检索结果-南通市图书馆

Frontiers of Structural and Civil Engineering 2022年第5期16卷 657-666页

作者： Hamid MIRZAHOSSEIN Milad SASHURPOUR Seyed Mohsen HOSSEINIAN Vahid Najafi Moghaddam GILANI Civil–Transportation Planning Department Faculty of Technical and EngineeringImam Khomeini International University(IKIU)Qazvin 34148-96818Iran School of Civil Engineering Iran University of Science and Technology(IUST)Tehran 13114-16846Iran

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural roads.Multiple Logistic Regression(MLR)was used to predict the likelihood of RTAs.For more accurate prediction,Multi-Layer Perceptron(MLP)and Radius Basis Function(RBF)neural networks were applied.Results indicated that in MLR,the model obtained from the backward method with the correct percent of 84.7%and R2 value of 0.893 was the best method for predicting the likelihood of RTAs.Also,MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead,followed byand then vehicle-motorcycle/bike accidents were the greatest problems.Among the models,MLP had a better performance,so that the prediction accuracy of MLR,MLP,and RBF were 84.7%,96.7%,and 92.1%,respectively.MLP model,due to higher accuracy,showed that the variable of reason of accident had the highest effect on the prediction of accidents,and considering MLR results,the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents.Therefore,motorcyclists and cyclists are more prone to accidents,and appropriate solutions should be adopted to enhance their safety.

关键词： safety rural accidents multiple logistic regression artificial neural networks

Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India

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 2023年第4期10卷 547-563页

作者： Pangam Heramb Pramod Kumar Singh K.V.Ramana Rao A.Subeesh Division of Irrigation and Drainage Engineering ICAR-Central Institute of Agricultural EngineeringBhopal 462038India Department of Irrigation and Drainage Engineering G.B.Pant University of Agriculture and TechnologyPantnagar 263145India Division of Agricultural Mechanization ICAR-Central Institute of Agricultural EngineeringBhopal 462038India

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allocation,etc.Modelling of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 models.The model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the models.All the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing phase.It was found that ANN models were superior to GEP models for the estimation purpose.It was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP models.The ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP models.The comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.

关键词： artificial neural networks Evolutionary algorithms Gene Expression Programming Machine Learning Regression Analysis Reference evapotranspiration models

Near-infrared optoelectronic synapses based on a Te/α-In2Se3 heterojunction for neuromorphic computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2023年第6期66卷 53-62页

作者： Tao YAN Yuchen CAI Yanrong WANG Jia YANG Shuhui LI Xueying ZHAN Fengmei WANG Ruiqing CHENG Feng WANG Jun HE Zhenxing WANG CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology Key Laboratory of Carbon Fiber and Functional Polymers Ministry of EducationBeijing University of Chemical Technology Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Key Laboratory of artificial Micro-and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University

Neuromorphic computing based on artificial optoelectronic synapses has attracted considerable attention owing to its high time/power efficiency and parallel processing capability. However, existing devices are mainly suitable for only the visible range. Here, high-performance near-infrared optoelectronic memories and synapses were demonstrated using Te/α-In2Se3heterostructures. Owing to the entangled ferroelectricitysemiconducting properties of α-In2Se3, whose ferroelectric polarizations can be switched by photocarriers that migrated from the Te near-infrared light absorber, the device could be set into a non-volatile highresistance/low-resistance state through the application of positive gate voltages/near-infrared light pulses.Hence, the device could function as a high-performance photodetector, with a photoresponsive on/off ratio of 5.25 × 104/8.3 × 103and a specific detectivity of 2.6 × 1011/7.5 × 1010Jones at 1550/1940 nm. In addition,the device could function as a multi-state optoelectronic synapse with good stability and high linearity;moreover, using the device, we developed an optoelectronic artificial neural network with high recognition accuracies of 100% and 89.9% for a database composed of 64-pixel letters with 10% and 70% noise levels,respectively. Our work provides a feasible avenue for developing neuromorphic networks applicable in the infrared range.

