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

《机械工程前沿(英文)》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要: 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 is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

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

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

《结构与土木工程前沿(英文)》 2024年 第18卷 第3期   页码 393-410 doi: 10.1007/s11709-024-1060-8

摘要: The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks (DNNs). A significant challenge in this field is the limited availability of measurement data for full-scale structures, which is addressed in this paper by generating data sets using a reduced finite element (FE) model constructed by SAP2000 software and the MATLAB programming loop. The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices. The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios. To achieve the most generalized surrogate model, the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process. The approach’s effectiveness, efficiency, and applicability are demonstrated by two numerical examples. The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data. Overall, the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms, which can be computationally intensive. This approach also shows potential for broader applications in structural damage detection.

关键词: vibration-based damage detection     deep neural network     full-scale structures     finite element model updating     noisy incomplete modal data    

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

《结构与土木工程前沿(英文)》 2022年 第16卷 第2期   页码 214-223 doi: 10.1007/s11709-021-0800-2

摘要: In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

关键词: concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

《结构与土木工程前沿(英文)》 2022年 第16卷 第10期   页码 1213-1232 doi: 10.1007/s11709-022-0880-7

摘要: The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef , the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.

关键词: FRCM     deep neural networks     confinement effect     strength model     confined concrete    

DAN:一种用于个性化推荐的深度联合神经网络 Research Articles

王旭娜,谭清美

《信息与电子工程前沿(英文)》 2020年 第21卷 第7期   页码 963-980 doi: 10.1631/FITEE.1900236

摘要: 传统推荐系统采用的协同过滤技术存在数据稀疏问题,同时传统的矩阵分解算法简单地将用户和项目分解为潜在因素的线性模型,这些局限性导致传统推荐算法推荐效果有限。在此情况下,出现了基于深度学习的推荐系统。当前深度学习推荐大多利用深度神经网络针对一些辅助信息建模,且在建模过程中根据输入数据类别,分别采用多条映射通路,将原始输入数据映射到潜在向量空间。然而,这些深度神经网络推荐算法忽略了不同类别数据间的联合作用可能对推荐效果产生的潜在影响。针对这一问题,本文提出一种基于多类别信息联合作用的前馈深度神经网络推荐方法——深度联合网络,以解决隐性反馈的推荐问题。具体来说,一方面,本文研究在模型的底层输入中不仅包含用户和项目信息,而且包含更多辅助信息。另一方面,充分考虑不同类别信息的联合作用对推荐效果的影响。在公开数据集上的实验表明,我们提出的方法对现有方法有显著改进。经验证据表明,使用深度联合推荐可以提供更好推荐性能。

关键词: 神经网络;深度学习;DAN;推荐    

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

《结构与土木工程前沿(英文)》 2021年 第15卷 第6期   页码 1453-1479 doi: 10.1007/s11709-021-0767-z

摘要: This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

关键词: damage detection     deep feed-forward neural networks     functionally graded carbon nanotube-reinforced composite plates     modal kinetic energy    

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

《环境科学与工程前沿(英文)》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1667-3

摘要:

● Hybrid deep-learning model is proposed for water quality prediction.

关键词: Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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

《机械工程前沿(英文)》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要: The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词: bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

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

《环境科学与工程前沿(英文)》 2023年 第17卷 第2期 doi: 10.1007/s11783-023-1622-3

摘要:

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

关键词: Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

《结构与土木工程前沿(英文)》 2024年 第18卷 第4期   页码 516-535 doi: 10.1007/s11709-024-1040-z

摘要: Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures. Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time. To address this issue, we explore the potential of deep learning (DL) to increase the efficiency of crack detection and forecasting crack growth. However, there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary. In the paper, we present DL models for identifying cracks, especially on concrete surface images, and for predicting crack propagation. Firstly, SegNet and U-Net networks are used to identify concrete cracks. Stochastic gradient descent (SGD) and adaptive moment estimation (Adam) algorithms are applied to minimize loss function during iterations. Secondly, time series algorithms including gated recurrent unit (GRU) and long short-term memory (LSTM) are used to predict crack propagation. The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results. For evaluation of crack propagation, GRU and LSTM are used as DL models and results show good agreement with the experimental data.

