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Guo X, Wang Z, Wu P, Li Y, Alsaadi FE, Zeng N. ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information. Comput Biol Med 2024; 169:107879. [PMID: 38142549 DOI: 10.1016/j.compbiomed.2023.107879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
The liver is one of the organs with the highest incidence rate in the human body, and late-stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of liver cancer are of important clinical value. This study proposes an enhanced network architecture ELTS-Net based on the 3D U-Net model, to address the limitations of conventional image segmentation methods and the underutilization of image spatial features by the 2D U-Net network structure. ELTS-Net expands upon the original network by incorporating dilated convolutions to increase the receptive field of the convolutional kernel. Additionally, an attention residual module, comprising an attention mechanism and residual connections, replaces the original convolutional module, serving as the primary components of the encoder and decoder. This design enables the network to capture contextual information globally in both channel and spatial dimensions. Furthermore, deep supervision modules are integrated between different levels of the decoder network, providing additional feedback from deeper intermediate layers. This constrains the network weights to the target regions and optimizing segmentation results. Evaluation on the LiTS2017 dataset shows improvements in evaluation metrics for liver and tumor segmentation tasks compared to the baseline 3D U-Net model, achieving 95.2% liver segmentation accuracy and 71.9% tumor segmentation accuracy, with accuracy improvements of 0.9% and 3.1% respectively. The experimental results validate the superior segmentation performance of ELTS-Net compared to other comparison models, offering valuable guidance for clinical diagnosis and treatment.
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Affiliation(s)
- Xiaoyue Guo
- College of Engineering, Peking University, Beijing 100871, China; Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fujian 350116, China; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fujian 350116, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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2
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Luo Y, Wang Z, Dong H, Mao J, Alsaadi FE. A novel sequential switching quadratic particle swarm optimization scheme with applications to fast tuning of PID controllers. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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3
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Xie T, Wang Z, Li H, Wu P, Huang H, Zhang H, Alsaadi FE, Zeng N. Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. Comput Biol Med 2023; 159:106947. [PMID: 37099976 PMCID: PMC10116157 DOI: 10.1016/j.compbiomed.2023.106947] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/15/2023] [Indexed: 04/28/2023]
Abstract
In this paper, a novel deep learning-based medical imaging analysis framework is developed, which aims to deal with the insufficient feature learning caused by the imperfect property of imaging data. Named as multi-scale efficient network (MEN), the proposed method integrates different attention mechanisms to realize sufficient extraction of both detailed features and semantic information in a progressive learning manner. In particular, a fused-attention block is designed to extract fine-grained details from the input, where the squeeze-excitation (SE) attention mechanism is applied to make the model focus on potential lesion areas. A multi-scale low information loss (MSLIL)-attention block is proposed to compensate for potential global information loss and enhance the semantic correlations among features, where the efficient channel attention (ECA) mechanism is adopted. The proposed MEN is comprehensively evaluated on two COVID-19 diagnostic tasks, and the results show that as compared with some other advanced deep learning models, the proposed method is competitive in accurate COVID-19 recognition, which yields the best accuracy of 98.68% and 98.85%, respectively, and exhibits satisfactory generalization ability as well.
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Affiliation(s)
- Tingyi Xie
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Huixiang Huang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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Liu M, Wang Z, Li H, Wu P, Alsaadi FE, Zeng N. AA-WGAN: Attention augmented Wasserstein generative adversarial network with application to fundus retinal vessel segmentation. Comput Biol Med 2023; 158:106874. [PMID: 37019013 DOI: 10.1016/j.compbiomed.2023.106874] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In this paper, a novel attention augmented Wasserstein generative adversarial network (AA-WGAN) is proposed for fundus retinal vessel segmentation, where a U-shaped network with attention augmented convolution and squeeze-excitation module is designed to serve as the generator. In particular, the complex vascular structures make some tiny vessels hard to segment, while the proposed AA-WGAN can effectively handle such imperfect data property, which is competent in capturing the dependency among pixels in the whole image to highlight the regions of interests via the applied attention augmented convolution. By applying the squeeze-excitation module, the generator is able to pay attention to the important channels of the feature maps, and the useless information can be suppressed as well. In addition, gradient penalty method is adopted in the WGAN backbone to alleviate the phenomenon of generating large amounts of repeated images due to excessive concentration on accuracy. The proposed model is comprehensively evaluated on three datasets DRIVE, STARE, and CHASE_DB1, and the results show that the proposed AA-WGAN is a competitive vessel segmentation model as compared with several other advanced models, which obtains the accuracy of 96.51%, 97.19% and 96.94% on each dataset, respectively. The effectiveness of the applied important components is validated by ablation study, which also endows the proposed AA-WGAN with considerable generalization ability.
