资源类型

期刊论文 1280

会议视频 39

会议信息 1

年份

2024 2

2023 99

2022 129

2021 111

2020 106

2019 85

2018 77

2017 66

2016 55

2015 64

2014 54

2013 45

2012 45

2011 37

2010 51

2009 47

2008 41

2007 37

2006 28

2005 27

展开 ︾

关键词

人工智能 47

数学模型 13

模型试验 9

数值模拟 8

机器学习 7

模型 7

信息技术 6

智能制造 5

COVID-19 4

不确定性 4

群体智能 4

GM(1 3

工程管理 3

新一代人工智能 3

深度学习 3

能源 3

计算机模拟 3

1)模型 2

展开 ︾

检索范围：

排序： 展示方式：

Determination of shear strength of steel fiber RC beams: application of data-intelligence models

Abeer A. AL-MUSAWI

《结构与土木工程前沿（英文）》 2019年 第13卷 第3期   页码 667-673 doi: 10.1007/s11709-018-0504-4

摘要： Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process. This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization. For the considered data-intelligence models, the input matrix attribute is one of the central element in attaining accurate predictive model. Hence, various input attributes are constructed to model the shear strength “as a targeted variable”. The modeling is initiated using historical published researches steel fiber reinforced concrete beams information. Seven variables are used as input attribute combination including reinforcement ratio ( ), concrete compressive strength ( ), fiber factor ( ), volume percentage of fiber ( ), fiber length to diameter ratio ( ) effective depth ( ), and shear span-to-strength ratio ( ), while the shear strength ( ) is the output of the matrix. The best network structure obtained using the network having ten nodes and one hidden layer. The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams. In more representable details, the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82, respectively.

关键词： hybrid intelligence model     shear strength     prediction     steel fiber reinforced concrete    

HTML PDF 收藏

人工智能走向2.0

潘云鹤

《工程（英文）》 2016年 第2卷 第4期   页码 409-413 doi: 10.1016/J.ENG.2016.04.018

摘要：

随着互联网的普及、传感网的渗透、大数据的涌现、信息社区的崛起，以及数据和信息在人类社会、物理空间和信息空间之间的交叉融合与相互作用，当今人工智能(AI) 发展所处信息环境和数据基础已经发生了深刻变化，人工智能的目标和理念正面临重要调整，人工智能的科学基础和实现载体也面临新的突破，人工智能正进入一个新的阶段。这个源于传统而又与之不同的人工智能新阶段被称为人工智能2.0(AI 2.0)。本文从人工智能60 年的发展历史出发，通过分析促成人工智能2.0形成的外部环境与目标的转变，分析技术萌芽，提出了人工智能2.0 的核心理念，并结合中国发展的社会需求与信息环境特色，给出了发展人工智能2.0 的建议。

关键词： 人工智能2.0     大数据     群体智能     跨媒体     人机混合智能     无人智能系统    

HTML PDF 收藏

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid

Nitin Kumar SAXENA,Ashwani Kumar SHARMA

《能源前沿（英文）》 2015年 第9卷 第4期   页码 472-485 doi: 10.1007/s11708-015-0373-7

摘要： It is well recognized that the voltage stability of a power system is affected by the load model and hence, to effectively analyze the reactive power compensation of an isolated hybrid wind-diesel based power system, the loads need to be considered along with the generators in a transient analysis. This paper gives a detailed mathematical modeling to compute the reactive power response with small voltage perturbation for composite load. The composite load is a combination of the static and dynamic load model. To develop this composite load model, the exponential load is used as a static load model and induction motors (IMs) are used as a dynamic load model. To analyze the dynamics of IM load, the fifth, third and first order model of IM are formulated and compared using differential equations solver in Matlab coding. Since the decentralized areas have many small consumers which may consist large numbers of IMs of small rating, it is not realistic to model either a single large rating unit or all small rating IMs together that are placed in the system. In place of using a single large rating IM, a group of motors are considered and then the aggregate model of IM is developed using the law of energy conservation. This aggregate model is used as a dynamic load model. For different simulation studies, especially in the area of voltage stability with reactive power compensation of an isolated hybrid power system, the transfer function of the composite load is required. The transfer function of the composite load is derived in this paper by successive derivation for the exponential model of static load and for the fifth and third order IM dynamic load model using state space model.

