Search | Engineering

订阅投稿

首页工程期刊工程焦点工程成就工程前沿关于我们 English

资源类型

期刊论文 485

年份

2024 1

2023 58

2022 48

2021 50

2020 48

2019 30

2018 28

2017 32

2016 15

2015 21

2014 12

2013 13

2012 12

2011 5

2010 4

2009 7

2008 13

2007 14

2006 9

2005 14

展开︾

关键词

神经网络 23

人工神经网络 5

优化 5

人工智能 4

BP神经网络 3

仿真 3

微波遥感 2

控制 2

机器学习 2

模糊神经网络 2

深度神经网络 2

算法 2

系统 2

网络 2

网络安全 2

计算机 2

长短期记忆网络 2

预测 2

10kV高压电力电缆 1

展开︾

检索范围：

排序：展示方式：

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

HTML PDF 收藏

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

《环境科学与工程前沿（英文）》 2023年第17卷第7期 doi: 10.1007/s11783-023-1688-y

摘要：

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

关键词： Water quality prediction Grasshopper optimization algorithm Variational mode decomposition Long short-term memory neural network

HTML PDF 收藏

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

《结构与土木工程前沿（英文）》 2020年第14卷第6期页码 1316-1330 doi: 10.1007/s11709-020-0646-z

摘要： In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.

关键词： concrete high temperature strength properties deep learning stacked auto-encoders LSTM network

HTML PDF 收藏

ECGID：一种基于自适应粒子群优化算法和双向LSTM网络的个体身份识别模型 Research Article

张烨菲,赵治栋,邓艳军,张晓红,张钰

《信息与电子工程前沿（英文）》 2021年第22卷第12期页码 1551-1684 doi: 10.1631/FITEE.2000511

摘要：经4种LSTM网络模型和机器学习算法的实验对比分析，证实所提算法在抑制过拟合和特征自学习方面存在一定优势，训练集、验证集和测试集的平均识别率分别为97.71%、99.41%和98.89%。

关键词：心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法

HTML PDF 收藏

一种基于充电模式识别的电动汽车充电时间预测方法 Research Article

李春喜1,傅莹颖1,崔向科2,葛泉波3,4,5

《信息与电子工程前沿（英文）》 2023年第24卷第2期页码 299-313 doi: 10.1631/FITEE.2200212

摘要：首先，基于动态加权密度峰值聚类（DWDPC）和随机森林融合的智能算法对车辆充电模式进行分类；然后，采用改进的简化粒子群优化算法（ISPSO）和强跟踪滤波器（STF），对LSTM神经网络进行优化，构建了一种高性能的充电时间预测方法；最后，通过实际工程数据对所提出的ISPSO-LSTM-STF方法进行了验证。

关键词：充电模式；充电时长；随机森林；长短期记忆网络（LSTM）；简化粒子群优化算法（SPSO）

HTML PDF 收藏

Ldformer：面向长期电力预测的并行神经网络模型

田冉,李新梅,马忠彧,刘颜星,王晶霞,王楚

《信息与电子工程前沿（英文）》 2023年第24卷第9期页码 1287-1301 doi: 10.1631/FITEE.2200540

摘要：准确的长期电力预测对电网决策运行和用户用电管理非常重要，可保证电力系统的可靠供电和电网经济的可靠运行。然而，大多数时间序列预测模型在数据量大、预测精度高的长时间序列预测任务中表现不佳。为了应对这一挑战，提出名为LDformer的并行时间序列预测模型。首先，将Informer与长短期记忆网络相结合，以获得时间序列的深度表达能力。其次，提出并行编码器模块提高模型鲁棒性，并将卷积层与注意力机制相结合，以避免注意力机制中的值冗余。最后，提出结合UniDrop的概率稀疏注意力机制，以减少计算开销并减轻序列中一些关键连接丢失的风险。在5个真实数据集上的实验结果显示，在不同的长时间序列预测任务中，LDformer大部分结果都优于最先进的基线结果。

