Direction of Arrival Estimation Algorithm for Underwater Distributed Sources Based on Deep Neural Network by Yinian LIANG, Jie LI, Fangjiong CHEN, Fei JI, Hua YU

“…The proposed method is compared with four traditional subspace-based methods and one deep convolutional neural network algorithm, and the results show that the root mean square error of the proposed method under the coherently distributed source case is 0.42° lower than that of other methods under different signal-to-noise ratios(SNRs) and snapshots; under the incoherently distributed source case, the RMSE of the proposed method is 0.04° lower than that of other methods with SNR greater than 0 dB and snapshots greater than 600. …”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…This module uses a parameter-aware mechanism to adapt its bottleneck structures to different network depths and widths, reducing parameters while maintaining performance. …”
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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Second, our approach employs the dilated causal convolution to extend the receptive field and prolong the prediction time. …”
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High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion by Duanli Yang, Zishang Tian, Jianzhong Xi, Hui Chen, Erdong Sun, Lianzeng Wang

“…Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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A Novel Two-Level Protection Scheme against Hardware Trojans on a Reconfigurable CNN Accelerator by Zichu Liu, Jia Hou, Jianfei Wang, Chen Yang

“…With the boom in artificial intelligence (AI), numerous reconfigurable convolution neural network (CNN) accelerators have emerged within both industry and academia, aiming to enhance AI computing capabilities. …”
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Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning by Jinling Ren, Misheng Cai, Dapai Shi

“…The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. …”
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Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology by Li Tian, Chun Wang, Hailiang Li, Haitian Sun

“…To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. …”
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Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning by Wen-Long Shang, Yanyan Chen, Xingang Li, Washington Y. Ochieng

“…In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. …”
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Measuring the Company’s Capabilities and Assessing their Impact on Its Economic Results: Analytic Tools by E. P. Tretiakova, M. S. Tretiakova

“…To solve analytical tasks at different levels of management, combinations of methods, tools and indicators called “research formats” (detailed, diagnostic, express diagnostic analysis) were developed. …”
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CSCA-YOLOv8: A lightweight network model for evaluating drought resistance in mung bean. by Dongshan Jiang, Jinyang Liu, Haomiao Zhang, Wenxiang Liang, Ziqiu Luo, Wenlong An, Shicong Li, Xin Chen, Xingxing Yuan, Shangbing Gao

“…Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. …”
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ALPD-Net: a wild licorice detection network based on UAV imagery by Jing Yang, Huaibin Qin, Jianguo Dai, Guoshun Zhang, Miaomiao Xu, Yuan Qin, Jinglong Liu

“…Additionally, to enhance the model’s attention to licorice at different scales, a Lightweight Multi-Scale Module (LMSM) using multi-scale dilated convolution is introduced, significantly reducing the probability of missed detections. …”
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Steel surface defect detection method based on improved YOLOv9 by Cong Chen, Hoileong Lee, Ming Chen

“…Second, the C3 module is incorporated to effectively fuse feature maps from different levels, enhancing the model’s ability to detect multi-scale targets. …”
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A DNA Encoding Image Encryption Algorithm Based on Chaos by Li Huang, Cong Ding, Zhenjie Bao, Haitao Chen, Changsheng Wan

“…Firstly, the initial value sequence and convolution kernel of the five-dimensional Hamiltonian conservative chaotic system are obtained using SHA-256. …”
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Towards Synthetic Augmentation of Training Datasets Generated by Mobility-on-Demand Service Using Deep Variational Autoencoders by Martin Gregurić, Filip Vrbanić, Edouard Ivanjko

“…The proposed 2-dimensional convolution VAE framework has achieved clustering results comparable with the other analysed approaches. …”
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Drought Stress Grading Model for Apple Rootstock Softwood Cuttings Based on the CU-ICA-Net by Xu Wang, Pengfei Wang, Jianping Li, Hongjie Liu, Xin Yang

“…The dataset used for model training consists of 1200 RGB images of cuttings under different grades of drought stress. The ratio of the training set to the test set is 1:0.7. …”
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Optimizing feature extraction and fusion for high-resolution defect detection in solar cells by Hoanh Nguyen, Tuan Anh Nguyen, Nguyen Duc Toan

“…We also introduce a spatial displacement with shift convolution module, replacing the traditional Multi-Layer Perceptron, which further enhances the model's receptive field and improves precision and recall. …”
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Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings by Jing Gu, Yijuan Pan, Jingjing Zhang

“…In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. …”
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Dynamic Calibration and Validation of an Accelerometer Force Balance for Hypersonic Lifting Models by Prakash Singh, Sharad Trivedi, Viren Menezes, Hamid Hosseini

“…Calibration and validation of the balance were carried out by a convolution technique using hammer pulse test and surface pressure measurements. …”
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