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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

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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

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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 r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f, the elastic modulus of mortar E_m. 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

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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

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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

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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

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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

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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

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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

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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

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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

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深度神经网络加速器体系结构概述 Review

陈怡然, 谢源, 宋凌皓, 陈凡, 唐天琪

《工程（英文）》 2020年第6卷第3期页码 264-274 doi: 10.1016/j.eng.2020.01.007

摘要：

最近，由于可使用的大数据和计算能力的快速增长，人工智能重新获得了巨大的关注和投资。机器学习（ML）方法已成功应用于解决学术界和工业界的许多问题。尽管大数据应用的高速增长为ML的发展提供动力，但它也给传统计算机系统带来了数据处理速度和可扩展性方面的严峻挑战。专门为AI应用程序设计的计算平台已经从对冯·诺依曼（von Neumann）平台的补充发展到必备的独立技术解决方案。这些平台属于更大的类别，被称为“专有域计算”，专注于针对AI的特定定制。在本文中，我们特别总结了用于深度神经网络（DNN）的加速器设计（即DNN加速器）的最新进展。我们从计算单元、数据流优化、网络模型、基于新兴技术的体系结构以及针对新兴应用的加速器等方面讨论支持DNN执行的各种体系结构。我们还提供了有关AI芯片设计未来趋势的展望。

关键词：深度神经网络特定领域体系结构加速器

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2020年第14卷第6期页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method constitutive model feedforward neural network recurrent neural network

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深度学习中的对抗性攻击和防御 Feature Article

任奎, Tianhang Zheng, 秦湛, Xue Liu

《工程（英文）》 2020年第6卷第3期页码 346-360 doi: 10.1016/j.eng.2019.12.012

摘要：

在深度学习（deep learning, DL）算法驱动的数据计算时代，确保算法的安全性和鲁棒性至关重要。最近，研究者发现深度学习算法无法有效地处理对抗样本。

关键词：机器学习深度神经网络对抗实例对抗攻击对抗防御

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Data-driven approach to solve vertical drain under time-dependent loading

《结构与土木工程前沿（英文）》 2021年第15卷第3期页码 696-711 doi: 10.1007/s11709-021-0727-7

摘要： Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

关键词： vertical drain artificial neural network time-dependent loading deep learning network genetic algorithm particle swarm optimization

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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

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

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

期刊论文

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

期刊论文

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

期刊论文

深度神经网络加速器体系结构概述

陈怡然, 谢源, 宋凌皓, 陈凡, 唐天琪

期刊论文

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

期刊论文

深度学习中的对抗性攻击和防御

任奎, Tianhang Zheng, 秦湛, Xue Liu

期刊论文

Data-driven approach to solve vertical drain under time-dependent loading

期刊论文

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