检索结果-南通市图书馆

Frontiers of Structural and Civil Engineering 2017年第1期11卷 90-99页

作者： Faezehossadat KHADEMI Mahmoud AKBARI Sayed Mohammadmehdi JAMAL Mehdi NIKOO Department of Civil Architectural and Environmental Engineering Illinois Institute of Technology Chicago IL 60616 USA Civil Engineering Department University of Kashan Kashan *** Iran Department of Civil Engineering University of Hormozgan Bandar Abbas 3995 IRAN Ahvaz Branch Islamic Azad University Ahvaz *** IRAN

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model （MLR）, artificial neural network （ANN）, and adaptive neuro-fuzzy inference system （ANFIS） are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis （SA） for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis （SA）

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

multiple linear regression Application on the Inter-Network Settlement of Internet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2006年第2期13卷 103-107页

作者： YANG Qing-feng ZHANG Qi-xiang Lǖ Ting-jie School of Management and Economics Beijing University of Posts and Telecommunications Beijing 100876 P.R. China

This paper develops an analytical framework to explain the lnternet interconnection settlement issues. The paper shows that multiple linear regression can be used in assessing the network value of lnternet Backbone Providers （ IBPs）. By using the exchange rate of each network, we can define a rate of network value, which reflects the contribution of each network to interconnection and the interconnected network resource usage by each of the network.

关键词： multiple linear regression network value internet settlement

Combined model based on optimized multi-variable grey model and multiple linear regression

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 Engineering and Electronics 2011年第4期22卷 615-620页

作者： Pingping Xiong Yaoguo Dang Xianghua wu Xuemei Li College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China College of Mathematics and Physics Nanjing University of Information Science and Technology Nanjing 210044 P. R. China

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

关键词： multi-variable grey model （MGM（1,m）） backgroundvalue optimization multiple linear regression combined predic-tion model.

A study of the mixed layer of the South China Sea based on the multiple linear regression

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

Acta Oceanologica Sinica 2012年第6期31卷 19-31页

作者： DUAN Rui YANG Kunde MA Yuanliang HU Tao College of Marine Northwestern Polytechnical UniversityXi'an 710072China Key Laboratory of Underwater Acoustic Environment Institute of AcousticsChinese Academy of SciencesBeijing 100190China

multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.

关键词： mixed layer multiple linear regression South China Sea vertical mixing model

Battery State of Health Estimation Based on multiple linear regression by Particle Swarm Optimization

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

Battery State of Health Estimation Based on Multiple Linear ...

Study on the Application of Order Agriculture in Precision Poverty Alleviation Based on multiple linear regression Method

第40届中国控制会议

作者： Peng Huang Pingwei Gu Xuebin Cao Kun Jiang Bin Duan Chenghui Zhang School of Control Science and Engineering Shandong University

State of health of battery is crucial for electric vehicles, which has been widely studied through many different methods where incremental capacity analysis（ICA） is a common method for battery state of health estimation. However,previous methods are difficult to find suitable feature information from multiple features to make the estimation result better,especially considering that the ICA curves of different batteries have different peak numbers and positions. In this paper, in order to acquire accurate peak coordinates, the Gaussian filtering is used to make the incremental capacity（IC） curve smoother, and then the method that using multiple linear regression by particle swarm optimization（PSO） is proposed to solve the problems mentioned above. Compared with the traditional multiple linear regression, multiple linear regression by particle swarm optimization selects the most appropriate variables to improve the accuracy of estimation. In the experiment, a group of peak data of series module of battery is selected for linear regression to get the optimal variables and corresponding parameters, and then the corresponding verification is carried out by using the data of six different aging states of another series module of battery. The experimental results show that except the sixth group of the state of health estimation error is 2.09%, the estimation error of the remaining five groups is less than 2%, so this method has high accuracy and adaptability for series module of battery.

关键词： battery particle swarm optimization multiple linear regression state of health Gaussian filtering

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Study on the Application of Order Agriculture in Precision P...