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Geng H, Wang Z, Hu J, Alsaadi FE, Cheng Y. Outlier-Resistant Sequential Filtering Fusion for Cyber-Physical Systems with Quantized Measurements under Denial-of-Service Attacks. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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6
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Zhang Y, Wu P, Li H, Liu Y, Alsaadi FE, Zeng N. DPF-S2S: A novel dual-pathway-fusion-based sequence-to-sequence text recognition model. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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7
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Song Q, Yang L, Liu Y, Alsaadi FE. Stability of quaternion-valued neutral-type neural networks with leakage delay and proportional delays. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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8
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Li H, Wu P, Wang Z, Mao J, Alsaadi FE, Zeng N. A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis. Comput Biol Med 2022; 151:106265. [PMID: 36401968 DOI: 10.1016/j.compbiomed.2022.106265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. To build a highly generalized computer-aided diagnosis (CAD) system, an information refinement unit employing depth- and point-wise convolutions is meticulously designed, where a dual-domain attention mechanism is adopted to focus primarily on the important areas. By deploying a residual fusion unit, context information is further integrated to extract highly discriminative features with strong representation ability. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, which has achieved average sensitivity, specificity, precision, accuracy and F1 score of 0.9992, 0.9998, 0.9992, 0.9997 and 0.9992 in a five-class cancer detection task, and in comparison to some other advanced deep learning models, above indicators have been improved by 1.23%, 0.31%, 1.24%, 0.5% and 1.26%, respectively. Moreover, the proposed FLE-CNN provides satisfactory results in three important diagnosis, which further validates that FLE-CNN is a competitive CAD model with high generalization ability.
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Affiliation(s)
- Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Jingfeng Mao
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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Wan Z, Yang R, Huang M, Alsaadi FE, Sheikh MM, Wang Z. Segment alignment based cross-subject motor imagery classification under fading data. Comput Biol Med 2022; 151:106267. [PMID: 36356391 DOI: 10.1016/j.compbiomed.2022.106267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/06/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022]
Abstract
Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.
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Affiliation(s)
- Zitong Wan
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Rui Yang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
| | - Mengjie Huang
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Muntasir M Sheikh
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
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10
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Chai L, Wang Z, Chen J, Zhang G, Alsaadi FE, Alsaadi FE, Liu Q. Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach. Comput Biol Med 2022; 150:105985. [PMID: 36137319 DOI: 10.1016/j.compbiomed.2022.105985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/05/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
Abstract
In recent years, deep learning (DL) has been recognized very useful in the semantic segmentation of biomedical images. Such an application, however, is significantly hindered by the lack of pixel-wise annotations. In this work, we propose a data pair generative adversarial network (DPGAN) for the purpose of synthesizing concurrently the diverse biomedical images and the segmentation labels from random latent vectors. First, a hierarchical structure is constructed consisting of three variational auto-encoder generative adversarial networks (VAEGANs) with an extra discriminator. Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In such a way, a large number of new segmentation data pairs are generated from random latent vectors and then used to augment the original data sets. Finally, to validate the effectiveness of the proposed DPGAN, experiments are carried out on a vestibular schwannoma data set, a kidney tumor data set and a skin cancer data set. The results indicate that, in comparison to other state-of-the-art GAN-based methods, the proposed DPGAN shows better performance in the generative quality, and meanwhile, gains an effective boost on semantic segmentation of class imbalanced biomedical images.
