检索结果-南通市图书馆

Particuology 2022年第11期70卷 1-9页

作者： Juan F.Saldarriag Yuby Cruz Idoi Estiati Mikel Tellabide Martin Olazar Department of Civil and Environmental Engineering Universidad de los AndesCarrera 1Este#19A-40BogotaColombia Department of Chemical Engineering University of the Basque CountryBarrio Sarriena s/nLeioaSpain

Pressure drop is an essential parameter in the operation of conical spouted beds(CSB)and depends on its geometric factors and materials used.Irregular materials,like biomass,are complex to treat and,unlike other gas–solid contact methods,CSB turn out to be a suitable technology for their treatment.Artificial neural networks were used in this study for the prediction of operating and peak pressure drops,and their performance has been compared with that of empirical correlations reported in the literature.Accordingly,a multi-layer perceptron network with backward propagation was used due to its ability to model non-linear multivariate systems.The fitting of the experimental data of both operating and peak pressure drop was significantly better than those reported in the literature,specifically in the case of the peak pressure drop,with R^(2) being 0.92.Therefore,Artificial neural networks have been proven suitable for the prediction of pressure drop in CSB.

关键词： Pressure drop Artificial neural networks Biomass Spouted bed

Comparing the efficiency of Artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Digital Communications and networks 2022年第2期8卷 162-173页

作者： Wafa Batayneh Enas Abdulhay Mohammad Alothman Jordan University of Science and Technology Mechanical Engineering DepartmentIrbid22110 Jordan Jordan University of Science and Technology Biomedical Engineering DepartmentIrbid22110 Jordan

Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflects the subjects’intention in moving their limbs.Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches.However,these methods have drawbacks that limit their usability.Different from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the Artificial neural networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,impossible.The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals.The presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric study.All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system.The RBFNN has the best performance over the others with a 79.80%mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67%in some joints.Interestingly,the highest accuracy over individual joints is 93.46%,which is achieved via the GRNN.The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.

关键词： Surface electromyography Kinematics estimation Artificial neural networks

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and Artificial neural networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Geo-Spatial Information Science 2022年第3期25卷 353-364页

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El Mostafa Ettachfini Badr Rerhou Faculty of Sciences Chouaib Doukkali UniversityEl JadidaMorocco Agricultural Development Department Doukkala Regional Agricultural Development OfficeEl JadidaMorocco Soil Science Department Hassan II Agronomic and Veterinary InstituteRabatMorocco

In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial neural networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

关键词： Digital soil mapping soil organic matter remote sensing multiple linear regression Artificial neural networks irrigated area Doukkala Morocco

Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial neural networks—An Example of a Heavy Rainfall Event in Henan Province

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Disaster Risk Science 2023年第1期14卷 41-51页

作者： Can Xu Adu Gong Long Liang Xiaoke Song Yi Wang State Key Laboratory of Remote Sensing Science Beijing Normal UniversityBeijing100875China Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal UniversityBeijing100875China Faculty of Geographical Science Beijing Normal UniversityBeijing100875China

Cultural relic conservation capability is an important issue in cultural relic conservation research,and it is critical to decrease the vulnerability of immovable cultural relics to rainfall hazards.Commonly used vulnerability assessment methods are subjective,are mostly applied to regional conditions,and cannot accurately assess the vulnerability of cultural relics.In addition,it is impossible to predict the future vulnerability of cultural relics.Therefore,this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard intensities.An extreme rainfall event in Henan Province in 2021 was selected as an example,with a survey report on the damage to cultural relics as a database.The results imply that the back propagation(BP)neural network-based method of assessing the vulnerability of immovable cultural relics is reliable,with an accuracy rate higher than 92%.Based on this model to predict the vulnerability of Zhengzhou City’s cultural relics,the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were obtained.Among them,the vulnerability of ancient sites is higher than those of other cultural relic types.The assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.

关键词： Artificial neural networks Henan Province Immovable cultural relics Rainfall hazards Vulnerability assessment

The use of Artificial neural networks to estimate seismic damage and derive vulnerability functions for traditional masonry

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2020年第3期14卷 609-622页

作者： Tiago Miguel FERREIRA Joao ESTEVAO Rui MAIO Romeu VICENTE ISISE Institute of Science and Innovation for Bio-Sustainability(IB-S)Department of Civil EngineeringUniversity of MinhoGuimaraes 4800-058Portugal Department of Civil Engineering University of AlgarveFaro 8005-139Portugal RISCO Department of Civil EngineeringUniversity of AveiroAveiro 3810-193Portugal

ABSTRACT 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 asssment 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

Forecasting pest risk level in roses greenhouse:Adaptive neuro-fuzzy inference system vs Artificial neural networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2021年第3期8卷 386-397页

作者： Ahmad Tay Frederic Lafont Jean-Francois Balmat University of Toulon LISUMR CNRS 7020Bt XCS 6058483041 Toulon cedex 9France

The purpose of this study is to establish a system for the prediction of the pests’risk level in a roses greenhouse by applying Artificial neural networks(ANNs)and an Adaptive Neuro-Fuzzy Inference System(ANFIS).Pests in roses greenhouses are known to be fatal to plants if not detected at a premature stage.Early detection could avoid huge agronomic and eco-nomic losses.Though,it could be a difficult task to achieve.The complexities arising from the interactions between variables influencing the development could be a barrier to fulfill the previously mentioned task.The output of the developed system represents the next day?s risk level of Western flower Thrips(WFT)(Frankliniella occidentalis)in a roses green-house.Four explanatory variables,such as internal temperature,internal humidity,today’s pest risk level and human intervention have been considered for this estimation.The main contributions of this study are three fold;providing a daily estimate WFT risk level,reduc-ing the use of pesticides and finally mitigating yield loss.The obtained results were com-pared to each other and to real data.The performance of the models has been evaluated by 3 statistical indicators.Numerical results showed conspicuous performance of both models,indicating their efficiency for pest monitoring.The novelty associated with the sys-tem is the creation of decision support tool for daily risk assessment of WFT.Relying on a small number of variables,this system is a monitoring tool which contributes to help farm-ers early reveal warning signs.In addition,this is a first attempt to employ ANNs and ANFIS for the prediction of WFT。