关键词: deep learning     crack segmentation     crack propagation     encoder−decoder     recurrent neural network    

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿(英文)》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词: XGBoost     deep neural network     healthcare     risk prediction    

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

《信息与电子工程前沿(英文)》 2015年 第16卷 第11期   页码 930-939 doi: 10.1631/FITEE.1500125

摘要: Head pose estimation has been considered an important and challenging task in computer vision. In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network (DCNN) for 2D face images. We design an effective and simple method to roughly crop the face from the input image, maintaining the individual-relative facial features ratio. The method can be used in various poses. Then two convolutional neural networks are set up to train the head pose classifier and then compared with each other. The simpler one has six layers. It performs well on seven yaw poses but is somewhat unsatisfactory when mixed in two pitch poses. The other has eight layers and more pixels in input layers. It has better performance on more poses and more training samples. Before training the network, two reasonable strategies including shift and zoom are executed to prepare training samples. Finally, feature extraction filters are optimized together with the weight of the classification component through training, to minimize the classification error. Our method has been evaluated on the CAS-PEAL-R1, CMU PIE, and CUBIC FacePix databases. It has better performance than state-of-the-art methods for head pose estimation.

关键词: Head pose estimation     Deep convolutional neural network     Multiclass classification    

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

《环境科学与工程前沿(英文)》 2023年 第17卷 第2期 doi: 10.1007/s11783-023-1621-4

摘要:

● Used a double-stage attention mechanism model to predict ozone.

关键词: Ozone prediction     Deep learning     Time series     Attention     Volatile organic compounds    

图神经网络与深度强化学习结合的算力网络资源分配方法 Research Article

韩雪莹1,谢明熹2,禹可2,黄小红1,杜宗鹏3,姚惠娟3

《信息与电子工程前沿(英文)》 2024年 第25卷 第5期   页码 701-712 doi: 10.1631/FITEE.2300009

摘要: 由于具有特定计算需求及超低延迟传输需求的实时应用呈现爆炸性增长,算力网络成为热门研究课题。当前算力网络的主要挑战是如何权衡网络资源与计算资源,作出联合最优决策。尽管近年来深度强化学习在网络优化方面取得一定进步,但这些方法仍然受到拓扑结构变化的影响,特别是对未在训练中出现的网络拓扑作出决策。本文提出一个基于图神经网络的深度强化学习框架,使得智能体在进行网络与计算资源联合优化的同时,兼具拓扑泛化性,更加适应网络拓扑的动态变化。借助图神经网络的泛化优势,该方法可在变动的网络拓扑中运行,且相比基于传统深度强化学习的方法具有更强的优化决策能力。

关键词: 算力网络;路由优化;深度学习;图神经网络;资源分配    

Machine vision-based automatic fruit quality detection and grading

《农业科学与工程前沿(英文)》 doi: 10.15302/J-FASE-2023532

摘要:

● A machine vision-based prototype system was developed for fruit grading.

关键词: Computer and machine vision     convolution neural network     deep learning     defective fruit detection     fruit grading     microcontroller    

标题 作者 时间 类型 操作

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

期刊论文

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

期刊论文

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

期刊论文

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

期刊论文

DAN:一种用于个性化推荐的深度联合神经网络

王旭娜,谭清美

期刊论文

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

期刊论文

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

期刊论文

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

期刊论文

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

期刊论文

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

期刊论文

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

期刊论文

图神经网络与深度强化学习结合的算力网络资源分配方法

韩雪莹1,谢明熹2,禹可2,黄小红1,杜宗鹏3,姚惠娟3

期刊论文

Machine vision-based automatic fruit quality detection and grading

期刊论文