Deep learning based gasket fault detection: a CNN approach by S. Arumai Shiney, R. Seetharaman, V. J. Sharmila, S. Prathiba

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A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness by Zachary M. Choffin, Lingyan Kong, Yu Gan, Nathan Jeong

“…A convolutional neural network (CNN) was employed to assess the ripeness level, which was determined by analyzing the Brix sugar content. …”
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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System by Warapon Chinsatit, Takeshi Saitoh

“…This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. …”
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Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…Convolutional Neural Network (CNN) merupakan algoritma deep learning yang memiliki performa bagus dalam klasifikasi citra. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). …”
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ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM by WANG YongDing, JIN ZiQi

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A Review of CNN Applications in Smart Agriculture Using Multimodal Data by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe

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Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection by Brendan Ubochi, Abayomi E. Olawumi, John Macaulay, Oyawoye I. Ayomide, Kayode F. Akingbade

“…Hence, in this work, a comparative analysis is performed on vanilla CNN, AlexNet, GoogLeNet, and ResNet50 using two popular glaucoma datasets (ACRIMA and ORIGA). …”
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Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN by Ning Hu, Zhenguo Liu, Shixin Jiang, Quanzhou Li, Shuqi Zhong, Bingquan Chen

“…To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. …”
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Landslide Displacement Prediction Model Based on Time Series and CNN-GRU by FU Zhentao, LI Limin, WANG Lianxia, REN Ruibin, CUI Chengtao, FENG Qingqing

“…Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only 0.525%,with RMSE of 9.614 and R² of 0.993.Experimental results show that CNN-GRU has higher prediction accuracy.…”
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A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings by Li Kui, Sui Xin, Liu Chunyang, Li Jishun, Xu Yanwei, Yang Fang

“…Aiming at the difficulty of extracting fault features of rolling bearings under the influence of strong background noise, a rolling bearing fault diagnosis method based on the fusion of variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. After decomposing the original variation signal into multiple components, the proposed method employs the Pearson correlation coefficient as the automatic decomposition termination threshold and the optimal modal component selection index; a convolutional neural network is constructed according to bearing fault features and the optimal modal component is used as the input to extract and classify the fault types. …”
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Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN by Chuan Li, Nianbiao Cai, Tong Pu, Xi Yang, Hao Liu, Lulu Wang

Subjects: “…faster R-CNN…”
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Application of bilateral fusion model based on CNN in hyperspectral image classification by Hongmin GAO, Xueying CAO, Yao YANG, Zaijun HUA, Chenming LI

“…Aiming at the issues of decreasing spatial resolution and feature loss caused by pooling operation in depth CNN-based hyperspectral image classification algorithm,a bilateral fusion block network （DFBN）composed of bilateral fusion blocks was designed.The upper structure of bilateral fusion block was constituted by 1×1 convolution and hyperlink,which was used to transfer local spatial characteristics.The lower structure was constituted by pooling layer,convolutional layer,deconvolution layer and upsampling to enhance the characteristics of efficient discrimination.Experimental results on three benchmark hyperspectral image data sets illustrate that the model is superior to other similar classification models.…”
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Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease by Kahkashan Perveen, Sanjay Kumar, Sahil Kansal, Mukesh Soni, Najla A. Alshaikh, Shanzeh Batool, Mehrun Nisha Khanam, Bernard Osei

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Integration in CNN and FIR filters for improved computational efficiency in signal processing by A. Sridevi, A. Sathiya

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Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox by Iulian Lupea, Mihaiela Lupea

“…This paper studies the relevance of CWT (continuous wavelet transform) processing of vibration signals for improving the performance of CNN-based models that detect certain types of helical gearbox faults. …”
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RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN by TIAN LiYong, ZHAO JianJun, YU Ning

“…Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings.…”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. …”
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CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People by Fahad Ashiq, Muhammad Asif, Maaz Bin Ahmad, Sadia Zafar, Khalid Masood, Toqeer Mahmood, Muhammad Tariq Mahmood, Ik Hyun Lee

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Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning by Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf

“…The objective of this research is to improve the CNN-based image classification system by utilizing the advantages of ensemble learning and GA. …”
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