Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Reconstruction of normalized difference vegetation index (NDVI) time series plays an imperative part in the inference of vegetation dynamics. …”
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Evaluation of Peroxide Value and Free Fatty Acid Content in Coconut Oil and Palm Oil under Different Heating Temperature Treatments using Reflectance–Fluorescence-based computer vi... by Luluk Oktaviana, Wahyunanto Agung Nugroho, Dimas Firmanda Al Riza Al Riza

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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…Analyzing the impact of the phenological differences between on-year and off-year Moso bamboo on pest identification accuracy revealed that when four machine learning models accounted for these phenological characteristics, their accuracy in identifying pests was significantly higher than that of a model which did not take into account the bamboo phenology. …”
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DCDGAN-STF: A Multiscale Deformable Convolution Distillation GAN for Remote Sensing Image Spatiotemporal Fusion by Yan Zhang, Rongbo Fan, PeiPei Duan, Jinfang Dong, Zhiyong Lei

“…However, compared to traditional image super-resolution tasks, remote sensing image STF involves merging a larger amount of multitemporal data with greater resolution difference. To enhance the robust matching performance of spatiotemporal transformations between multiple sets of remote sensing images captured at DTDS and to generate super-resolution composite images, we propose a feature fusion network called the multiscale deformable convolution distillation generative adversarial network (DCDGAN-STF). …”
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Research on Damage Detection Methods for Concrete Beams Based on Ground Penetrating Radar and Convolutional Neural Networks by Ning Liu, Ya Ge, Xin Bai, Zi Zhang, Yuhao Shangguan, Yan Li

“…High-frequency GPR equipment is used for data acquisition, A-scan data corresponding to different defects is extracted as a training set, and appropriate labeling is carried out. …”
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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

“…The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. …”
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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

“…The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. …”
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Impact of the Radar Image Resolution of Military Objects on the Accuracy of their Classification by a Deep Convolutional Neural Network by I. F. Kupryashkin

“…Introduction. Deep convolutional neural networks are considered as one of the most promising tools for classifying small-sized objects on radar images. …”
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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution by Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG

“…Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.…”
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Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network by Huaiguang Liu, Wancheng Ding, Qianwen Huang, Li Fang

“…In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). …”
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Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network by Feng-Ping An

“…A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in-depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. …”
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A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling by Enze Zhang, Yan Li, Haifeng Lin, Min Xia

“…Therefore, to address the need for lightweight solutions in scenarios with limited computing resources, this paper proposes an attention-based lightweight remote sensing change detection network (ABLRCNet), which achieves a balance between computational efficiency and detection accuracy by using lightweight residual convolution blocks (LRCBs), multi-scale spatial-attention modules (MSAMs) and feature-difference enhancement modules (FDEMs). …”
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A Novel Multistep Wavelet Convolutional Transfer Diagnostic Framework for Cross-Machine Bearing Fault Diagnosis by Lujia Zhao, Yuling He, Hai Zheng, Derui Dai

“…Due to the different mechanical structures of different machines, the signal transmission paths are vastly different, and the distribution of collected data varies greatly, making it difficult for existing transfer fault diagnosis methods to meet diagnostic needs. …”
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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). …”
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A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network by Xinyan Zhang, Shaobin Cai, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…In this method, first, GM is used to reduce the noise of the collected vibration signals; Secondly, MSCNN is used for feature extraction, and the characteristics of vibration signals at different frequencies and time scales can be captured by the convolutional kernels of different scales; thirdly, two feature enhancement branches are added, utilizing the undenoised vibration signal as input, to enrich and diversify features while enhancing the model’s expressive and generalization capabilities; Finally, the experimental analysis was conducted on two bearing datasets to indicates that the noise robustness of GMSCNN is strong.…”
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SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks. by Safaeid Hossain Arib, Rabeya Akter, Sejuti Rahman, Shafin Rahman

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Multi-Signal Induction Motor Broken Rotor Bar Detection Based on Merged Convolutional Neural Network by Tianyi Wang, Shiguang Wen, Shaotong Sheng, Huimin Ma

“…This experiment investigates the detection of broken rotor bars of motors with different loads (25%, 50%, 75%, and 100% of rated load) and different fault levels (Normal, 1BRB, 2BRB, 3BRB, and 4BRB). …”
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A Novel Multi-Task and Ensembled Optimized Parallel Convolutional Autoencoder and Transformer for Speech Emotion Recognition by Zahra Sharifzadeh Jafari, Sanaz Seyedin

“…Recognizing the emotions from speech signals is very important in different applications of human-computer-interaction (HCI). …”
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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

“…Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis, a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network (MCMI-GCFN) is proposed in this paper. …”
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A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration by Kunpeng Mu, Wenqing Wang, Han Liu, Lili Liang, Shuang Zhang

“…In the upper branch of the dual-branch feature extraction module, we designed a combination of multi-scale convolution and Swin Transformer to fully extract features of remote sensing images at different scales and levels to better understand the global structure and context information. …”
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