A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

Li, Zele; Noori, Mohammad; Wan, Chunfeng; Yu, Bo; Wang, Bochen; Altabey, Wael A.

doi:10.3390/app12199440

Cited by 15 publications

(4 citation statements)

References 63 publications

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“…Interspersed with sub-sampling layers, convolutional layers are established to increase computation efficiency and further improve configural and spatial invariance. [32][33][34].…”

Section: Cnn Architecturementioning

confidence: 99%

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Altabey,

Noori,

2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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In this work, the Convolutional Neural Network (CNN) algorithm is introduced in pipeline surface cracks monitoring-based image processing method for improving the efficiency and accuracy of crack type, location, and area identification. The method is used to extract the cracks area called the CNN based on crack contour network (CCN-CNNs) method from locate and extract the crack shape. CCN-CNNs is provides the accuracy rate (P%), recall rate (R%), and F-score (F%) index to assess the algorithm in the problem while identifying the cracks, and then according to the maximum F-score, we computes the crack corresponding contour area. In this work the pipeline crake images datasets are provided using an inspection drone with high definition camera. To the best of the authors' knowledge, the methodology presented in this paper for pipeline crack identification is an original contribution to the literature. This work introduces an efficient approach that also significantly reduces the time for crack type, location, and area identification of pipelines, the accuracy rate (P%), recall rate (R%), and Fscore (F%) are recorded 91.8% ,86.1%, and 84.6% respectively. 3357 COMPDYN 2023 9 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.

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“…Interspersed with sub-sampling layers, convolutional layers are established to increase computation efficiency and further improve configural and spatial invariance. [32][33][34].…”

Section: Cnn Architecturementioning

confidence: 99%

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Altabey,

Noori,

2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…ANN is the most used algorithm between different artificial intelligence (AI) algorithms for the advanced nonlinear problems solution [33,34]. Each individual network of ANN consists of the number of computational nodes, and each node is used for processing the inputs and transferring the input calculation results to output connections.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm

Altabey

Noori

et al. 2022

Materials

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Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms.

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“…Several researchers in the fields of pavement crack identification algorithm and system integration research and development domain have also carried out exploratory research, by continuously improving the algorithm and system tests such as wavelet transform, block processing, and other methods to solve the problem of obtaining crack data [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] Wang et al [57] proposed an image segmentation method based on shape features, Martin et al [58] consider the crack region combined with boundary information to detect crack edges, this class is based on edge features. The crack identification algorithm is very important for the detection of pavement cracks.…”

Section: Introductionmentioning

confidence: 99%

Research of pavement crack detection system based on image processing

Xu¹,

Zhang²,

Gongzuo³

et al. 2023

Third International Conference on Computer Vision and Information Technology (CVIT 2022)

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With the continuous development of the social economy, road traffic plays an increasingly significant role in the national economy and people's lives. Traffic has become one of the important infrastructures for people's daily travel and economic construction in my country, and it is the key to reflecting the economic development of a region. During the long-term operation of expressways, various types of diseases will occur. In order to solve the problem that traditional pavement crack detection, a pavement crack detection method based on image processing under complex background is proposed. A pavement crack image segmentation model based on semantic segmentation is built, and cracks in highresolution crack images are extracted by using the pavement image segmentation model. The results show that, compared with the existing algorithms, the pro-posed algorithm has a better detection effect and stronger generalization ability in complex road scenes.

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A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

Cited by 15 publications

References 63 publications

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm

Research of pavement crack detection system based on image processing

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