关键词： near infrared phototransistor two-dimensionalferroelectric semiconductor artificial neural networks optoelectronicsynapses

Prognosis of forest production using machine learning techniques

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2023年第1期10卷 71-84页

作者： Jeferson Pereira Martins Silva Mayra Luiza Marques da Silva Adriano Ribeiro de Mendonca Gilson Fernandes da Silva Antoˆnio Almeida de Barros Junior Evandro Ferreira da Silva Marcelo Otone Aguiar Jeangelis Silva Santos Nı´vea Maria Mafra Rodrigues Federal University of Espirito Santo UFESDepartment of Forestry and Wood ScienceAvenue Governor Lindemberg31629550-000 Jeroˆnimo MonteiroESBrasil b Departamento de Cieˆncias Agra´riasUniversidade Federal de Sa o Joa del-Rei Sete Lagoas CampusHighway MG 424-Km 47Sete LagoasMinas Gerais 35701-970Brazil Federal University of Para(UFPA) University Campus of AltamiraStreet Cel.Jose Porfırio2515Sa o Sebastiao68372-040 AltamiraPABrazil

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level.Although the use of regression models is common in forest measurement,there is a constant search for estimation procedures that provide greater accuracy.Recently,machine learning techniques have been used with satisfactory performance in measuring forests.However,methods such as Adaptive NeuroFuzzy Inference System(ANFIS)and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil.Therefore,it is essential to check whether these techniques can provide gains in terms of accuracy.Thus,this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production.The data used come from continuous forest inventories carried out in stands of eucalyptus clones.The data were divided into 70%for training and 30%for validation.The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means.Besides,training was done with the hybrid algorithm(descending gradient and least squares)with the number of seasons ranging from 1 to 20.Several RFs were trained,varying the number of trees from 50 to 850 and the number of observations by five leaves to 35.artificial neural networks and decision trees were also trained to compare the feasibility of the techniques.The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics:correlation coefficient(r),relative Bias(RB),and the relative root mean square error(RRMSE)in percentage.In general,the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values0.98.The RF presented inferior statistics about the ANFIS for the prognosis of forest production.The Subtractive Clustering(SC)and Fuzzy-C-Means(FCM)algorithms provide accurate baseline and volume projection estimates;both techniques

关键词： artificial Intelligence Ensemble Learning Forest Measurement Forest inventory artificial neural networks Regression

CoolVox:Advanced 3D convolutional neural network models for predicting solar radiation on building facades

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Building Simulation 2022年第5期15卷 755-768页

作者： Jung Min Han Eun Seuk Choi Ali Malkawi Harvard University Graduate School of Design CambridgeUSA Harvard Center for Green Buildings and Cities CambridgeUSA Harvard John A.Paulson School of Engineering and Applied Sciences CambridgeUSA

Data-driven models have become increasingly prominent in the building,architecture,and construction industries.One area ideally suited to exploit this powerful new technology is building performance simulation.Physics-based models have traditionally been used to estimate the energy flow,air movement,and heat balance of buildings.However,physics-based models require many assumptions,significant computational power,and a considerable amount of time to output predictions.artificial neural networks(ANNs)with prefabricated or simulated data are likely to be a more feasible option for environmental analysis conducted by designers during the early design phase.Because ANNs require fewer inputs and shorter computation times and offer superior performance and potential for data augmentation,they have received increased attention for predicting the surface solar radiation on buildings.Furthermore,ANNs can provide innovative and quick design solutions,enabling designers to receive instantaneous feedback on the effects of a proposed change to a building's design.This research introduces deep learning methods as a means of simulating the annual radiation intensities and exposure level of buildings without the need for physics-based engines.We propose the CoolVox model to demonstrate the feasibility of using 3D convolutional neural networks to predict the surface radiation on building facades.The CoolVox model accurately predicted the radiation intensities of building facades under different boundary conditions and performed better than ARINet(with average mean square errors of 0.01 and 0.036,respectively)in predicting the radiation intensity both with(validation error=0.0165)and without(validation error=0.0066)the presence of boundary buildings.

关键词： artificial neural networks 3D convolutional neural networks solar radiation simulation building performance simulation

artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Nuclear Science and Techniques 2023年第10期34卷 91-102页

作者： Yuan-Qiao Li Bao-Ji Zhu Yang Lv Heng Zhu Min Lin Ke-Sheng Chen Li-Jun Xu China Institute of Atomic Energy Beijing 102413China National Key Laboratory for Metrology and Calibration Techniques Beijing 102413China

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

关键词： Pulse shape discrimination artificial neural networks Alpha and beta sources Multi-wire proportional counter Surface emission rate

An Application of Nonlinear Autoregressive(NARX)Model to Predict Adsorbent Bed Temperature of Solar Adsorption Refrigeration System