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Affiliation(s)
- Lu Chai
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Jianqing Chen
- Department of Otolaryngology, Head & Neck Surgery, Shanghai Ninth People's Hospital, Shanghai 200041, China
| | - Guokai Zhang
- Department of Computer Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Fawaz E Alsaadi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Qinyuan Liu
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.
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11
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Liu Y, Liu H, Xue C, Alotaibi ND, Alsaadi FE. State estimate via outputs from the fraction of nodes for discrete-time complex networks with Markovian jumping parameters and measurement noise. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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Alsaadi FE, Wang Z, Luo Y, Alharbi NS, Alsaade FW. H ∞ State Estimation for BAM Neural Networks With Binary Mode Switching and Distributed Leakage Delays Under Periodic Scheduling Protocol. IEEE Trans Neural Netw Learn Syst 2022; 33:4160-4172. [PMID: 33587713 DOI: 10.1109/tnnls.2021.3055942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article is concerned with the H∞ state estimation problem for a class of bidirectional associative memory (BAM) neural networks with binary mode switching, where the distributed delays are included in the leakage terms. A couple of stochastic variables taking values of 1 or 0 are introduced to characterize the switching behavior between the redundant models of the BAM neural network, and a general type of neuron activation function (i.e., the sector-bounded nonlinearity) is considered. In order to prevent the data transmissions from collisions, a periodic scheduling protocol (i.e., round-robin protocol) is adopted to orchestrate the transmission order of sensors. The purpose of this work is to develop a full-order estimator such that the error dynamics of the state estimation is exponentially mean-square stable and the H∞ performance requirement of the output estimation error is also achieved. Sufficient conditions are established to ensure the existence of the required estimator by constructing a mode-dependent Lyapunov-Krasovskii functional. Then, the desired estimator parameters are obtained by solving a set of matrix inequalities. Finally, a numerical example is provided to show the effectiveness of the proposed estimator design method.
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Alsaadi FE, Liu Y, Alharbi NS. Design of robust H∞ state estimator for delayed polytopic uncertain genetic regulatory networks: Dealing with finite-time boundedness. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Alsaadi FE, Wang Z, Alharbi NS, Liu Y, Alotaibi ND. A new framework for collaborative filtering with p-moment-based similarity measure: Algorithm, optimization and application. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Sun J, Shen B, Liu Y, Alsaadi FE. Dynamic event-triggered state estimation for time-delayed spatial-temporal networks under encoding-decoding scheme. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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16
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Sun W, Wang Z, Lv X, Alsaadi FE, Liu H. H∞ observer design for networked Hamiltonian systems with sensor saturations and missing measurements. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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17
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Song Q, Zeng R, Zhao Z, Liu Y, Alsaadi FE. Mean-square stability of stochastic quaternion-valued neural networks with variable coefficients and neutral delays. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Tian L, Wang Z, Liu W, Cheng Y, Alsaadi FE, Liu X. Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01440-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractIn this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.
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Wang H, Xiaoping Liu P, Xie X, Liu X, Hayat T, Alsaadi FE. Adaptive fuzzy asymptotical tracking control of nonlinear systems with unmodeled dynamics and quantized actuator. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2018.04.011] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Tian L, Wang Z, Liu W, Cheng Y, Alsaadi FE, Liu X. A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09922-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractAs a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.
