Application of a Convolutional Neural Network in a Terrain-Based Tire Pressure Management System by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Emmanuel A. Salcedo, Marites B. Tabanao, Emmy Grace T. Requillo, John Paul T. Cruz

“…In this study, we integrate a terrain recognition component which uses a convolutional neural network (CNN), specifically, ResNet-18, into the TPMS to classify and detect terrain conditions and apply the correct pressure level. …”
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Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction by Linlong Chen, Linbiao Chen, Hongyan Wang, Jian Zhao

“…We further introduce a dynamic graph generation network to enhance the flexibility of graph convolution, enabling the model to capture dynamic spatiotemporal correlations by integrating both current and historical states. …”
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Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network by Baoling Liu, Jun He, Xiaocui Yuan, Huiling Hu, Xuan Zeng, Zhifang Zhu, Jie Peng

“…To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. …”
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Enhanced Crack Detection in Composite Plates: Integrating Haar Wavelet Transform with Convolutional Neural Networks by Mehrparvar Marmar, Majak Jüri, Karjust Kristo

“…In order to ensure structural integrity, detecting cracks, as a common structural flaw, is crucial. The current study presents a method for crack detection and prediction in plates under free vibration using the Convolutional Neural Network (CNN) and the Haar wavelet transformation. …”
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Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network by Haixia Zhang, Wenao Cheng

“…Aiming at the problem of single-phase-to-ground fault line selection in resonant grounding system, a fault line selection method based on transfer learning depthwise separable convolutional neural network (DSCNN) is proposed. The proposed method uses two pixel-level image fusions to transform the three-phase current of each feeder into the RGB color image, which is used as the input of DSCNN. …”
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A Lightweight Conditional Diffusion Segmentation Network Based on Deformable Convolution for Surface Defect Detection by Jiusheng Chen, Yibo Zhao, Haibing Wang

“…To solve this problem, a lightweight conditional diffusion segmentation network based on deformable convolution is proposed. …”
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Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks by Feiyan Zhou, Duanshu Fang

“…To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchical features combined with the Lead Encoder Attention (LEA) mechanism for multi-lead ECG classification. …”
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Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks by Skowron Maciej

“…This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. …”
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A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis by Jiamao Yu, Hexuan Hu

“…To address these challenges, this paper proposes a Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation (MSCNN-SKD) for bearing fault diagnosis. …”
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Optical detection of beetle-related indicators and stem quality in roundwood using convolutional neural networks by Julia Achatz, Mark Schubert

“…Sorting wood based on macroscopic images using convolutional neural networks (CNN) is a cost-effective and efficient approach. …”
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PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection by Wenjuan Liu, Zhengyan Sun, Subo Wei, Shunxiang Zhang, Guangli Zhu, Lei Chen

“…Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. …”
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Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction by Song Zhang, Yanbing Liu, Yunpeng Xiao, Rui He

“…In this paper, we propose a spatial-temporal upsampling graph convolutional network (STUGCN) for daily long-term traffic speed prediction. …”
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Efficient remote sensing image classification using the novel STConvNeXt convolutional network by Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan

“…To address the balance between computational efficiency and feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. In its architectural design, the model incorporates a split-based mobile convolution module with a hierarchical tree structure. …”
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Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices by Vincent, Garry Darian, Nico Surantha

“…This study evaluates the performance of a convolutional neural network (CNN) model trained on 10,000 dermoscopic images spanning seven classes from the Harvard Skin Lesion dataset. …”
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Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs by Zhenghao Han, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Hailin Wang

“…Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. …”
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OCSCNet-Tracker: Hyperspectral Video Tracker Based on Octave Convolution and Spatial–Spectral Capsule Network by Dong Zhao, Mengyuan Wang, Kunpeng Huang, Weixiang Zhong, Pattathal V. Arun, Yunpeng Li, Yuta Asano, Li Wu, Huixin Zhou

“…To address this challenge, the current study explores the application of capsule networks in HVT and proposes an approach based on octave convolution and a spatial–spectral capsule network (OCSCNet). …”
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Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks by Yan Zun Nga, Zuhayr Rymansaib, Alfie Anthony Treloar, Alan Hunter

“…A robust, automated method for detecting human body-shaped objects is required to maximise operational functionality. The use of a convolutional neural network (CNN) for automatic target recognition (ATR) is proposed. …”
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From Convolutional Networks to Vision Transformers: Evolution of Deep Learning in Agricultural Pest and Disease Identification by Mengyao Zhang, Chaofan Liu, Zihan Li, Baoquan Yin

“…In this paper, we systematically present the application of traditional machine learning methods in pest and disease identification and their limitations, and focus on the research progress of deep learning methods, covering three mainstream architectures: convolutional neural network (CNN), Vision Transformer model and CNN–Transformer hybrid model. …”
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Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold by Li Yang, Qiong Yao, Xiang Xu

“…Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition accuracy. …”
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Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks by V. Forcadell, V. Forcadell, C. Augros, O. Caumont, O. Caumont, K. Dedieu, M. Ouradou, C. David, J. Figueras i Ventura, O. Laurantin, H. Al-Sakka

“…This study utilises convolutional neural network (CNN) models trained on dual-polarisation radar data to detect severe-hail occurrence on the ground. …”
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