Image Reconstruction for Electrical Impedance Tomography Using Radial Basis Function Neural Network Based on Hybrid Particle Swarm Optimization Algorithm

Wang, Huan; Li, Kai; Wu, Yang; Wang, Song; Zhang, Zheng; Li, Fang; Yao, Jiafeng

doi:10.1109/jsen.2020.3019309

Cited by 31 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hybrid optimization algorithm proposed in this paper, i.e., Newton–Raphson particle swarm optimization (NRPSO), combines the advantages of NRM and PSO in BO-TMA. Interest in hybrid optimization methods has increased over the past few decades [ 14 , 15 , 16 , 17 , 22 , 23 , 24 ]. Early hybridization was mainly done between several metaheuristic algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, a study was conducted to find a combination of usual industrial radioactive sources that achieves the highest precision in dual-energy radiation-based three-phase flow meters, in which a hybrid model, an adaptive neural fuzzy reasoning system (ANFIS) trained through gray wolf optimization (GWO), was used [ 15 ]. In the field of electrical impedance tomography, the quality of the reconstructed image was improved by optimizing the radial basis function neural network (RBFNN) with the hybrid particle swarm optimization (HPSO) algorithm [ 16 ]. A multimodel deep learning (MMDL) framework has been proposed that takes into account the strengths of a deep autoencoder neural network (DeepAEC) and one-dimensional conventional neural network (1D-CNN) to effectively enhance the performance of the recommender system (RS) [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Shi

Choi

2021

Sensors

View full text Add to dashboard Cite

Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton–Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton–Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Shi

Choi

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Because of the importance of EIT in various fields, numerous approaches in solving the inverse problem can be found in the literature [97]- [102]. Several meta-heuristic algorithms were applied to the EIT inverse conductivity problem and produced promising results [94], [103]- [105]. We show how PEOA can also effectively solve the EIT inverse problem.…”

Section: Function Value Errormentioning

confidence: 99%

Philippine Eagle Optimization Algorithm

2022

View full text Add to dashboard Cite

We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.

show abstract

“…They verified the feasibility of the proposed method on real data sets, and the experimental results showed that the performance of their method is better than that of the existing methods. Wang et al [ 25 ] proposed a radial basis function neural network based on hybrid particle swarm optimization algorithm to reconstruct images in EIT, which improved the imaging accuracy and the robustness to noise. Therefore, DNNs may become an effective method for blood flow velocity inversion based on electromagnetic induction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

View full text Add to dashboard Cite

Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

show abstract

Image Reconstruction for Electrical Impedance Tomography Using Radial Basis Function Neural Network Based on Hybrid Particle Swarm Optimization Algorithm

Cited by 31 publications

References 23 publications

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Philippine Eagle Optimization Algorithm

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Contact Info

Product

Resources

About