A new method to detect the initial rotor position of switched reluctance machine (SRM) is presented in this article. Unlike most conventional position estimation methods, the proposed method does not ...need any extra premeasurement and only the data with finite element method (FEM) are required. First, a linear regression model (LRM) is presented to describe the relationship between FEM and measured inductance characteristics. Then, to detect the position, the residual sum of squares of the proposed LRM is considered as an objective function, which is a convex function with rotor position. The rotor position can be estimated by minimizing the objective function with the golden-section search method. Finally, the accuracy of the proposed estimation algorithm is validated by the experimental results on a three-phase 12/8 pole SRM prototype. Compared with the existing position estimation methods, the proposed method has higher accuracy and less measurement effort. The proposed method can serve as a supplement to provide accurate initial position information for incremental position sensors.
In order to achieve effective control of thermal error compensation of computer numerical control (CNC) machine tools, the prediction accuracy and robustness of the compensation model is particularly ...important. In this paper, the temperature of sensitive points and thermal error of the spindle in Z direction are measured. Using a combination of fuzzy clustering analysis and gray correlation method to select temperature-sensitive points and then using multiple linear regression of least squares and least absolute estimation methods, distributed lag model, and support vector regression machine to establish prediction models of the relationship between temperature of sensitive points and the thermal error. Also, the temperature values of sensitive points and the thermal error in the experimental conditions of different ambient temperatures and different spindle speeds are measured. By comparing the prediction accuracy of various prediction models under different experimental conditions verify the robustness of the models. Experimental results show that when the modeling data are less, the prediction accuracy of multiple linear regression of least squares and least absolute estimation methods and distributed lag model is declined, and their robustness are poor, while support vector regression model has good prediction accuracy and its robustness remains strong when changing the experimental conditions. However, when modeling data are rich, the prediction accuracy of various algorithms is improved, but the robustness of support vector regression model is volatile. The robustness analysis of different models provides a useful reference for the thermal error compensation model, selection of CNC machine tools, and has good engineering applications.
A common way to deal with count data is to fit a generalized linear model. The most common approaches are the Poisson regression model and the negative binomial regression model. However, ...Conway-Maxwell Poisson (COM-Poisson) regression model is more flexible to fit count data. This model has been widely used to describe under- or over-dispersion problem for count data in cross-sectional setting. However, there is no application of the COM-Poisson model in longitudinal data. We propose and develop the COM-Poisson regression model to fit longitudinal count data. We compare this model with the Poisson regression model and the negative binomial model, under two different working correlation structures; exchangeable and autoregressive of order 1, AR(1). The results show that the COM-Poisson model is very suitable to longitudinal count data, even in presence of dispersion; it gives the smallest AIC values. Also, it is insensitive to the choice of the working structure. Extensive simulation is conducted for small, moderate and large sample sizes, to evaluate the proposed model. The proposed approach has good results compared with other models using different criteria.
Urbanization and industrialization have significant impacts on energy consumption and CO2 emissions, but their relationship varies at different stages of economic development. Taking cognizance of ...heterogeneity and the “ratchet effect,” this paper adopts the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimates the relationship using different panel date models. The main results are obtained by dynamic panel threshold regression models, which divide a balanced panel dataset of 73 countries over the period of 1971–2010 into four groups according to their annual income levels. The key results are: (1) in the low-income group, urbanization decreases energy consumption but increases CO2 emissions; (2) in the middle-/low-income and high-income groups, industrialization decreases energy consumption but increases CO2 emissions, while urbanization significantly increases both energy consumption and CO2 emissions; (3) for the middle-/high-income group, urbanization does not significantly affect energy consumption, but does hinder the growth of emissions; while industrialization was found to have an insignificant impact on energy consumption and CO2 emissions; (4) from the population perspective, it produces positive effects on energy consumption, and also increases emissions except for the high-income group. These novel methodology and findings reveal that different development strategies of urbanization and industrialization should be pursued depending on the levels of income in a bid to conserve energy and reduce emissions.
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•Simultaneous detection of melamine and urea in cow milk sample.•Optimization of chitosan membrane and electrochemical parameters for high selectivity towards melamine and urea.•Assay ...was constructed by establishing 24 linear regression models.•Error analysis was performed to verify practicability of these 24 linear regression models.•Developed sensor could detect picomolar concentrations of melamine and urea in cow milk sample.
