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Rmote sensing of leaf water content for winter wheat using grey relational analysis (GRA), stepwise regression method (SRM) and partial least squares (PLS)

Rmote sensing of leaf water content for winter wheat using g...

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1st International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Jin, Xiuliang Xu, Xinguang Natl Engn Res Ctr Informat Technol Agr Beijing Peoples R China

ISBN: (纸本)9781467324946

Leaf water content (LWC) is an important parameter for evaluating crop health and predicting crop yield. The objective of this study was to compare two methods for the precision of estimating LWC in winter wheat by combining stepwise regression method and partial least squares (SRM-PLS) or PLS based on the relational degree of grey relational analysis (GRA) between water vegetation indexes (WVIs) and LWC. Firstly, using data from 2008 was utilized to analyze the grey relationships between LWC and the selected typical water vegetation indices (WVIs) to determined the sensitivity of different WVIs to LWC. Secondly, the two methods of estimating LWC in winter wheat were compared, one was to directly use PLS and the other was to combine SRM-PLS based on the sensitive WVIs was selected by GRA between WVIs and LWC, and then the method with the highest determination coefficient (R-2) and lowest root mean square error (RMSE) was selected to estimate LWC in winter wheat. The results showed that the relationships between the first five WVI and LWC were stable by using GRA, and then LWC was estimated by using PLS and SRM-PLS at anthesis for winter wheat with 0.63 and 0.46. To validate two model estimation accuracy by using 2009 data, we compared actual value with predicted value by using PLS and SRM-PLS and RMSEs were 2.6 % and 3.12 %, respectively. The results indicated that the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM- PLS.

关键词： Leaf relative water content grey relational analysis stepwise regression method partial least squares winter wheat water vegetation index

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Monitoring Leaf Nitrogen Concentration in Wheat Based on Spectral Parameters From HJ-CCD Data With stepwise regression method

Monitoring Leaf Nitrogen Concentration in Wheat Based on Spe...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Tan, Changwei Jin, Xiuliang Wang, Junchan Tong, Lu Zhu, Xinkai Guo, Wenshan Yangzhou Univ Key Lab Crop Genet & Physiol Jiangsu Prov Key Lab Crop Physiol Ecol & Cultivat Middle & Low Minist Agr Yangzhou 225009 Peoples R China

ISBN: (纸本)9783037853696

Nitrogen is a crucial parameter in maintaining crop health and predicting crop yield. It is possible that leaf nitrogen concentration (LNC) was quickly and non-destructively estimated by remote sensing method. The objective of this experiment was to develop a sensitive spectral index for monitoring LNC in wheat based on HJ-CCD data. In this study, we assessed several common spectral indices based on stepwise regression methods with the determination coefficient (R-2) and root mean square error (RMSE). The results indicated that compared to other spectral index, the spectral index of 1.65SIPI-1.35PSRI was the most positively related to LNC (R-2= 0.6328, p<0.01), and the model for monitoring LNC based on 1.65S1P1-1.35PSRI was with the RMSE of 0.685 g.m(-2) (p<0.01), and was significantly better than the models adopting other spectral indices. In conclusion, this study confirmed the feasibility of utilizing 1.65SIPI-1.35PSRI derived from airborne remotely sensed data to monitor LNC in wheat.

关键词： Leaf Nitrogen Concentration Spectral Parameters HJ-CCD stepwise regression method Wheat

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Application of stepwise regression method in aerodynamic parameter identification of unmanned aerial vehicle

Application of stepwise regression method in aerodynamic par...

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2024 International Conference on Intelligent Robotics and Automatic Control

作者： Li, Hanbing Commercial Aircraft Corp China Ltd Shanghai Aircraft Design Res Inst Shanghai Peoples R China

ISBN: (纸本)9798350389814;9798350389807

The system identification theory is an important tool for modeling vehicle dynamics. Aiming at the situation that the unmanned aerial vehicle (UAV) flight test conditions are limited and complete flight data cannot be obtained, a method for estimating aerodynamic angles and aerodynamic coefficients based on measurable data is proposed. Taking the flying wing UAV as an example, the aerodynamic parameters were determined and calculated by the stepwise regression method. The comparison of test data and simulation results verifies the correctness and practicality of the method. Considering the mathematical nature of discriminant modeling, the method is generally applicable to fixed-wing UAVs with various aerodynamic layouts.

关键词： unmanned aircraft flight test aerodynamic parameter identification stepwise regression method

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A data-driven time-step determination approach for dynamic simulation of heat-electric coupled system

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IET RENEWABLE POWER GENERATION 2022年第13期16卷 2840-2858页

作者： Guan, Aobo Zhou, Suyang Gu, Wei Liu, Zhong Zhan, Xin Liu, Hengmen Southeast Univ Sch Elect Engn 2 Sipailou Nanjing 210096 Peoples R China Jiangsu Elect Power Co Yangzhou Power Supply Branch State Grid China Yangzhou Jiangsu Peoples R China

Dynamic simulation of heat-electric coupled system (HECS) can accurately describe the operating condition of networks and equipment of the system, where the space-time step plays a crucial role in the simulation accuracy and speed. To optimally determine the simulation space-time step, this paper identifies the main influencing parameters for simulation elapsed time by distance correlation (DC) analysis and establishes a simulation time prediction model with stepwise regression (SR) method. Meanwhile, the quadratic curve fitting (QCF) method is adopted to obtain the simulation error prediction model. A simulation space-time step optimal determination model considering the specific simulation requirements is further introduced to improve the simulation efficiency based on the aforementioned models. To validate the proposed methodologies, comprehensive analysis of various study cases on a 20-node testbed is presented. The results demonstrate that the average error in elapsed time and error prediction by the proposed model is merely 0.2426 s and 4.84% compared with the real data.

