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可见光波段的深度衍射神经网络 Article

陈航, 冯佳楠, 江闽伟, 王逸群, 林杰, 谭久彬, 金鹏

《工程（英文）》 2021年第7卷第10期页码 1485-1493 doi: 10.1016/j.eng.2020.07.032

摘要：

基于衍射光学元件的光学深度学习在并行处理、计算速度和计算效率方面有着独特优势。深度衍射神经网络（D²NN）是其中一项具有里程碑意义的研究工作。D²NN在太赫兹波段通过3D打印进行神经网络的物理固化。鉴于太赫兹波段下存在的粒子间耦合限制和材料损耗，本文将D2NN的应用波段延展至可见光波段，并提出了包括修订公式在内的一般理论，解决了工作波长、人工神经元特征尺寸和加工制备之间的矛盾。在632.8 nm的工作波长下，本文提出了一种新颖的可见光D²NN分类器，可用于原始目标（手写数字0~9）和已更改目标（被遮盖和涂改目标）的目标识别。本文获得的实验分类精度（84%）和数值分类精度（91.57%）量化了理论设计和制造系统性能之间的匹配程度。本文所提出的一般理论模型可将D²NN应用于各种实际问题或设计全新的应用场景。

关键词：光计算光学神经网络深度学习光学机器学习深度衍射神经网络

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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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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

《医学前沿（英文）》 2020年第14卷第4期页码 450-469 doi: 10.1007/s11684-019-0726-4

摘要： As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

关键词： deep learning neural networks pulmonary medical image survey

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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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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

《机械工程前沿（英文）》 2022年第17卷第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling dimensional accuracy cutting force convolutional neural networks coherent noise

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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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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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Novel interpretable mechanism of neural networks based on network decoupling method

《工程管理前沿（英文）》 2021年第8卷第4期页码 572-581 doi: 10.1007/s42524-021-0169-x

摘要： The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.

关键词： neural networks interpretability dynamical behavior network decouple

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

《化学科学与工程前沿（英文）》 2023年第17卷第6期页码 759-771 doi: 10.1007/s11705-022-2269-5

摘要： This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

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深度卷积神经网络高效计算研究进展 Review

Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU

《信息与电子工程前沿（英文）》 2018年第19卷第1期页码 64-77 doi: 10.1631/FITEE.1700789

摘要：近年来迅速发展的深度神经网络已成为许多智能系统的基础工具。同时，深度网络的计算复杂度和资源消耗也在持续增加，这给深度网络的部署带来了严峻挑战，尤其在实时应用中或应用设备资源有限时。因此，网络加速是深度学习领域的热门话题。为提升深度神经网络的硬件性能，最近几年涌现出一大批基于现场可编程门阵列（field-programmable gate array, FPGA）或专用集成电路（application-specific integrated circuit, ASIC）的加速器。本文针对网络加速、压缩、软硬件结合的加速器设计等方面的进展进行了详细而全面的总结。特别地，本文对网络剪枝、低秩估计、网络量化、拟合网络、紧凑网络设计以及硬件加速器进行了深入分析。最后，展望了该领域未来一些研究方向。

关键词：深度神经网络；加速；压缩；硬件加速器

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基于图像的深度学习降雨强度估计方法 Article

尹航, 郑飞飞, 段焕丰, Dragan Savic, Zoran Kapelan

《工程（英文）》 2023年第21卷第2期页码 162-174 doi: 10.1016/j.eng.2021.11.021

摘要：进一步来说，一种称为基于图像的降雨卷积神经网络（image-based rainfall convolutional neural network, irCNN）模型是使用从现有密集传感器（即智能手机或交通摄像头

关键词：城市洪水降雨图像深度学习模型卷积神经网络（CNN）降雨强度

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年第7卷第3期页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery artificial intelligence extraction neural networks supercritical extraction

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

《结构与土木工程前沿（英文）》 2020年第14卷第3期页码 609-622 doi: 10.1007/s11709-020-0623-6

摘要： This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

关键词： Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves

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基于双向深度生成模型和功能磁共振成像数据的大脑编码和解码 Review

杜长德, 李劲鹏, 黄利皆, 何晖光

《工程（英文）》 2019年第5卷第5期页码 948-953 doi: 10.1016/j.eng.2019.03.010

摘要：

通过功能磁共振成像（fMRI）进行大脑编码和解码是视觉神经科学的两个重要方面。尽管以前的研究人员在大脑编码和解码模型方面取得了显著进步，但是现有方法仍需要使用先进的机器学习技术进行改进。例如，传统方法通常会分别构建编码和解码模型，并且容易对小型数据集过度拟合。实际上，有效地统一编码和解码过程可以进行更准确的预测。在本文中，我们首先回顾了现有的编码和解码方法，并讨论了“双向”建模策略的潜在优势。接下来，在体系结构和计算规则方面，我们证明了深度神经网络和人类视觉通路之间存在的对应关系。此外，深度生成模型[如变分自编码器（VAE）和生成对抗网络（GAN）]在大脑编码和解码研究中产生了可喜的成果。最后，我们提出了最初为机器翻译任务设计的对偶学习方法，该方法通过利用大规模未配对数据提高了编码和解码模型的效果。

关键词：大脑编码和解码功能磁共振成像深度神经网络深度生成模型双重学习

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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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标题作者时间类型操作

可见光波段的深度衍射神经网络

陈航, 冯佳楠, 江闽伟, 王逸群, 林杰, 谭久彬, 金鹏

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

Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

期刊论文