关键词：长期电力预测长短期记忆网络 UniDrop 自注意力机制

HTML 收藏

一种用于淮河上游日径流预测的增强型LSTM模型 Article

满媛媛, 杨勤丽, 邵俊明, 王国庆, 白林龙, 薛运宏

《工程（英文）》 2023年第24卷第5期页码 230-239 doi: 10.1016/j.eng.2021.12.022

摘要：为此，本研究提出了一种用于日径流预测的增强型长短期记忆（LSTM）模型，其中集成了特征提取器并引入了新的损失函数。具体而言，为每个气象站建立由三个LSTM网络组成的特征提取器，旨在提取每个气象站输入数据的时间特征。本研究以中国淮河流域上游为研究对象，利用增强型LSTM模型进行1960—2016 年的日径流预测。结果表明，增强型LSTM模型表现良好，纳什效率系数（NSE）在验证期（2005 年11 月至2016 年12 月）达到了0.917~0.924，优于广泛使用的集总式水文模型（AWBM、Sacramento以PES 作为损失函数的增强型LSTM在极端径流预测方面表现最佳，在洪水期间的平均NSE为0.873。此外，海拔较高的气象站的降水比距离出水口最近的气象站对径流预测的影响更大。

关键词：径流预测长短期记忆网络淮河上游流域极端径流损失函数

HTML PDF 收藏

一种基于非线性时空效应的个性化下一个兴趣点推荐方法

孙曦,吕志民

《信息与电子工程前沿（英文）》 2023年第24卷第9期页码 1273-1286 doi: 10.1631/FITEE.2200304

摘要：该模型使用具有注意力机制的长短期记忆网络（LSTM）作为基本框架，并将时空信息以编码形式引入模型。在编码信息过程中，使用指数型衰减因子刻画用户兴趣随时间和距离的非线性漂移特性。

关键词：兴趣点推荐时空效应长短期记忆网络注意力机制

HTML 收藏

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

HTML PDF 收藏

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

《机械工程前沿（英文）》 2023年第18卷第2期 doi: 10.1007/s11465-022-0736-9

摘要： Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition fault localization multi-sensor relations network analysis graph neural network

HTML PDF 收藏

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

HTML PDF 收藏

Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

《化学科学与工程前沿（英文）》 2012年第6卷第4期页码 484-502 doi: 10.1007/s11705-012-1221-5

摘要： Heat (energy), water (mass), and work (pressure) are the most fundamental utilities for operation units in chemical plants. To reduce energy consumption and diminish environment hazards, various integration methods have been developed. The application of heat exchange networks (HENs), mass exchange networks (MENs), water allocation heat exchange networks (WAHENs) and work exchange networks (WENs) have resulted in the significant saving of energy and water. This review presents the main works related to each network. The similarities and differences of these networks are also discussed. Through comparing and discussing these different networks, this review inspires researchers to propose more efficient and convenient methods for the design of existing exchange networks and even new types of networks including multi-objective networks for the system integration in order to enhance the optimization and controllability of processes.

关键词： process system engineering integration methods heat exchange network mass exchange network work exchange network

HTML PDF 收藏

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

HTML PDF 收藏

Identifying spreading influence nodes for social networks

《工程管理前沿（英文）》页码 520-549 doi: 10.1007/s42524-022-0190-8

摘要： The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.

关键词： complex network network science spreading influence machine learning

HTML PDF 收藏

信息网络——现代信息工程学的前沿

钟义信

《中国工程科学》 1999年第1卷第1期页码 24-29

摘要：

信息网络正在各地迅猛崛起，并以史所罕见的规模和速度生长成为世界性社会基础结构，深刻地改变着人们的生产方式、工作方式、学习方式、交往方式、生活方式和思维方式，成为工程学界以至整个社会普遍关注的集点、热点和前沿。文章旨在从理论上廓清信息网络的概念，阐明为什么信息网络对于科学技术的进步、对于世界经济和人类社会的发展能够产生如此巨大和深远的作用与影响。在此基础上，论述信息网络在现代工程学中的作用与地位，以及信息网络工程学在当前的主要研究内容和方向。

关键词：信息网络智能化社会生产工具网络时代信息网络工程学