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21
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Chen S, Song Q, Zhao Z, Liu Y, Alsaadi FE. Global asymptotic stability of fractional-order complex-valued neural networks with probabilistic time-varying delays. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.043] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Huang W, Song Q, Zhao Z, Liu Y, Alsaadi FE. Robust stability for a class of fractional-order complex-valued projective neural networks with neutral-type delays and uncertain parameters. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Song Q, Chen S, Zhao Z, Liu Y, Alsaadi FE. Passive filter design for fractional-order quaternion-valued neural networks with neutral delays and external disturbance. Neural Netw 2021; 137:18-30. [PMID: 33529939 DOI: 10.1016/j.neunet.2021.01.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/14/2020] [Accepted: 01/14/2021] [Indexed: 11/17/2022]
Abstract
The problem on passive filter design for fractional-order quaternion-valued neural networks (FOQVNNs) with neutral delays and external disturbance is considered in this paper. Without separating the FOQVNNs into two complex-valued neural networks (CVNNs) or the FOQVNNs into four real-valued neural networks (RVNNs), by constructing Lyapunov-Krasovskii functional and using inequality technique, the delay-independent and delay-dependent sufficient conditions presented as linear matrix inequality (LMI) to confirm the augmented filtering dynamic system to be stable and passive with an expected dissipation are derived. One numerical example with simulations is furnished to pledge the feasibility for the obtained theory results.
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Affiliation(s)
- Qiankun Song
- Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China.
| | - Sihan Chen
- School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
| | - Zhenjiang Zhao
- Department of Mathematics, Huzhou University, Huzhou 313000, China
| | - Yurong Liu
- Department of Mathematics, Yangzhou University, Yangzhou 225002, China; School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng 224051, China
| | - Fuad E Alsaadi
- Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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24
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Zeng N, Li H, Wang Z, Liu W, Liu S, Alsaadi FE, Liu X. Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.001] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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25
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Yang H, Wang Z, Shen Y, Alsaadi FE, Alsaadi FE. Event-triggered state estimation for Markovian jumping neural networks: On mode-dependent delays and uncertain transition probabilities. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.050] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Song Q, Chen Y, Zhao Z, Liu Y, Alsaadi FE. Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.08.059] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Liu X, Song Q, Yang X, Zhao Z, Liu Y, Alsaadi FE. Asymptotic stability and synchronization for nonlinear distributed-order system with uncertain parameters. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Wu Q, Song Q, Hu B, Zhao Z, Liu Y, Alsaadi FE. Robust stability of uncertain fractional order singular systems with neutral and time-varying delays. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang Y, Arumugam A, Liu Y, Alsaadi FE. Finite-time event-triggered non-fragile state estimation for discrete-time delayed neural networks with randomly occurring sensor nonlinearity and energy constraints. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Shen Y, Wang Z, Shen B, Alsaadi FE, Dobaie AM. l 2-l ∞ state estimation for delayed artificial neural networks under high-rate communication channels with Round-Robin protocol. Neural Netw 2020; 124:170-179. [PMID: 32007717 DOI: 10.1016/j.neunet.2020.01.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/27/2019] [Accepted: 01/14/2020] [Indexed: 11/16/2022]
Abstract
In this paper, the l2-l∞ state estimation problem is addressed for a class of delayed artificial neural networks under high-rate communication channels with Round-Robin (RR) protocol. To estimate the state of the artificial neural networks, numerous sensors are deployed to measure the artificial neural networks. The sensors communicate with the remote state estimator through a shared high-rate communication channel. In the high-rate communication channel, the RR protocol is utilized to schedule the transmission sequence of the numerous sensors. The aim of this paper is to design an estimator such that, under the high-rate communication channel and the RR protocol, the exponential stability of the estimation error dynamics as well as the l2-l∞ performance constraint are ensured. First, sufficient conditions are given which guarantee the existence of the desired l2-l∞ state estimator. Then, the estimator gains are obtained by solving two sets of matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the developed l2-l∞ state estimation scheme.