关键词： Decision making Artificial neural networks ANFIS Risk assessment Integrated pest management

Large eddy simulation of spray combustion using flamelet generated manifolds combined with Artificial neural networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

能源与人工智能(英文)

能源与人工智能(英文) 2020年第2期2卷 33-42页

作者： Yan Zhang Shijie Xu Shenghui Zhong Xue-Song Bai Hu Wang Mingfa Yao State Key Laboratory of Engines Tianjin University135 Yaguan RdTianjin 300350China Department of Energy Sciences Lund UniversityLund 22100Sweden

In the present work,Artificial neural networks(ANN)technique combined with flamelet generated manifolds(FGM)is proposed to mitigate the memory issue of FGM models.A set of ANN models is firstly trained using a 68-species mass fractions in mixture fraction-progress variable space.The ANN prediction accuracy is examined in large eddy simulation(LES)and Reynolds averaged Navier-Stokes(RANS)simulations of spray combustion.It is shown that the present ANN models can properly replicate the FGM table for most of the species mass fractions.The network models with relative error less than 5%are considered in RANS and LES to simulate the Engine Combustion Network(ECN)Spray H flames.Validation of the method is firstly conducted in the framework of RANS.Both non-reacting and reacting cases show the present method predicts very well the trend of spray and combustion process under different ambient temperatures.The results show that FGM-ANN can replicate the ignition delay time(IDT)and lift-off length(LOL)precisely as the conventional FGM method,and the results agree very well with the experiments.With the help of ANN,it is possible to achieve high efficiency and accuracy,with a significantly reduced memory requirement of the FGM models.LES with FGM-ANN is then applied to explore the detailed spray combustion process.Chemical explosive mode analysis(CEMA)approach is used to identify the local combustion modes.It is found that before the spray flame is developed to the steady-state,the high CH_(2)O zone is always associated with ignition mode.However,high CH_(2)O zone together with high OH zone is dominated by the burned mode after the steady-state.The lift-off position is dominated mainly by the diffusion mode.

关键词： Flamelet generated manifolds Artificial neural networks Engine combustion network Spray H Chemical explosive mode analysis

Application of Artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Earthquake Engineering and Engineering Vibration 2018年第4期17卷 903-915页

作者： Meysam Ramezani Akbar Bathaei Amir K.Ghorbani-Tanha School of Civil Engineering College of Engineering University of Tehran

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial neural networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

关键词： Artificial neural networks tuned mass damper wind load auto-regressive model optimal frequency anddamping

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial neural networks in the Presence of Input and Model Uncertainties

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Energy Engineering 2021年第3期118卷 715-725页

作者： Vin Cent Tai Yong Chai Tan Nor Faiza Abd Rahman Hui Xin Che Chee Ming Chia Lip Huat Saw Mohd Fozi Ali Centre for Modelling and Simulation Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling JayaMalaysia Mechanical Engineering Department Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling JayaMalaysia Lee Kong Chian Faculty of Engineering and Science UTARKajangMalaysia Faculty of Civil Engineering Universiti Teknologi MARAShah AlamMalaysia

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An artificial neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

关键词： Long-term load forecasting SARIMA Artificial neural networks uncertainty analysis Malaysia

Enhancing the reusability and interoperability of Artificial neural networks with DEVS modeling and simulation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Modeling, Simulation, and Scientific Computing 2016年第2期7卷 88-108页

作者： David Ifeoluwa Adelani Mamadou Kaba Traore Department of Computer Science African University of Science and Technology AbujaNigeria LIMOS CNRS UMR 6158 Universit´e Blaise PascalClermont-Ferrand 2 Campus des Cezeaux63173 Aubiere

Artificial neural networks(ANNs),a branch of artificial intelligence,has become a very interesting domain since the eighties when back-propagation(BP)learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems.It is used extensively to solve complex nonalgorithmic problems such as prediction,pattern recognition and clustering.However,in the context of a holistic study,there may be a need to integrate ANN with other models developed in various paradigms to solve a problem.In this paper,we suggest discrete event system specification(DEVS)be used as a model of computation(MoC)to make ANN models interoperable with other models(since all discrete event models can be expressed in DEVS,and continuous models can be approximated by DEVS).By combining ANN and DEVS,we can model the complex configuration of ANNs and express its internal workings.Therefore,we are extending the DEVS-based ANN proposed by Toma et al.[A new DEVS-based generic art-ficial neural network modeling approach,The 23rd European Modeling and Simulation Symp.(Simulation in Industry),Rome,Italy,2011]for comparing multiple configuration parameters and learning algorithms and also to do prediction.The DEVS models are described using the high level language for system specification(HiLLS),[Ma¨ıga et al.,A new approach to modeling dynamic structure systems,The 29th European Modeling and Simulation Symp.(Simulation in Industry),Leicester,United Kingdom,2015]a graphical modeling language for clarity.The developed platform is a tool to transform ANN models into DEVS computational models,making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation(MAS).

关键词： Artificial neural networks DEVS Z-schema reusability interoperability HiLLS learning algorithm modeling and simulation