关键词： isolated hybrid power system (IHPS)     composite load model     static load     dynamic load     induction motor load model     aggregate load    

HTML PDF 收藏

Diffusion process in enzyme–metal hybrid catalysts

《化学科学与工程前沿（英文）》 2022年 第16卷 第6期   页码 921-929 doi: 10.1007/s11705-022-2144-4

摘要： Enzyme–metal hybrid catalysts bridge the gap between enzymatic and heterogeneous catalysis, which is significant for expanding biocatalysis to a broader scope. Previous studies have demonstrated that the enzyme–metal hybrid catalysts exhibited considerably higher catalytic efficiency in cascade reactions, compared with that of the combination of separated enzyme and metal catalysts. However, the precise mechanism of this phenomenon remains unclear. Here, we investigated the diffusion process in enzyme–metal hybrid catalysts using Pd/lipase-Pluronic conjugates and the combination of immobilized lipase (Novozyme 435) and Pd/C as models. With reference to experimental data in previous studies, the Weisz–Prater parameter and efficiency factor of internal diffusion were calculated to evaluate the internal diffusion limitations in these catalysts. Thereafter, a kinetic model was developed and fitted to describe the proximity effect in hybrid catalysts. Results indicated that the enhanced catalytic efficiency of hybrid catalysts may arise from the decreased internal diffusion limitation, size effect of Pd clusters and proximity of the enzyme and metal active sites, which provides a theoretical foundation for the rational design of enzyme–metal hybrid catalysts.

关键词： enzyme–metal hybrid catalyst     internal diffusion     proximity effect     kinetic model    

HTML PDF 收藏

Statics of levitated vehicle model with hybrid magnets

Desheng LI, Zhiyuan LU, Tianwu DONG

《机械工程前沿（英文）》 2009年 第4卷 第1期   页码 35-39 doi: 10.1007/s11465-009-0002-4

摘要： By studying the special characteristics of permanent and electronic magnets, a levitated vehicle model with hybrid magnets is established. The mathematical model of the vehicle is built based on its dynamics equation by studying its machine structure and working principle. Based on the model, the basic characteristics and the effect between the excluding forces from permanent magnets in three different spatial directions are analyzed, statics characteristics of the interference forces in three different spatial directions are studied, and self-adjusting equilibrium characteristics and stabilization are analyzed. Based on the structure above, the vehicle can levitate steadily by control system adjustment.

关键词： magnetic levitation     permanent magnet     modeling     equilibrium    

HTML PDF 收藏

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

《医学前沿（英文）》 2020年 第14卷 第4期   页码 488-497 doi: 10.1007/s11684-020-0762-0

摘要： Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

关键词： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

HTML PDF 收藏

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

HTML PDF 收藏

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    

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

新一代人工智能引领下的制造业新模式与新业态研究

“新一代人工智能引领下的制造业新模式新业态研究”课题组

《中国工程科学》 2018年 第20卷 第4期   页码 66-72 doi: 10.15302/J-SSCAE-2018.04.011

摘要：

在新一代人工智能技术引领下，制造业的生产技术、组织方式、竞争策略等，都将面临重大调整，为制造业新模式与新业态的形成提供了可能。受新一代人工智能技术驱动，制造业实践中不断涌现由服务而产生的新模式新业态是本课题的研究核心。课题重点围绕由开展智能服务而产生的新模式和新业态进行研究，分析了在人工智能技术引领下，制造业的模式与业态的演进趋势，新模式与新业态的典型类型，支撑性、关键性技术；提出了新模式与新业态的发展方针、目标与途径。根据我国工业领域的发展基础与现状，选择远程运维服务和规模定制服务作为突破，提出两大业务模式在相关领域的发展方向以及发展目标，并提出相关政策建议。

关键词： 人工智能     制造业     新模式     新业态    

HTML PDF 收藏

The study of hybrid model identification, computation analysis and fault location for nonlinear dynamic

XIE Hong, HE Yi-gang, ZENG Guan-da

《机械工程前沿（英文）》 2006年 第1卷 第2期   页码 233-237 doi: 10.1007/s11465-006-0003-5

摘要： This paper presents the hybrid model identification for a class of nonlinear circuits and systems via a combination of the block-pulse function transform with the Volterra series. After discussing the method to establish the hybrid model and introducing the hybrid model identification, a set of relative formulas are derived for calculating the hybrid model and computing the Volterra series solution of nonlinear dynamic circuits and systems. In order to significantly reduce the computation cost for fault location, the paper presents a new fault diagnosis method based on multiple preset models that can be realized online. An example of identification simulation and fault diagnosis are given. Results show that the method has high accuracy and efficiency for fault location of nonlinear dynamic circuits and systems.

