An adaptive electrical resistance tomography sensor with flow pattern recognition capability

Wang, Pai; Li, Yang-bo; Wang, Mei; Qin, Xuebin

doi:10.1007/s11771-019-4032-8

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…2) Greedy tracking method In order to overcome the sparse representation problem using l0 norm constraints, the tracking method provides a special way to obtain an approximate sparse solution. The earliest greedy algorithm is MP (Matching Pursuit) [37]. The MP algorithm can directly obtain the representation of the signal sparsity, its essence is realized by calculating the best nonlinear estimation of the signal on the redundant dictionary [38].…”

Section: ) Convex Relaxation Optimization Methodsmentioning

confidence: 99%

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Chen

2020

IEEE Access

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With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.

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Section: ) Convex Relaxation Optimization Methodsmentioning

confidence: 99%

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Chen

2020

IEEE Access

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“…The mathematical model of ERT can be divided into positive and negative problems [6].The positive ERT problem: The conductivity distribution in the sensitive field is known, and the potential distribution in the sensitive field is solved by the boundary conditions. The inverse problem of ERT is: the known boundary measurement voltage, through the image reconstruction algorithm, to reconstruct the distribution of the conductivity in the field of the measured object.…”

Section: Ert Reconstructed Image and Causes Of Distortionmentioning

confidence: 99%

ERT Image Evaluation Based on Sparse Representation Algorithm

Song¹,

Wang²,

Li³

2020

dtetr

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Most of the existing image quality evaluation is to extract image features, and then use support vector regression, sparse representation and other algorithms to evaluate the image quality. In this paper, an adaptive sub-dictionary image evaluation algorithm based on sparse representation is used. By extracting relevant image features such as gradient features and color features, a complete dictionary is first constructed using the features, and then a sparse representation method based on the sub-dictionary is used to obtain the sparse corresponding to the ERT image. Coefficient, the final image quality score is obtained by the corresponding formula.

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“…Electrical resistance tomography (ERT) is a visual measurement technology for two-phase or multiphase flow parameter measurement [1], which reconstructs the dielectric conductivity distribution or dielectric constant distribution in the measurement area through boundary measurement values and image reconstruction algorithm [2]. The two-phase flow pattern will affect the flow characteristics and heat and mass transfer performance, and the measurement of other parameters often depends on the accurate identification of convection pattern [3].…”

Section: Introductionmentioning

confidence: 99%

Multi Classification ERT Flow Pattern Recognition Method Based on Deep Learning

Wang

Miao

2022

J. Phys.: Conf. Ser.

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Electrical resistance tomography (ERT) is the frontier technology of modern industrial detection, in which flow pattern is an important index of two-phase flow detection. Affected by many factors, ERT flow pattern recognition is difficult. In this paper, an ERT flow pattern recognition method based on deep learning is designed in order to obtain the real situation of flow pattern in pipeline in practical application. The original ERT measured voltage is transformed from one-dimensional data information to two-dimensional dot matrix information by pseudo image coding method. According to the characteristics, the flow patterns are divided into 27 categories, and then ERT voltage image databases with different scales are established in time domain and frequency domain. Convolutional neural network is used to construct ERT flow pattern recognition network model based on deep learning, and experiments are designed to verify its performance. The results show that the average accuracy of each flow pattern recognition of this algorithm can reach 98.74%, of which the accuracy of 14 types of flow pattern recognition is 100%. This method can achieve high-precision ERT flow pattern recognition task.

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An adaptive electrical resistance tomography sensor with flow pattern recognition capability

Cited by 7 publications

References 19 publications

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

ERT Image Evaluation Based on Sparse Representation Algorithm

Multi Classification ERT Flow Pattern Recognition Method Based on Deep Learning

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