Tomographic Imaging of Flames: Assessment of Reconstruction Error Based on Simulated Results

2014 ◽  
Vol 30 (2) ◽  
pp. 350-359 ◽  
Author(s):  
Avishek Guha ◽  
Ingmar M. Schoegl
Keyword(s):  
Reconstruction Error ◽  
Tomographic Imaging

2.5D Algorithm for Tomographic Imaging of the Deep Electromagnetic Geophysical Measurement

PIERS Online ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 214-218
Author(s):  
Aria Abubakar ◽  
Tarek M Habashy ◽  
V. Druskin ◽  
D. Alumbaugh ◽  
P. Zhang ◽  
...  
Keyword(s):  
Tomographic Imaging ◽  
Geophysical Measurement

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

2014 ◽  
Author(s):  
Jeng-Ren Duann ◽  
Tzyy-Ping Jung ◽  
Jin-Chern Chiou
Keyword(s):  
Tomographic Imaging

Kinematic Analysis of the Reconstruction Error of A Calibration Volume for 3d Analysis in Swimming~!2009-07-05~!2009-11-09~!2010-04-20~!

2010 ◽  
Vol 3 (1) ◽  
pp. 28-30 ◽  
Author(s):  
S. Brandao ◽  
P. Figueiredo ◽  
P. Goncalves ◽  
J. P. Vilas-Boas ◽  
R. J. Fernandes
Keyword(s):  
Kinematic Analysis ◽  
Reconstruction Error ◽  
3D Analysis

Tomographic imaging of ionisation in gases

1996 ◽  
Vol 32 (12) ◽  
pp. 1085 ◽  
Author(s):  
C.J. Kotre
Keyword(s):  
Tomographic Imaging

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

scholarly journals Millimeter-Wave Radar Sensor for Automated Tomographic Imaging of Composite Materials in a Manufacturing Environment

2021 ◽  
Vol 5 (3) ◽  
pp. 1-4
Author(s):  
Dominik Meier ◽  
Christian Zech ◽  
Benjamin Baumann ◽  
Bersant Gashi ◽  
Matthias Malzacher ◽  
...  
Keyword(s):  
Composite Materials ◽  
Millimeter Wave ◽  
Tomographic Imaging ◽  
Radar Sensor ◽  
Manufacturing Environment ◽  
Millimeter Wave Radar ◽  
Wave Radar

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

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