(s):  
Spinous Process ◽  
Net Method

Sparsity-promoting elastic net method with rotations for high-dimensional nonlinear inverse problem

2019 ◽  
Vol 345 ◽  
pp. 263-282 ◽  
Author(s):  
Yuepeng Wang ◽  
Lanlan Ren ◽  
Zongyuan Zhang ◽  
Guang Lin ◽  
Chao Xu
Keyword(s):  
Inverse Problem ◽  
Elastic Net ◽  
High Dimensional ◽  
Nonlinear Inverse Problem ◽  
Net Method

Pr/t Net Method for Robot Planning

1995 ◽  
pp. 913-921
Author(s):  
Jaegeol Yim ◽  
Tadao Murata
Keyword(s):  
Net Method

scholarly journals Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration

2020 ◽  
Author(s):  
Nada Chaari ◽  
Hatice Camgöz Akdağ ◽  
Islem Rekik
Keyword(s):  
Right Hemisphere ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Network Clustering ◽  
Male And Female ◽  
Clustering Model ◽  
Net Method ◽  
The Right ◽  
Gender Specific

Abstract The estimation of a connectional brain template (CBT) integrating a population of brain networks while capturing shared and differential connectional patterns across individuals remains unexplored in gender fingerprinting. This paper presents the first study to estimate gender-specific CBTs using multi-view cortical morphological networks (CMNs) estimated from conventional T1-weighted magnetic resonance imaging (MRI). Specifically, each CMN view is derived from a specific cortical attribute (e.g. thickness), encoded in a network quantifying the dissimilarity in morphology between pairs of cortical brain regions. To this aim, we propose Multi-View Clustering and Fusion Network (MVCF-Net), a novel multi-view network fusion method, which can jointly identify consistent and differential clusters of multi-view datasets in order to capture simultaneously similar and distinct connectional traits of samples. Our MVCF-Net method estimates a representative and well-centered CBTs for male and female populations, independently, to eventually identify their fingerprinting regions of interest (ROIs) in four main steps. First, we perform multi-view network clustering model based on manifold optimization which groups CMNs into shared and differential clusters while preserving their alignment across views. Second, for each view, we linearly fuse CMNs belonging to each cluster, producing local CBTs. Third, for each cluster, we non-linearly integrate the local CBTs across views, producing a cluster-specific CBT. Finally, by linearly fusing the cluster-specific centers we estimate a final CBT of the input population. MVCF-Net produced the most centered and representative CBTs for male and female populations and identified the most discriminative ROIs marking gender differences. The most two gender-discriminative ROIs involved the lateral occipital cortex and pars opercularis in the left hemisphere and the middle temporal gyrus and lingual gyrus in the right hemisphere.

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