R-mode instability in compact stars

R-mode oscillations have been identified as viable and promising targets for continuous gravitational wave searches, meanwhile, it would allow us to probe the interior of compact stars directly. As well as emitting gravitational wave, r-modes would strongly affect the thermal and spin evolution of compact stars. In this paper, we reviewed the theory behind the gravitational wave driven r-mode instability in a rapidly rotating compact star. In particular, we will focus on r-mode instability window, r-mode evolution and detectability of r-mode.Comment: contribution to the AIP Proceedings of the Xiamen-CUSTIPEN Workshop on the EOS of Dense Neutron-Rich Matter in the Era of Gravitational Wave Astronomy, Jan. 3-7, 2019, Xiamen, China. arXiv admin note: text overlap with arXiv:0806.1005, arXiv:1510.07051, arXiv:1209.5962 by other author

Component Selection in the Additive Regression Model

Similar to variable selection in the linear regression model, selecting significant components in the popular additive regression model is of great interest. However, such components are unknown smooth functions of independent variables, which are unobservable. As such, some approximation is needed. In this paper, we suggest a combination of penalized regression spline approximation and group variable selection, called the lasso-type spline method (LSM), to handle this component selection problem with a diverging number of strongly correlated variables in each group. It is shown that the proposed method can select significant components and estimate nonparametric additive function components simultaneously with an optimal convergence rate simultaneously. To make the LSM stable in computation and able to adapt its estimators to the level of smoothness of the component functions, weighted power spline bases and projected weighted power spline bases are proposed. Their performance is examined by simulation studies across two set-ups with independent predictors and correlated predictors, respectively, and appears superior to the performance of competing methods. The proposed method is extended to a partial linear regression model analysis with real data, and gives reliable results

Design and Implementation of Intelligent Home Automatic Control and Monitoring System

With the development of social economy and science and technology in China, the performance and application range of microprocessor chips are constantly improving. In the current era, the development of home towards intelligence has become the main trend. Therefore, it is an urgent problem to explore the home control system with stable state, strong practicability and lower power consumption cost. This paper analyzes the current situation of smart home and discusses its automatic control system

Research on Automatic Control of Central Fresh Air System

In the complex flow of people in large shopping malls and hospitals, in order to ensure the good quality of indoor air, it is very important to supply fresh air indoors. With the popularization of the application of central fresh air system, in order to strengthen the practicability of fresh air system, it is very important to manage the fresh air system through automatic control. This paper expounds the basic control theory of fresh air system and the principle of automatic control of central fresh air system

Generalized single-index models: The EFM approach

Generalized single-index models are natural extensions of linear models and circumvent the so-called curse of dimensionality. They are becoming increasingly popular in many scientific fields including biostatistics, medicine, economics and finan- cial econometrics. Estimating and testing the model index coefficients beta is one of the most important objectives in the statistical analysis. However, the commonly used assumption on the index coefficients, beta = 1, represents a non-regular problem: the true index is on the boundary of the unit ball. In this paper we introduce the EFM ap- proach, a method of estimating functions, to study the generalized single-index model. The procedure is to first relax the equality constraint to one with (d - 1) components of beta lying in an open unit ball, and then to construct the associated (d - 1) estimating functions by projecting the score function to the linear space spanned by the residuals with the unknown link being estimated by kernel estimating functions. The root-n consistency and asymptotic normality for the estimator obtained from solving the re- sulting estimating equations is achieved, and a Wilk's type theorem for testing the index is demonstrated. A noticeable result we obtain is that our estimator for beta has smaller or equal limiting variance than the estimator of Carroll et al. (1997). A fixed point iterative scheme for computing this estimator is proposed. This algorithm only involves one-dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Numerical studies based on simulation and on applications suggest that this new estimating system is quite powerful and easy to implement.Generalized single-index model, index coefficients, estimating equations, asymptotic properties, iteration