Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network

AbstractCeramic materials are an indispensable part of our lives. Today, ceramic materials are mainly used in construction and kitchenware production. The fact that some deformations cannot be seen with the naked eye in the ceramic industry leads to a loss of time in the detection of deformations in the products. Delays that may occur in the elimination of deformations and in the planning of the production process cause the products with deformation to be excessive, which adversely affects the quality. In this study, a deep learning model based on acoustic noise data and transfer learning techniques was designed to detect cracks in ceramic plates. In order to create a data set, noise curves were obtained by applying the same magnitude impact to the ceramic experiment plates by impact pendulum. For experimental application, ceramic plates with three invisible cracks and one undamaged ceramic plate were used. The deep learning model was trained and tested for crack detection in ceramic plates by the data set obtained from the noise graphs. As a result, 99.50% accuracy was achieved with the deep learning model based on acoustic noise.

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Advanced deep learning model-based impact characterization method for composite laminates

Composites Science and Technology ◽

10.1016/j.compscitech.2021.108713 ◽

2021 ◽

Vol 207 ◽

pp. 108713

Author(s):

Kyung-Chae Jung ◽

Seung-Hwan Chang

Keyword(s):

Deep Learning ◽

Composite Laminates ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

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Transferable deep learning model based on the phenological matching principle for mapping crop extent

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102451 ◽

2021 ◽

Vol 102 ◽

pp. 102451

Author(s):

Shuang Ge ◽

Jinshui Zhang ◽

Yaozhong Pan ◽

Zhi Yang ◽

Shuang Zhu

Keyword(s):

Deep Learning ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

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A ConvBiLSTM Deep Learning Model Based Approach for Twitter Sentiment Classification

IEEE Access ◽

10.1109/access.2021.3064830 ◽

2021 ◽

pp. 1-1

Author(s):

Sakirin Tam ◽

Rachid Ben Said ◽

Omer Ozgur Tanriover

Keyword(s):

Deep Learning ◽

Learning Model ◽

Sentiment Classification ◽

Model Based ◽

Deep Learning Model

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Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.149654 ◽

2021 ◽

pp. 149654

Author(s):

Jusong Kim ◽

Xiaoli Wang ◽

Chollyong Kang ◽

Jinwon Yu ◽

Penghui Li

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Wavelet Transform ◽

Learning Model ◽

Air Pollutant ◽

Pollutant Concentration ◽

Model Based ◽

Empirical Wavelet Transform ◽

Deep Learning Model

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A Deep Learning Model Based on Convolutional Neural Networks for Classification of Magnetic Resonance Prostate Images

Artificial Intelligence and Applied Mathematics in Engineering Problems - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-36178-5_59 ◽

2020 ◽

pp. 701-708

Author(s):

Fatih Uysal ◽

Fırat Hardalaç ◽

Mustafa Koç

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

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Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽

10.3390/cancers14010012 ◽

2021 ◽

Vol 14 (1) ◽

pp. 12

Author(s):

Jose M. Castillo T. ◽

Muhammad Arif ◽

Martijn P. A. Starmans ◽

Wiro J. Niessen ◽

Chris H. Bangma ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Characteristic Curve ◽

Model Development ◽

Learning Model ◽

Multiparametric Mri ◽

Data Sets ◽

Data Set ◽

Test Sets ◽

Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

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