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Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images Research Article

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 630-643 doi: 10.1631/FITEE.2000611

Abstract: As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural networkIn this paper, taking PPI images as an example, a method based on the Marine-Faster R-CNN algorithmFirst, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNNwas optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, denseFinally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm

Keywords： Marine target detection     Navigation radar     Plane position indicator (PPI) images     Convolutional neuralnetwork (CNN)     Faster R-CNN (region convolutional neural network) method    

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting.

Keywords： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network    

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Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 80-96 doi: 10.1007/s11465-020-0614-2

Abstract: Topology optimization is a pioneer design method that can provide various candidates with high mechanicalcomputationally intractable puzzle, especially for the solid isotropic material with penalization (SIMP) methodconvolutional neural network (SRCNN) technique in the framework of SIMP.A combined treatment method that uses 2D SRCNN is built as another speed-up strategy to reduce the highTypical examples show that the high-resolution topology optimization method using SRCNN demonstrates

Keywords： topology optimization     convolutional neural network     high resolution     density-based    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search.Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and isTo solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which canThird, a new training method for ACNN is designed to enhance its stability.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0692-4

Abstract: This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial pistonpressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutionalneural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed methodResults show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of

Keywords： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 882-895 doi: 10.1007/s11709-022-0859-4

Abstract: In this case, the convolutional neural network (CNN) provides a better alternative.This study proposes a slope database generation method based on the LEM.The results show that the slope stability prediction method based on the CNN does not need complex calculationreaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering

Keywords： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

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Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 305-317 doi: 10.1007/s11709-021-0725-9

Abstract: Today, the most commonly used civil infrastructure inspection method is based on a visual assessmentTo overcome this challenge, this paper presents a method for automating concrete damage classificationusing a deep convolutional neural network.The convolutional neural network was designed after an experimental investigation of a wide number ofmodel, with the highest validation accuracy of approximately 94%, was selected as the most suitable network

Keywords： concrete structure     infrastructures     visual inspection     convolutional neural network     artificial intelligence    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: To address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decisiontree methods by rebuilding the output decision layer of CNN according to the hierarchical structuralExperimental results can fully demonstrate the feasibility and superiority of the proposed method.

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: In this paper, a large-scale underwater crack examination method is proposed based on image stitchingIn addition, a purpose of this paper is to design a new convolution method to segment underwater imagesThe graph convolutional neural network (GCN) was used to segment the stitched image.The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutionalneural network    

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optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparametersFirst, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable    

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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1430-6

Abstract:

• UV-vis absorption analyzer was applied in drainage type online recognition.

Keywords： Drainage online recognition     UV-vis spectra     Derivative spectrum     Convolutional neural network    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-sourceIn this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neuralnetwork CNN-BiLSTM-CRF model combined with a feature template (FT).The feature template is used to extract local context features, and a neural network model is used toExperimental results showed that our method can achieve an F-score of 86% on a large-scale network security

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methodsThis article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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A convolutional neural network based approach to sea clutter suppression for small boat detection

Guan-qing Li, Zhi-yong Song, Qiang Fu,liguanqing09@nudt.edu.cn,songzhiyong08@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 10,   Pages 1413-1534 doi: 10.1631/FITEE.1900523

Abstract: In this paper we propose a novel convolutional neural network based dual-activated clutter suppressionThrough this, we can obtain the s (CAMs), which correspond to the positive region of the sea clutter.In addition, we propose a sampling-based data augmentation method and an effective multiclass codingmethod to improve the prediction accuracy.Measurement on real datasets verified the effectiveness of the proposed method.
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Title Author Date Type Operation

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Journal Article

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Journal Article

optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

A convolutional neural network based approach to sea clutter suppression for small boat detection

Guan-qing Li, Zhi-yong Song, Qiang Fu,liguanqing09@nudt.edu.cn,songzhiyong08@nudt.edu.cn

Journal Article