A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

Lee, Chau Ching; Rahiman, Mohd Hafiz Fazalul; Leow, Pei Ling; Rahim, Ruzairi Abdul; Saad, Fathinul Syahir Ahmad

doi:10.1016/j.flowmeasinst.2022.102234

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can process images to achieve reasonable analysis results. Especially, deep and large neural networks have great learning abilities [3]. But their number of parameters makes calculation difficult.…”

Section: Introductionmentioning

confidence: 99%

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Wang,

Ren,

2023

FCIS

View full text Add to dashboard Cite

The yield of coffee has a significant effect on the development of the economy. It is important to monitor the health status of coffee plants. Leaves can represent the growth of crops. Analysis leaf image is an effective method to monitor crop growth status. With the advancement of artificial intelligence technology, neural networks with strong learning ability have been proposed. They have high accuracy in identifying leaf pests and diseases. However, the structure of these networks is complex and the speed of computing is slow. They are not conducive to real-time analysis. For simple networks, it is difficult to achieve high recognition accuracy directly. To solve this problem, a lightweight model is designed for leaf image analysis. Leaf images are learned by VGG network with pre-trained weights on ImageNet. Use the VGG network as a teacher network. Then design a lightweight student network. Train student network with knowledge distillation method. A lightweight model with high recognition accuracy can be obtained. This research explored the effect of the method on the coffee leaf data set. Experiment proved that the accuracy of the proposed method is 96.73%. The accuracy was 4.29% higher than directly training. Meantime, the calculation speed of the model is quick. The proposed method is of great practical significance for identifying coffee leaf pests and diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Wang,

Ren,

2023

FCIS

View full text Add to dashboard Cite

show abstract

“…Across the monitoring wireless sensor network (WSN), this RTI system practically works by exploiting the attenuation of RF signals caused by the presence of targeted objects. The electric characteristics of biological tissue during electromagnetic radiation exposure determine the biological consequences caused by that radiation [5]- [7]. Determining the effective permittivity of biological tissue has garnered recurrent attention in recent years due to challenges with measuring biological tissue in vivo and its permittivity, which exhibits nonlinear properties in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Initial Study of Radio Tomographic Imaging for Human localization by using Simulation Model

Abdullah,

Rahiman,

Khalid

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper explains the details of modelling the simulation works designin setup for the RTI system. Th simulation modelling using software is focused on the interaction of electromagnetic behaviour in a dielectric medium of human inside a monitoring area. The modelling works have involved the criteria of the human, frequency and number of sensor nodes, dielectric properties of the human and last but not least, the configurations of the Radio tomography imaging (RTI) system. The model is then developed in the software to observe and investigate the result.

show abstract

“…Due to the special transmission mode of dense connections, the training process consumes considerable memory, which directly affects the imaging speed. Lee et al employed a DNN to solve the ill-posed inverse problem caused by the reduction in the sensor model in a tomography system [26].…”

Section: Introductionmentioning

confidence: 99%

One-dimensional structure reparameterized convolutional neural network for two-phase image reconstruction based on ERT

Chao

Zhang

Chen

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Electrical resistance tomography (ERT) can be applied to two-phase flow pattern identification which is a key research direction for improving the operational safety of different industrial equipment systems with complex flow fields. Aiming at the existing problem that the traditional algorithm for defining flow patterns cannot accurately establish the mapping relationship between the measured voltage from ERT system and the two-phase flow conductivity distribution, a novel one-dimensional structure reparameterized convolutional neural network (1D-SRPCNN) algorithm for two-phase flow pattern image reconstruction based on ERT is proposed. First, finite element method and deep learning software framework are used to build dataset and train the neural network model respectively. Second, a deep residual network (ResNet) is used as the main network structure in the algorithm, and the one-dimensional multiscale feature extraction block (1DMSFE-Block) is improved by structural reparameterization. Then, multiscale convolution is introduced to 1DMSFE-Block for extracting features of different receptive field sizes and performing linear fusion, and the predicted two-phase flow conductivity pixel vector is obtained by the feature map passing with three fully connected layers. The results show that 1D-SRPCNN has high reconstruction performance, the average relative image error is 5.15%, the average correlation coefficient is 97.2%, and it has high anti-noise performance and generalization performance. Different experimental data also show that the proposed algorithm has high image reconstruction accuracy and efficiency. The research will provide important theoretical support for accurately identifying two-phase flow patterns in different fields.

show abstract

A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

Cited by 5 publications

References 28 publications

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Initial Study of Radio Tomographic Imaging for Human localization by using Simulation Model

One-dimensional structure reparameterized convolutional neural network for two-phase image reconstruction based on ERT

Contact Info

Product

Resources

About