The consumption of melamine and urea contaminated cow milk causes indigestion, acidity, ulcers and kidney stones in humans. In this context, a highly sensitive acetylcholinesterase cyclic voltammetric biosensor based on zinc oxide nanospheres modified Pt electrode has been successfully developed for the simultaneous determination of melamine and urea in cow milk sample. The fabricated bioelectrode showed 100% permeability to the binary mixture of melamine and urea, which in-turn enhanced selectivity. In addition, linear regression models for the estimation of binary mixture of melamine and urea in cow milk were formulated by keeping added melamine and urea as dependent variables and the estimated electrochemical paremeters as independent variables. The prediction performance of linear regression models was validated using %recovery, relative prediction error and root mean square error for cross-validation. The developed Pt/ZnO/AChE/Chitosan bioelectrode detected melamine and urea over a range of 1–20nM with a limit of detection of 3 pM and 1 pM respectively. The proposed sensor exhibited good recovery (99.96–102.22%), thus providing a promising tool for analysis of melamine and urea in cow milk samples.
With the increase in the number of cyclists, a method to evaluate the satisfaction of cyclists has become necessary. Previous studies in Europe and America have reported several approaches to ...evaluate cyclists’ perception of satisfaction. This research explored an emerging technology using 360° videos to develop a method for investigating cyclists’ level of satisfaction on both sidewalks and paved shoulders in Japan. The 360° videos provide a high level of immersion compared with traditional videos. All 360° videos were filmed at sixteen different locations in Kumamoto city, Kyushu island. Participants were asked to take a video survey by viewing the 360° videos with a head-mounted display and then rating their level of satisfaction. Finally, based on the results of the video survey, both multinomial ordered logit regression model and random parameters multinomial ordered logit regression model were used to explain the relationships between cyclists’ satisfaction, traffic conditions, and road characteristics. The results show that the road characteristics variables for both sidewalks and paved shoulders have statistically significant effects on participants’ satisfaction (p < 0.05). On the other hand, traffic conditions variables did not have an impact on participants for sidewalks section. In particular, there is data available on all the variables in the model that allows planners and engineers use this method to evaluate the satisfaction of cyclists on various road segments.
The Poisson regression model (PRM) aims to model a counting variable y, which is usually estimated by using maximum likelihood estimation (MLE) method. The performance of MLE is not satisfactory in ...the presence of multicollinearity. Therefore, we propose a Poisson James-Stein estimator (PJSE) as a solution to the problems of inflated variance and standard error of MLE with multicollinear explanatory variables. For assessing the superiority of proposed estimator, we present a theoretical comparison based on the matrix mean squared error (MMSE) and scalar mean squared error (MSE) criterions. A Monte Carlo simulation study is performed under different conditions in order to investigate the performance of the proposed estimator where MSE is considered as an evaluation criterion. In addition, an aircraft damage data is also considered to assess the superiority of proposed estimator. Based on the results of simulation and real data application, it is shown that the PJSE outperforms the classical MLE and other biased estimation methods in a sense of minimum MSE criterion.
Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned ...aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.
We are interested in the estimation of average treatment effects based on right‐censored data of an observational study. We focus on causal inference of differences between t‐year absolute event ...risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.
The dramatic economic growth and urbanization witnessed in China have been accompanied by a range of social and environmental problems. To comprehensively understand the influence of social ...inequality on climate change, the study analyzed the mechanism and pathways of the effects of education level on energy consumption, selecting typical indicators to reflect (i) the education quality within the population and (ii) the level of education development in order to evaluate the impact of education inequality and disparity. Under the framework of a STIRPAT model hypothesis, we investigated how the education level in Guangdong Province influenced energy consumption, using panel data from 2002 to 2017 and making a distinction between the Pearl River Delta region and the “non-Pearl River Delta” region. The empirical results show a significant disparity between the education level and energy consumption of the two regions. The education level has exerted significant effects in relation to energy consumption in the whole of Guangdong province. In cities with lower education levels, this impact was more obvious. However, in places with higher levels of education, this impact was overshadowed by other more significant factors, such as income level. The quality of the education within the population was found to increase energy consumption in the non-Pearl River Delta region, while this did not significantly impact on energy consumption within the Pearl River Delta. Our results hold implications for policy makers that they should adopt education methods and interventions to promote low-carbon knowledge and awareness that reflect the different stages of education development of regions. In this way, residents can be encouraged to develop low-carbon lifestyles, thereby reducing energy consumption and mitigating CO2 emissions.
•The impact of education level inequality on energy consumption is investigated.•Education level has significant effects on energy consumption in Guangdong province.•In cities with lower education levels, the impact of education level on energy consumption is more obvious.•Quality of education within the population increases energy consumption in the non-Pearl River Delta region.•Quality of education within the population has no significantly impact on energy consumption within the Pearl River Delta.
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