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Affiliation(s)
- Yuxuan Shen
- College of Information Science and Technology, Donghua University, Shanghai 200051, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Bo Shen
- College of Information Science and Technology, Donghua University, Shanghai 200051, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Abdullah M Dobaie
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Alsaadi FE, Hayat T, Khan SA, Alsaadi FE, Khan MI. Investigation of physical aspects of cubic autocatalytic chemically reactive flow of second grade nanomaterial with entropy optimization. Comput Methods Programs Biomed 2020; 183:105061. [PMID: 31539717 DOI: 10.1016/j.cmpb.2019.105061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/28/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Nanofluids have innovative characteristics that make them potentially beneficial in numerous applications in heat and mass transports like fuel cells, hybrid-powered engines, microelectronics, pharmaceutical processes, domestic refrigerator, engine cooling, heat exchanger, chiller and in boiler flue gas temperature decay. Nanomaterial increased the coefficient of heat transport and thermal performance compared to continuous phase liquid. Having such significance in mind, the nanofluid flow of second grade material over a convectively heated surface is examined here. Nano-fluid is electrically conducting. Energy expression is studied through Joule heating, heat source/sink and dissipation. In addition, thermophoresis and Brownian diffusion are investigated. Physical aspects of entropy optimization in nanomaterials with cubic autocatalysis chemical reaction are accounted. Through second law of thermodynamics the total entropy generation rate is computed. METHODS The nonlinear governing PDE's are transformed to ordinary ones through transformations. Total residual error is calculated for momentum, energy and concentration equations using optimal homotopy analysis method (OHAM). RESULTS Behaviors of different variables on velocity, Bejan number, concentration, temperature and entropy optimization are examined via graphs. Local skin friction coefficient (Cfx) and gradient of temperature (Nux)are examined graphically. Comparison between the recent and previous result is given. Temperature and velocity are enhanced significantly versus (λ1). Entropy generation rate boosts up for magnetic parameter and Brinkman number. CONCLUSIONS The obtained outcomes show that velocity is higher via mixed convective variable. Temperature boosts up in presence of higher magnetic parameter, thermophoretic paraemter, Brinkman number and second grade parameter while Biot number decays. Concentration has increasing behavior via larger Brownian and homogeneous and heterogeneous parameters. Entropy rate and Bejan number have similar impact through diffusion parameters with respect to both homogeneous and heterogeneous reactions variables.
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Affiliation(s)
- Fawaz E Alsaadi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - T Hayat
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Mathematics, Quaid-I-Azam University 45320, Islamabad 44000, Pakistan
| | - Sohail A Khan
- Department of Mathematics, Quaid-I-Azam University 45320, Islamabad 44000, Pakistan
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - M Ijaz Khan
- Department of Mathematics, Quaid-I-Azam University 45320, Islamabad 44000, Pakistan.
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Alsaadi FE, Hayat T, Khan MI, Alsaadi FE. Heat transport and entropy optimization in flow of magneto-Williamson nanomaterial with Arrhenius activation energy. Comput Methods Programs Biomed 2020; 183:105051. [PMID: 31526945 DOI: 10.1016/j.cmpb.2019.105051] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 08/21/2019] [Accepted: 08/24/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND A newly developed approach in the field of nanotechnology for solving problems and collection of information is the use of nanoparticles. This idea has been further utilized in a better way in pharmaceutical industries. By using nanotechnology, the field of pharmaceutical science has been modernized and redeveloped. The use of nanotechnology in such industries has convinced the scientist to obtain more economical and easier applications. Therefore, with such effectiveness in mind, a theoretical study has been conducted to examine the effects of nonlinear radiative heat flux and magnetohydrodynamics for nanomaterial flow of Williamson fluid over a convectively heated stretchable surface. Brownian diffusion is utilized in mathematical modeling. Furthermore, heat source/sink, viscous dissipation and nonlinear radiative heat flux are examined. Convective boundary condition is implemented. Salient effects of chemical reaction and Arrhenius activation energy in mass transfer are considered. Total entropy rate is obtained through implementation of thermodynamics second law. METHODS The nonlinear PDEs are reduced into ordinary ones by appropriate similarity transformations. A semi-analytical technique i.e., homotopy method is implemented to obtain the convergent series solutions. RESULTS The obtained results indicate that the velocity of fluid particles increases versus higher fluid parameter. Schmidt number and activation energy variable have opposite effect on concentration. Entropy rate grows up with fluid parameter and Brinkman and Biot numbers while opposite trend is seen for Bejan number. CONCLUSIONS Velocity of the material particles declines through larger estimations of magnetic variable while it upsurges for higher fluid parameter. Thermal distribution shows similar impact for radiative and magnetic variables. Mass concentration decreases against chemical reaction parameter while it increases via activation energy variable. Entropy and Bejan numbers show opposite impacts versus Brinkman number. Skin friction coefficient increases through larger Weissenberg number.
