Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction by Song Zhang, Yanbing Liu, Yunpeng Xiao, Rui He

“…However, it is challenging to capture the global spatial-temporal correlations for daily long-term traffic prediction. In this paper, we propose a spatial-temporal upsampling graph convolutional network (STUGCN) for daily long-term traffic speed prediction. …”
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Attention Adaptive Temporal Graph Convolutional Network for Long-Term Seasonal Sea Surface Temperature Forecasting by Ling Xiao, Peihao Yang, Yuxue Wang, Sheng Li, Baoqin Chen

“…To address this, we introduce a novel attention adaptive temporal graph convolutional network (AA-TGCN) specifically designed for long-term seasonal SST forecasting. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…Despite substantial progress in this field, several challenges still remain. Firstly, most current methods rely on Graph Convolutional Networks (GCNs) to extract spatial correlations, typically using predefined adjacency matrices. …”
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Heterogeneous long-term changes in larch forest and shrubland cover in the Kolyma lowland are not captured by coarser-scale greening trends by Heather Kropp, Michael M Loranty, Howard Epstein, Gerald V Frost, Adam Koplik, Logan T Berner

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Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long‐Term Observations at ARM Eastern North Atlantic Site by Jingjing Tian, Jennifer Comstock, Andrew Geiss, Peng Wu, Israel Silber, Damao Zhang, Parvathi Kooloth, Ya‐ Chien Feng

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Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models by Yuzhou Lin, Ramaswamy Palaniappan, Philippe De Wilde, Ling Li

“…With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. …”
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Network traffic prediction based on transformer and temporal convolutional network. by Yi Wang, Peiyuan Chen

“…This paper proposes a hybrid model combining Transformer and Temporal Convolutional Network (TCN). This model addresses the shortcomings of current approaches in capturing long-term and short-term dependencies in network traffic prediction tasks. …”
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Seismic Events Prediction Using Deep Temporal Convolution Networks by Yue Geng, Lingling Su, Yunhong Jia, Ce Han

“…This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). …”
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Research on Identification Method of Transformer Windings’ Loose Vibration Spectrum Considering a Multi-Load Current Condition by Jin Fang, Xudong Deng, Yuancan Xia, Chen Wu, Yuehua Li, Xin Li, Kaixin Chen, Fan Wang, Zhanlong Zhang

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Damage detection in structural health monitoring using hybrid convolution neural network and recurrent neural network by Dung Bui-Ngoc, Hieu Nguyen-Tran, Lan Nguyen-Ngoc, Hoa Tran-Ngoc, Thanh Bui-Tien, Hung Tran-Viet

“…A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. …”
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Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network by Thanh Bui-Tien, Dung Bui-Ngoc, Hieu Nguyen-Tran, Lan Nguyen-Ngoc, Hoa Tran-Ngoc, Hung Tran-Viet

“…A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. …”
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Adaptive Disconnector States Diagnosis Method Based on Adjusted Relative Position Matrix and Convolutional Neural Networks by Peifeng Yan, Chenzhang Chang, Dong Hua, Haomin Huang, Suisheng Liu, Peiyi Cui

“…In this paper, we propose an HVD state diagnosis method featuring adaptive recognition capabilities based on Fault Difference Signals, Adjusted Relative Position Matrix and Convolutional Neural Networks (FDS-ARPM-CNN). First, we align the measured operational power signal of the HVD drive motor with the recorded normal operational power signal, deriving the FDS through subtraction. …”
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Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect by Maria Rugenstein, Senne VanLoon, Elizabeth A. Barnes

“…We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. …”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…This study introduces a novel deep learning model, termed 1DCNN-Informer, which integrates the one-dimensional convolutional neural network (1DCNN) and the Informer model. …”
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Fault Diagnosis of Rotating Machines Based on Combination of One-Dimensional Convolutional Neural Network and Long Short-Term Memory in Variable Working Conditions by Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen

“…Deep learning models, particularly one-dimensional convolutional neural networks (1D CNNs), have shown great potential in the fault diagnosis of rotating machines. …”
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Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System by Homari Matsumoto, Atomu Nakamura, Shun Nishide

“…We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. …”
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A novel framework to identify delamination location/size in BFRP pipe based on convolutional neural network (CNN) algorithm hybrid with capacitive sensors by Wael A. Altabey

“…Therefore, a new type of convolutional neural network (CNN) algorithm is adopted to train and test the EPC maps to evaluate delamination location/size. …”
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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images by Vatsala Anand, Prabhnoor Bachhal, Deepika Koundal, Arvind Dhaka

“…However, to enhance survival and quality of life for those afflicted by this aggressive haematological malignancy, more research and clinical trials are required to address the issues associated with resistance, relapse, and long-term toxicity. Consequently, a deep optimized Convolutional Neural Network (CNN) has been proposed for the early diagnosis and detection of ALL. …”
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Rainfall prediction based on CNN-LSTM model under sliding window by Rui Yuan

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A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data by Ningqing Pang, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, Jeremy Cheuk-Hin Leung

“…In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. …”
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