不同解释变量对幼儿园班级课程质量的影响及其差异贡献率

作者： Sun Bingqing Government Management Theory and Performance Evaluation Zhengzhou University

In 2020,China will fully realize a well-off society.To achieve this goal,China’s poverty alleviation work has entered a crucial stage.Under the background of the Internet era,the "Internet+Order Agriculture+Precision Poverty Alleviation"model has been formed,and the order agriculture has played an important role in promoting the development of precision poverty alleviation.Based on 1000 questionnaires in Hubei Province,this paper quantitatively studied the impact of contract farming on farmers’ income levels using multiple linear regression methods.The research shows that there is a positive correlation between farmers’ participation in contract farming and farmers’ income,and the correlation coefficient is high,indicating that farmers’ participation in contract farming can significantly increase their income.Therefore,the promotion of contract farming is conducive to the in-depth development of regional precision poverty alleviation,and can help farmers solve poverty problems.

关键词： Order Agriculture Precision Poverty Alleviation multiple linear regression

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

幼儿教育导读（教育科学版）

Research on the Impact of Employment on GDP Based on multiple linear regression Model

幼儿教育导读（教育科学版） 2023年第6期

作者：原晋霞王希

班级是幼儿园教育的基本单位，班级课程质量直接关乎幼儿发展质量。班级课程质量差异受诸多因素影响。本研究基于我国5个省（自治区）100所幼儿园294个班级的问卷调查和教育质量评估数据，运用多元线性回归和夏普里值分解方法，探讨幼... 详细信息

班级是幼儿园教育的基本单位，班级课程质量直接关乎幼儿发展质量。班级课程质量差异受诸多因素影响。本研究基于我国5个省（自治区）100所幼儿园294个班级的问卷调查和教育质量评估数据，运用多元线性回归和夏普里值分解方法，探讨幼儿园和班级不同层面变量对班级课程质量的影响及其对班级课程质量差异的贡献率。结果发现，幼儿园层面变量对班级课程质量差异的相对贡献率远高于班级层面变量，其中幼儿园的自评改进和教师专业发展支持对班级课程质量具有极其显著的正向影响，园长领导力对班级课程质量具有非常显著的正向影响。班级层面变量中的教师专业背景对班级课程质量具有非常显著的正向影响，师幼比对班级课程质量则具有非常显著的负向影响，大额班级的课程质量显著低于非大额班级。教师学历、教龄、编制及工资待遇对班级课程质量虽然也具有正向影响，但是均未达到显著性水平。为提高班级课程质量，我国幼儿园和教育管理部门应加强园所内部评价，为教师专业发展提供有效支持，提高园长专业领导力，改善教师专业背景，努力消除大班额现象。

关键词：班级课程质量幼儿园教育质量多元线性回归夏普里值分解　　class curriculum quality kindergarten education quality multiple linear regression Sharpley value decomposition

来源：人大复印报刊资料评论

在线全文

人大复印报刊资料

学校读者我要写书评

暂无评论

经济管理学刊（中英文版） 2022年第1期11卷 1-8页

作者： Wei Zheng Yao Xu Jun Yang Shuhuan Yang School of mathematics and computer science Chuxiong Normal UniversityChuxiong675000China

In order to study the impact of employed persons in various industries on regional GDP,based on the data of GDP in various regions and employed persons divided by industries in various regions in 2019,the employed persons are divided into seven categories,and the multiple linear regression model of GDP in various regions of China on employed persons in various industries is established by using the methods of multiple linear regression analysis and cluster analysis,It also analyzes the impact of employees in various industries on the GDP of various regions.

关键词： GDP Employees in Various Industries multiple linear regression

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by 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

Computational Chemistry 2022年第4期10卷 186-202页

作者： Yafigui Traoré Jean Missa Ehouman Mamadou Guy-Richard Koné Donourou Diabaté Nahossé Ziao Laboratory of Thermodynamics and Environmental Physico-Chemistry Nangui Abrogoua University Abidjan Ivory Coast Training and Research Unit Biological Sciences University Peleforo Gon Coulibaly Korhogo Ivory Coast Ivorian Research Group in Disease Modeling (GIR2M) Abidjan Ivory Coast

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μD, the energy of the highest occupied molecular orbital EHOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The multiple linear regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R2=91.28%, R2aj=89.11%, RMCE = 0.2831, R2ext=86.50% and R2=98.22%, R2aj=97.75%, RMCE = 0.1131, R2ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

关键词： Anti-Inflammatory Activity multiple linear regression Artificial Neural Network QSAR