关键词： optimisation regression analysis heat systems heat-electric coupled system stepwise regression method specific simulation requirements simulation elapsed time simulation space-time step optimal determination model data-driven time-step determination approach curve fitting simulation time prediction model simulation efficiency simulation accuracy simulation error prediction model dynamic simulation distance correlation analysis

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Influence of number and position of measuring points on the nondestructive testing method to predict the flexural modulus of aged wood

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HOLZFORSCHUNG 2022年第5期76卷 421-429页

作者： Yang, Na Wang, Zhongcheng Beijing Jiaotong Univ Sch Civil Engn Shangyuancun 3 Beijing 100044 Peoples R China

The purpose of this work was to ascertain the influence of the nondestructive testing (NDT) method, as well as the location and number of measuring points, on the correlation between NDT indices and the flexural modulus (MOE) of small clear specimens of aged wood. The MOE of the Tibetan Populus cathayana specimens sawed from an ancient floor beam were measured, and multiposition NDT was carried out with a stress wave and resistography detector. Unary and multivariate linear methods and the stepwise regression method were used to discuss the correlation between the NDT indices and MOE. The results indicated that the NDT index of M- f , which combined stress wave velocity and microdrilling resistance, had the highest correlation coefficient (R) with MOE. When the number of measuring points was increased from 2 to 8, R could be increased by up to 180.2%. The stepwise regression method is efficient in determining the optimal combination of variables while avoiding colinearity. Thus, using appropriate NDT indices while considering the texture characteristics of the specimen can effectively improve the MOE prediction accuracy.

关键词： ancient building wood flexural modulus multivariate linear model nondestructive test small clear specimens stepwise regression method

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In-situ characterization of handle characteristics of suiting woven fabrics by a simultaneous measurement method

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TEXTILE RESEARCH JOURNAL 2019年第13期89卷 2522-2531页

作者： Sun, Fengxin Du, Zhaoqun Zheng, Dongming Hu, Xiaorui Sun, Yi Gao, Weidong Jiangnan Univ Coll Text & Clothing Key Lab Ecotext Minist Educ Wuxi Jiangsu Peoples R China Donghua Univ Key Lab Text Sci & Technol Coll Text Minist Educ Shanghai Peoples R China Jiangxi Prov Ctr Qual Inspect & Supervis Prod Gongqingcheng Peoples R China Univ Calif Davis Davis CA 95616 USA

The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) was developed to measure multiple mechanical properties, which are related to fabric handle. In this paper, the potential of using the CHES-FY to evaluate the handle characteristics of woven fabrics was studied. Three prediction models corresponding to the stiffness, smoothness and tightness attributes of fabrics were determined to relate the subjective handle to suitable curve parameters extracted from the pulling-out force-displacement curve of the CHES-FY based on the stepwise regression method. Moreover, the independent validation of the prediction models and sensitivity analysis of the CHES-FY were also conducted. Results show that the CHES-FY together with the developed models provides good prediction for the handle characteristics of wool-rich woven suiting fabrics. It was also shown that the CHES-FY was capable of detecting changes of fabric handle of the woven fabrics with plasma treatments.

关键词： fabric handle pulling-out test stepwise regression method subjective evaluation

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Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods

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MEASUREMENT 2019年 136卷 579-593页

作者： He, Yaoyao Zheng, Yaya Xu, Qifa Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Anhui Peoples R China

Energy consumption can be taken as one of crucial indicators for economic development in any regions. Rapid development of economic, population and urbanization in Anhui province of China has led energy consumption to grow rapidly. The considerable demand for energy has become an issue of great concern. For proper policy formulation, it is necessary to have reliable forecasts for energy consumption. However, energy consumption forecasting is affected by some potential factors, including historical energy consumption, economic activities, population and weather. These uncertain factors put forward higher requirements for energy consumption forecasting methods. Considering these problems, a reasonable feature selection method called stepwise regression is adopted to extract important variables before prediction. Historical energy consumption, average annual GDP growth rate, and total GDP are identified as key factors on annual energy consumption. Then, two probability density forecasting methods based on Box-Cox transformation quantile regression via normal distribution (N-BCQR) and gamma distribution (G-BCQR) are proposed to measure the uncertainty and estimate the future demand of energy in Anhui province of China. The comparatives results show that the N-BCQR outperforms G-BCQR and other existing methods in terms of point forecasts and interval predictions. In addition, on the strength of predefined assumptions regarding the average annual gross domestic product (GDP) growth rate, the energy consumption of Anhui province through 2023 is forecasted. The results indicate that total energy consumption of Anhui is an important component determining local economic growth. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Energy consumption Probability density forecasting Box-Cox transformation quantile regression stepwise regression method