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Affiliation(s)
- Fawaz E Alsaadi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah, Saudi Arabia
| | - T Hayat
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah, Saudi Arabia; Department of Mathematics, Quaid-I-Azam University 45320, Islamabad 44000, Pakistan
| | - M Ijaz Khan
- Department of Mathematics, Quaid-I-Azam University 45320, Islamabad 44000, Pakistan.
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University Jeddah, Saudi Arabia
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Li Q, Wang Z, Sheng W, Alsaadi FE, Alsaadi FE. Dynamic event-triggered mechanism for H∞ non-fragile state estimation of complex networks under randomly occurring sensor saturations. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.08.063] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Wei G, Lu M, Alsaadi FE, Hayat T, Alsaedi A. IOS Press has retracted the following publication from its online content: [Journal of Intelligent & Fuzzy Systems, 33(2) (2017), 1129-1142] DOI: 10.3233/JIFS-16715. IFS 2019. [DOI: 10.3233/jifs-179527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guiwu Wei
- School of Business, Sichuan Normal University, Chengdu, P.R. China
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mao Lu
- School of Business, Sichuan Normal University, Chengdu, P.R. China
| | - Fuad E. Alsaadi
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, Quaid-I-Azam University 45320, Islamabad, Pakistan
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alsaedi
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Lu M, Wei G, Alsaadi FE, Hayat T, Alsaedi A. IOS Press has retracted the following publication from its online content: [Journal of Intelligent & Fuzzy Systems, 33(2) (2017), 1197-1207] DOI: 10.3233/JIFS-16946. IFS 2019. [DOI: 10.3233/jifs-179528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mao Lu
- School of Business, Sichuan Normal University, Chengdu, P.R. China
| | - Guiwu Wei
- School of Business, Sichuan Normal University, Chengdu, P.R. China
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fuad E. Alsaadi
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alsaedi
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Lu M, Wei G, Alsaadi FE, Hayat T, Alsaedi A. IOS Press has retracted the following publication from its online content: [Journal of Intelligent & Fuzzy Systems, 33(2) (2017), 1105– 1117] DOI: 10.3233/JIFS-16554. IFS 2019. [DOI: 10.3233/jifs-179525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mao Lu
- School of Business, Sichuan Normal University, Chengdu, P.R. China
| | - Guiwu Wei
- School of Business, Sichuan Normal University, Chengdu, P.R. China
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fuad E. Alsaadi
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alsaedi
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Wei G, Alsaadi FE, Hayat T, Alsaedi A. IOS Press has retracted the following publication from its online content: [Journal of Intelligent & Fuzzy Systems, 33(2) (2017), 1119-1128] DOI: 10.3233/JIFS-16612. IFS 2019. [DOI: 10.3233/jifs-179526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guiwu Wei
- School of Business, Sichuan Normal University, Chengdu, P.R. China
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fuad E. Alsaadi
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alsaedi
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Wang L, Wang Z, Wei G, Alsaadi FE. Observer-Based Consensus Control for Discrete-Time Multiagent Systems With Coding-Decoding Communication Protocol. IEEE Trans Cybern 2019; 49:4335-4345. [PMID: 30207977 DOI: 10.1109/tcyb.2018.2863664] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, the consensus control problem is investigated for a class of discrete-time networked multiagent systems (MASs) with the coding-decoding communication protocol (CDCP). Under a directed communication topology, an observer-based control scheme is proposed for each agent by utilizing the relative measurement outputs between the agent itself and its neighboring ones. The signal delivery is in a digital manner, which means that only the sequence of finite coded signals is sent from the observer to the controller. To be specific, the observed data is encoded to certain codewords by a designed coder via the CDCP, and the received codewords are then decoded by the corresponding decoder at the controller side. The purpose of the addressed problem is to design an observer-based controller such that the close-loop MAS achieves the expected consensus performance. First, with the help of the input-to-state stability theory, a theoretical framework for the detectability is established for analyzing and designing the CDCP. Then, under such a communication protocol, some sufficient conditions for the existence of the proposed observer-based controller are derived to guarantee the asymptotic consensus of the MASs. In addition, the controller parameter is explicitly determined in terms of the solution to certain matrix inequalities associated with the information of the communication topology. Finally, a simulation example is given to demonstrate the effectiveness of the developed control strategy.
