Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…There are many complex factors in the special environment of coal transportation in power plants, such as uneven light, dust interference, and the different shapes, sizes, and materials of foreign objects on the coal conveyor belt. …”
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Surface morphology segmentation and evaluation of diamond lapping pad based on improved Mask R-CNN by Wenlong SUO, Yanfen LIN, Congfu FANG

“…ConclusionsThe dilated convolution method can effectively expand the receptive field and improve the ability to extract deep semantic features of targets at different scales. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This network performs convolution operations on features at different instances, enabling the model to capture changes over time and identify long-term dependencies between action features. …”
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Multi-Dimensional Feature Perception Network for Open-Switch Fault Diagnosis in Grid-Connected PV Inverters by Yuxuan Xie, Yaoxi He, Yong Zhan, Qianlin Chang, Keting Hu, Haoyu Wang

“…This network captures multi-scale fault features under complex operating conditions through a multi-dimensional dilated convolution feature enhancement module and extracts non-causal relationships under different conditions using convolutional feature fusion with a Transformer. …”
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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN by Zhige He, Yuanqing He

“…Secondly, The DCN (Deformable Convolution Network) is employed in the backbone to strengthen the ability of extracting features for deformed objects. …”
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Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles by Kai Liang, Haijun Zhao

“…To demonstrate the quality difference of the generated samples from different input signals, two GAN models with different inputs were constructed. …”
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Comparative Analysis of Attention Mechanisms in Densely Connected Network for Network Traffic Prediction by Myeongjun Oh, Sung Oh, Jongkyung Im, Myungho Kim, Joung-Sik Kim, Ji-Yeon Park, Na-Rae Yi, Sung-Ho Bae

“…Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. …”
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A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market by Ruidan Su, Chun Chi, Shikui Tu, Lei Xu

“…Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. …”
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First-Arrival Picking for Microseismic Monitoring Based on Deep Learning by Xiaolong Guo

“…In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. …”
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SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing by Qingjun Niu, Kun Wu, Jialu Zhang, Zhenqi Han, Lizhuang Liu

“…SDEC combines wavelet transform and difference convolution(DC) to enhance high-frequency features while preserving low-frequency information. …”
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Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN by Pinyan Li, Lap Man Hoi, Yapeng Wang, Xu Yang, Sio Kei Im

“…In this study, two CRET models are proposed, and these two models are compared with the baseline models Multi-Scale Backbone Architecture (Res2Net) and ECAPA-TDNN in different channels and different datasets. The experimental findings indicate that our proposed models exhibit strong performance across various experiments conducted on both training and test sets, even when the network layer is deep. …”
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Channel Estimation Using CNN-LSTM in RIS-NOMA Assisted 6G Network by Chi Nguyen, Tiep M. Hoang, Adnan A. Cheema

“…This paper proposes a deep learning (DL)-based channel estimation method using a convolutional long-short term memory (CNN-LSTM) model for RIS-NOMA wireless communication systems that integrate RIS and NOMA techniques. …”
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A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis by Lina Li

“…In this paper, novel scale perception module and inverse scale perception module are designed to further facilitate the mining of multiscale information by the counting model; the main function of the third stage is to generate the population distribution density map, which mainly consists of three columns of void convolution with different void rates and generates the final population distribution density map using the feature maps of different branch regressions. …”
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Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios by Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo, Zihao Wang

“…In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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INFLUENCE OF THE AVERAGE WEIGHTED ESTIMATION TYPE ON THE DEPENDENCE OF THE COMPLEX QUALITY INDEX ON THE PARAMETERS OF OBJECT by A. M. Dolzhanskiy, O. A. Bondarenko, Ye. A. Petlyovaniy

“…It includes single quality indicators with their significance factors. The convolution of the corresponding dependencies represents average weighted quantities: arithmetic, geometric, harmonic, quadratic, etc. …”
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Evaluation of Various Free Software Options for Catphan 504 Phantom Analysis by Lorena Cunha Fernandes, Maira Ribeiro dos Santos, Leonardo Peres da Silva, Thiago Viana Miranda Lima, Rafael Figueiredo Pohlmann Simões

“…Purpose: the purpose of this study is to analyse the images reconstructed with different thorax and bone convolution filters using popular free-use software in the field of medical physics, for the Catphan 504 phantom. …”
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A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction by Xinyang Chen, Pengfeng Xiao, Xueliang Zhang, Dilxat Muhtar, Luhan Wang

“…Although some studies have considered the integration between both high- and low-frequency features, they overlook the suitability of different network depths for extracting different frequency features. …”
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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…However, the speed of computation is low because of the time-consuming spatial convolution integrals. To solve this problem, we introduce the recursive convolution (RC) with vector fitting (VF) method to accelerate the computational speed. …”
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