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Electro-thermal breakdown measurement of 500 kV cross-linked polyethylene submarine cable insulation material and its lifetime model analysis

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第10期14卷 862-869页

作者： Hao, Jian Liu, Zhiqian Liao, Ruijin Li, Jian Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China

The first 500 kV AC cross-linked polyethylene (XLPE) cable in the world has been installed in China in 2019. The insulation performance of insulation material under the electro-thermal stress is the basis for its service life. At here, the AC breakdown voltage values and the breakdown time of the 500 kV AC XLPE submarine cable insulation material under the electro-thermal step stress was analyzed. The lifetime prediction accuracy of a new proposed model was compared to the classic FALLOU, SIMONI/RAMU and CRINE lifetime model. Results show that the AC breakdown strength and lifetime of the XLPE samples increase slowly at low temperatures (40-65 degrees C) and decrease rapidly at high temperatures (65-85 degrees C). The lifetime prediction error of the classic FALLOU, SIMONI/RAMU and CRINE models present that their fit goodness cannot meet the accuracy requirement. The new lifetime model using stepwise regression method could optimize the combination of variables, which considers the coupling effect of electrical and thermal stress. The evaluation error of the new proposed model is better than the FALLOU, SIMONI/RAMU and CRINE model, which is suitable for analyzing the lifetime of the AC 500 kV XLPE submarine cable insulation material.

关键词： error analysis submarine cables XLPE insulation power cable insulation thermal stresses electric breakdown regression analysis power transmission lines electro-thermal breakdown measurement lifetime model analysis polyethylene cable electro-thermal stress AC breakdown voltage values electro-thermal step stress lifetime prediction accuracy AC breakdown strength lifetime prediction error analyses electrical stress AC XLPE submarine cable insulation material cross-linked polyethylene submarine cable insulation material Zhoushan China SIMONI-RAMU CRINE lifetime model stepwise regression method coupling effect voltage 500 0 kV temperature 55 0 degC to 65 0 degC temperature 65 0 degC to 85 0 degC

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Determination of amino acids and dipeptides is correlated significantly with optimum temperatures of microbial lipases

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ANNALS OF MICROBIOLOGY 2013年第1期63卷 307-313页

作者： Zhang, Hui-Min Li, Jian-Fang Wu, Min-Chen Shi, Hong-Ling Tang, Cun-Duo Jiangnan Univ Sch Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Med & Pharmaceut Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Biotechnol Minist Educ Key Lab Ind Biotechnol Wuxi 214122 Jiangsu Peoples R China

Amino acids and dipeptides that are correlated significantly with lipase optimum temperatures were searched for in 34 microbial lipase sequences by a stepwise regression method. The positive dipeptides were found to be IR, KS, NY, SA, ST and YR, whereas negative ones were DK, DY, IS, KA, WS, YS and QI. The calculated optimum temperatures from an optimal regression equation of dipeptides fitted the corresponding experimental optimum temperatures of lipases very well, and the maximal absolute difference was only 3.43A degrees C. The spatial positions of the related dipeptides were searched for in two known crystal structures of a thermophilic and mesophilic lipase, respectively. Most of the positive dipeptides were sited in the alpha-helices, while the negative ones were located mainly in the beta-strands or coils and about half of them existed in the N- or C-terminii of the lipases. The results obtained will be very useful in lipase engineering for enhancing lipase thermostability.

关键词： Microbial lipase Dipeptide Optimum temperature stepwise regression method Thermostability

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Operating speed models for curved segments of highways in plateau regions

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Journal of Southeast University(English Edition) 2022年第1期38卷 85-91页

作者： Wang Chenzhu Chen Fei Xu Wensheng Wu Yutao Zhang Yunlong Bo Wu Zhang Ping School of Transportation Southeast UniversityNanjing 211189China National Experimental Teaching Demonstration Center of Road Traffic Engineering Southeast UniversityNanjing 211189China China Harbour Engineering Co. Ltd.Beijing 100027China Zachry Department of Civil Environmental Engineering Texas A&M University 3136 TAMUCollege StationUSA School of Engineering Tibet UniversityLasa 850000China

To examine the influence of the harsh environment in plateau areas on the operating speed of vehicles,advanced speed prediction models for curved segments are established based on observed actual speed ***,the speed characteristics at the starting,mid,and end points of a plane curve were observed on Lalin Highway and China National Highway 318 with Bushnell s handheld radar speedometer ***,the stepwise regression method was proposed to determine the significant parameters and propose the prediction models of the operating speed of cars and large vehicles for the two ***,reserved test group data were utilized to prove the validity and practicality of the proposed *** with traditional methods,the established models can produce more accurate prediction results and deeply examine the nonlinear relationships between parameters and the predicted operating *** study provides a considerate direction and basis for the operating speed prediction model for other segments in plateau regions.

关键词： plateau areas operating speed stepwise regression method prediction model

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