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Meng F, Li K, Zhao Z, Song Q, Liu Y, Alsaadi FE. Periodicity of impulsive Cohen–Grossberg-type fuzzy neural networks with hybrid delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wan X, Yang X, Tang R, Cheng Z, Fardoun HM, Alsaadi FE. Exponential synchronization of semi-Markovian coupled neural networks with mixed delays via tracker information and quantized output controller. Neural Netw 2019; 118:321-331. [DOI: 10.1016/j.neunet.2019.07.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 06/12/2019] [Accepted: 07/07/2019] [Indexed: 10/26/2022]
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Wang J, Wei G, Wang R, Alsaadi FE, Hayat T, Wei C, Zhang Y, Wu J. Some q‐rung interval‐valued orthopair fuzzy Maclaurin symmetric mean operators and their applications to multiple attribute group decision making. INT J INTELL SYST 2019. [DOI: 10.1002/int.22156] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Jie Wang
- School of BusinessSichuan Normal University Chengdu China
| | - Guiwu Wei
- School of BusinessSichuan Normal University Chengdu China
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz University Jeddah Saudi Arabia
| | - Rui Wang
- School of BusinessSichuan Normal University Chengdu China
| | - Fuad E. Alsaadi
- Communications Systems and Networks (CSN) Research Group, Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz University Jeddah Saudi Arabia
| | - Tasawar Hayat
- Department of MathematicsQuaid‐I‐Azam University Islamabad Pakistan
- Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of ScienceKing Abdulaziz University Jeddah Saudi Arabia
| | - Cun Wei
- School of BusinessSichuan Normal University Chengdu China
- School of StatisticsSouthwestern University of Finance and Economics Chengdu China
| | - Yi Zhang
- Business SchoolSouthwest University of Political Science & Law Chongqing China
| | - Jiang Wu
- School of StatisticsSouthwestern University of Finance and Economics Chengdu China
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Wang L, Song Q, Zhao Z, Liu Y, Alsaadi FE. Synchronization of two nonidentical complex-valued neural networks with leakage delay and time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Niu B, Wang D, Alotaibi ND, Alsaadi FE. Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method. IEEE Trans Neural Netw Learn Syst 2019; 30:1076-1087. [PMID: 30130237 DOI: 10.1109/tnnls.2018.2860944] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem. Furthermore, by combining the average dwell-time scheme and the adaptive backstepping design, a valid adaptive neural state-feedback controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. Finally, the availability of the developed control scheme is verified by two simulation examples.
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Zhang W, Zhang H, Cao J, Alsaadi FE, Chen D. Synchronization in uncertain fractional-order memristive complex-valued neural networks with multiple time delays. Neural Netw 2019; 110:186-198. [DOI: 10.1016/j.neunet.2018.12.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/13/2018] [Accepted: 12/04/2018] [Indexed: 11/16/2022]
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49
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Meng F, Li K, Song Q, Liu Y, Alsaadi FE. Periodicity of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.038] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhou W, Niu B, Xie X, Alsaadi FE. Adaptive neural-network-based tracking control strategy of nonlinear switched non-lower triangular systems with unmodeled dynamics. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.077] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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