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

Journal of Systems Science and Systems Engineering 2023年第6期32卷 687-707页

作者： Fatih Bouzeffour Benyoucef Khelidj Unitéde Développement des Equipements Solaires UDES Centre de Développement des Energies Renouvelables CDER42415TipazaAlgeria FIMA University Djilali Bounaama of Khemis-Miliana44225Khemis-MilianaAlgeria

In any solar adsorption refrigeration system,there are three major components:a solar collector adsorbent bed,a condenser and an evaporator.All of those components operate at different temperature levels.A solar collector with a tubular adsorbent configuration is proposed and numerically investigated.In this study,a nonlinear auto-regressive model with exogenous input is applied for the prediction of adsorbent bed temperature during the heating and desorption period.The developed neuronal model uses the MATLAB Network toolbox to obtain a better configuration network,applying multilayer feed-forward,the TANSIG transfer function,and the back-propagation learning algorithm.The input parameters are ambient temperature and the uncontrolled natural factor of solar radiation.The output network contains a variable representing the adsorbent bed temperature.The values obtained from the network model were compared with the experimental data,and the prediction performance of the network model was examined using various performance parameters.The mean square error(MSE)and the statistical coefficient of determination(R2)values are excellent numerical criteria for evaluating the performance of a prediction tool.A well-trained neural network model produces small MSE and higher R2 values.In the current study,the adsorbent bed temperature results obtained from a neural network with a two neuron in hidden layer and the number of the tapped time-delays d=9 provided a reasonable degree of accuracy:MSE=1.0121 and R2=0.99864 and the index of agreement was 0.9988.This network model,based on a high-performance algorithm,provided reliable and high-precision results concerning the predictable temperature of the adsorbent bed.

关键词： artificial neural networks NARX solar collector adsorption refrigerator adsorbent bed temperature

Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1797-1809页

作者： Ding Wang Jiangyu Wang Mingming Zhao Peng Xin Junfei Qiao IEEE Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China

This paper is concerned with a novel integrated multi-step heuristic dynamic programming(MsHDP)algorithm for solving optimal control problems.It is shown that,initialized by the zero cost function,MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman(HJB)equation.Then,the stability of the system is analyzed using control policies generated by MsHDP.Also,a general stability criterion is designed to determine the admissibility of the current control policy.That is,the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP.Further,based on the convergence and the stability criterion,the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly.Besides,actor-critic is utilized to implement the integrated MsHDP scheme,where neural networks are used to evaluate and improve the iterative policy as the parameter architecture.Finally,two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.

关键词： Adaptive critic artificial neural networks Hamilton-Jacobi-Bellman(HJB)equation multi-step heuristic dynamic programming multi-step reinforcement learning optimal control

Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

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

Computers, Materials & Continua 2023年第2期74卷 3455-3470页

作者： Zulqurnain Sabir Sánchez-Chero Manuel Muhammad Asif Zahoor Raja Gilder-Cieza–Altamirano María-Verónica Seminario-Morales Fernández Vásquez JoséArquímedes Purihuamán Leonardo Celso Nazario Thongchai Botmart Wajaree Weera Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Universidad Nacional de Frontera SullanaPiuraPerú Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3Douliou64002YunlinTaiwan Universidad Nacional Autónoma de Chota CajamarcaPerú Universidad Cesar Vallejo TrujilloLa LibertadPerú Universidad Señor de Sipán ChiclayoPerú Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient,called ANNs-SCG.The fractional derivatives have been applied to get more reliable performances of the system.The mathematical form of the biological Leptospirosis system is divided into five categories,and the numerical performances of each model class will be provided by using the ANNs-SCG.The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results.The reference solutions have been obtained by using theAdams numerical scheme.For these investigations,the data selection is performed at 82%for training,while the statics for both testing and authentication is selected as 9%.The procedures based on the recurrence,mean square error,error histograms,regression,state transitions,and correlation will be accomplished to validate the fitness,accuracy,and reliability of the ANNs-SCG scheme.

关键词： Fractional leptospirosis biological model artificial neural networks scale conjugate gradient numerical performances

Swarming Computational Approach for the Heartbeat Van Der Pol Nonlinear System

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第9期72卷 6185-6202页

作者： Muhammad Umar Fazli Amin Soheil Salahshour Thongchai Botmart Wajaree Weera Prem Junswang Zulqurnain Sabir Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,ANNs-PSO-ASA.The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study.An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions.The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA.The designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction period.To perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical method.The statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.

关键词： Particle swarm optimization van der Pol heartbeat system statistical analysis artificial neural networks active-set algorithm numerical computing