TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…This convolutional operation is embedded following standard convolution to mitigate the loss of detailed features. …”
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Restoration of a blurred photographic image of a moving object obtained at the resolution limit by V. B. Fedorov, S. G. Kharlamov, A. I. Starikovskiy

“…The proposed method for restoring a blurred photographic image of a moving object differs from traditional approaches in that the discrete convolution equation, to which the problem of restoring a blurred image is reduced, is obtained by approximating the corresponding integral equation based on the Kotelnikov interpolation series rather than on the traditional basis of the quadrature formula. …”
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Robust low frequency seismic bandwidth extension with a U-net and synthetic training data by P. Zwartjes, J. Yoo

“…This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data. …”
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RSR Calculator, a tool for the Calibration / Validation activities by C. Durán-Alarcón, A. Santamaría-Artigas, N. Valenzuela, C. Mattar

“…<p>The calibration/validation of remote sensing products is a key step that needs to be done before its use in different kinds of environmental applications and to ensure the success of remote sensing missions. …”
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A Latent Multi-Scale Residual Transformer Approach for Cross-Modal Medical Image Synthesis by Xinmiao Zhu, Yang Li

“…This module consists of two layers of residual convolutional blocks and transformer blocks of different scales, where the transformer blocks assist the convolutional blocks in capturing contextual features, and lower-level blocks support higher-level blocks in learning high-dimensional global information. …”
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Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review by Ahmad Chusyairi, Yeni Herdiyeni, Heru Sukoco, Edi Santosa

“…Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).…”
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Research on Aerospace Text Classification Based on BERT-LSTM Model by AN Rui, CHEN Hailong, AI Siyu, CUI Xinying

“…Then, the word vectors of the text sequence are concatenated into a matrix, and different sizes of convolution kernels are used for convolution operations. …”
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CFANet: The Cross-Modal Fusion Attention Network for Indoor RGB-D Semantic Segmentation by Long-Fei Wu, Dan Wei, Chang-An Xu

“…Appropriate feature extraction methods are designed according to the different characteristics of RGB images and depth maps. …”
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Performance of A Statistical-Based Automatic Contrast-to-Noise Ratio Measurement on Images of the ACR CT Phantom by Choirul Anam, Riska Amilia, Ariij Naufal, Heri Sutanto, Wahyu S. Budi, Geoff Dougherty

“…The CNR was measured on images acquired with different parameters: tube voltage (80–140 kVp), tube current (80–200 mA), slice thickness (1.25–10 mm), field of view (190–230 mm), and convolution kernel (edge, ultra, lung, bone, chest, standard). …”
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A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT by Ozgur Efiloglu, Muhammed Yildirim, Kadir Yildirim, Harun Bingol, Mustafa Kaan Akalin, Meftun Culpan, Bilal Alatas, Asif Yildirim

“…Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. …”
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Research on Wind Turbine Main Shaft Bearing Fault Diagnosis Method Based on Unity 3D and Transfer Learning by Shuai Wang, Wenlei Sun, Han Liu, Shenghui Bao, Yunhao Wang, Xin Zhao

“…The state monitoring visualization is limited, fault data and sample labels are scarce, and fault data distribution varies under different operational conditions, leading to low diagnosis accuracy and slow diagnosis speed. …”
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Maize quality detection based on MConv-SwinT high-precision model. by Ning Zhang, Yuanqi Chen, Enxu Zhang, Ziyang Liu, Jie Yue

“…Concurrently, the extracted features undergo further processing through a specially designed convolutional block. The fused features, combined with those processed by the convolutional module, are fed into an attention layer. …”
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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…Within the scope of the analysis, machine learning algorithms such as K-Nearest Neighbors, XGBoost and AdaBoost are compared with the proposed Convolutional Neural Network (CNN) model. The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. …”
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Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source by Qiqi Zheng, Meng Li, Bangyu Wu

“…The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. …”
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Self-Healing Properties of Partially Coherent Schell-Model Beams by Gaofeng Wu, Xiaoyan Pang

“…Convolution expression for partially coherent beams that are partially blocked by an opaque obstacle is derived in detail in a paraxial <inline-formula><tex-math notation="LaTeX">$ABCD$</tex-math></inline-formula> optical system. …”
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Statistical Inference for Stochastic Differential Equations with Small Noises by Liang Shen, Qingsong Xu

“…The asymptotic distribution of the estimator is shown to be the convolution of a stable distribution and a normal distribution, which is completely different from the classical cases.…”
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The Cultural Value Validity of Digital Media Art Based on Deep Learning Network Model by Yuan Ruan

“…In order to solve this problem, this paper proposes a deep learning neural network model based on a dual-core compression activation module. The convolution kernels of different sizes in one module are used to extract the overall features and local details of the image, and another module is used to achieve the main goal. …”
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Non-stationary signal combined analysis based fault diagnosis method by Zhe CHEN, Yuqi HU, Shiqing TIAN, Huimin LU, Lizhong XU

“…Considering the complementarity between the deep learning,spectrum and time frequency analysis methods,a multi-stream framework was designed by combining the convolutional network,Fourier transform and wavelet package decomposition methods,with the aim to analyze the non-stationary signal.Accordingly,a none-stationary signal combined analysis based fault diagnosis method was proposed to extract features in difference aspects.The fault diagnosis experiments demonstrate that the combined analysis method can efficiently and stably depict the fault and significantly improve the performance of fault diagnosis.…”
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A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion by Yan Li, Chunping Li, Tingting Zhu, Shurong Zhang, Li Liu, Zhanpeng Guan

“…The GhostConv module further improves detection accuracy by reducing the number of convolution kernels. Additionally, the BiFPN structure strengthens the model’s ability to detect objects of different sizes by fusing multi-scale feature information. …”
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A Novel Pseudo-Siamese Fusion Network for Enhancing Semantic Segmentation of Building Areas in Synthetic Aperture Radar Images by Mengguang Liao, Longcheng Huang, Shaoning Li

“…Next, the encoded features were reconstructed through skip connections and transposed convolution operations to obtain discriminative features of the building areas. …”
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