关键词： block-pulse function     nonlinear     multiple     diagnosis     combination    

HTML PDF 收藏

Convergence performance comparisons of PID, MRAC, and PID+MRAC hybrid controller

Dan ZHANG,Bin WEI

《机械工程前沿（英文）》 2016年 第11卷 第2期   页码 213-217 doi: 10.1007/s11465-016-0386-x

摘要：

This study proposes a hybrid controller by combining a proportional-integral-derivative (PID) control and a model reference adaptive control (MRAC), which named as PID+MRAC controller. The convergence performances of the PID control, MRAC, and hybrid PID+MRAC are also compared. Through the simulation in Matlab, the results show that the convergence speed and performance of the MRAC and the PID+MRAC controller are better than those of the PID controller. In addition, the convergence performance of the hybrid control is better than that of the MRAC control.

关键词： proportional-integral-derivative (PID) control     model reference adaptive control     hybrid control     convergence speed     comparison    

HTML PDF 收藏

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction     ANN     BOA     FEM     experimental modal analysis    

HTML PDF 收藏

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1299-1315 doi: 10.1007/s11709-020-0712-6

摘要： The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

关键词： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain curve    

HTML PDF 收藏

Evaluation of factors influencing soluble microbial product in submerged MBR through hybrid ASM model

Fangyue LI, Joachim BEHRENDT, Knut WICHMANN, Ralf OTTERPOHL

《环境科学与工程前沿（英文）》 2009年 第3卷 第2期   页码 226-235 doi: 10.1007/s11783-009-0008-5

摘要： In this study, a mathematical model was established to predict the formation of the soluble microbial product (SMP) in a submerged membrane bioreactor. The developed model was calibrated under the reference condition. Simulation results were in good agreement with the measured results under the reference condition. The calibrated model was then used in the scenario studies to evaluate the effect of three chosen operating parameters: hydraulic retention time (HRT), dissolved oxygen concentration, and sludge retention time (SRT). Simulation results revealed that the SMP dominated the soluble organic substances in the supernatant. The scenario studies also revealed that the HRT can be decreased to 1 h without deteriorating the effluent quality; dissolved oxygen concentration in the reactor can be kept at 2-3 mg/L to maintain the effluent quality, reduce the content of SMP, and minimize operating costs; the optimal SRT can be controlled to 10-15 d to achieve complete nitrification process, less membrane fouling potential, and acceptable organic removal efficiency.

关键词： hybrid activated sludge model (ASM)     membrane bioreactor (MBR)     soluble microbial product (SMP)    

HTML PDF 收藏

标题 作者 时间 类型 操作

Determination of shear strength of steel fiber RC beams: application of data-intelligence models

Abeer A. AL-MUSAWI

期刊论文

人工智能走向2.0

潘云鹤

期刊论文

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid

Nitin Kumar SAXENA,Ashwani Kumar SHARMA

期刊论文

Diffusion process in enzyme–metal hybrid catalysts

期刊论文

Statics of levitated vehicle model with hybrid magnets

Desheng LI, Zhiyuan LU, Tianwu DONG

期刊论文

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive

期刊论文

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

期刊论文

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

期刊论文

新一代人工智能引领下的制造业新模式与新业态研究

“新一代人工智能引领下的制造业新模式新业态研究”课题组

期刊论文

The study of hybrid model identification, computation analysis and fault location for nonlinear dynamic

XIE Hong, HE Yi-gang, ZENG Guan-da

期刊论文

Convergence performance comparisons of PID, MRAC, and PID+MRAC hybrid controller

Dan ZHANG,Bin WEI

期刊论文

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

期刊论文

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

期刊论文

Evaluation of factors influencing soluble microbial product in submerged MBR through hybrid ASM model

Fangyue LI, Joachim BEHRENDT, Knut WICHMANN, Ralf OTTERPOHL

期刊论文