Phase group synchronization between any signals and its physical characteristics

The phase group synchronization between any signals is further revealed,which is based on proposing the new concepts of the greatest common factor frequency,the least common multiple period,quantized phase shift resolution,equivalent phase comparison frequency and so on.Then the problem of phase comparison and processing between different frequency signals is solved and shown in detail.Using the basic principle and the variation law of group phase difference,the frequency stability better than 10-14/s can be easily obtained in the timefrequency measurement and control domain,and experimental results also show the phase relations between atomic energy level transition signal and the locked crystal oscillator signal in an active hydrogen atomic clock are strict phase group synchronization,and locked precision with 10-13/s can be reached based on phase group synchronization.The phase group synchronization can provide technical support to frequency linking among radio frequency,microwave and light frequency.     

Combined state and least squares parameter estimation algorithms for dynamic systems

The control theory and automation technology cast the glory of our era. Highly integrated computer chip and automation products are changing our lives. Mathematical models and parameter estimation are basic for automatic control. This paper discusses the parameter estimation algorithm of establishing the mathematical models for dynamic systems and presents an estimated states based recursive least squares algorithm, and the states of the system are computed through the Kalman filter using the estimated parameters. A numerical example is provided to confirm the effectiveness of the proposed algorithm.     

The partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology

In this paper we develop the partial adjustment valuation approach in which the speeds of (partial) adjustment are assumed to be dynamic and variable, rather than fixed or constant, to assessing the value of information technology (IT). The speeds of adjustment are a function of a set of macroeconomic and/or microeconomic variables, observed and unobserved and, hence, become time-varying or dynamic and variable over time. The approach is illustrated by a practical application. The results imply that the constant speeds of adjustment may overestimate or underestimate the actual speeds of adjustment and, accordingly, may miscalculate the values of performance metrics. Thus, the partial adjustment valuation approach with dynamic and variable speeds of adjustment is more realistic and, more importantly, captures the changing patterns and trends of the adjustment speeds and the performance measures as well. As such, the partial adjustment valuation approach with constant speeds of adjustment fails to adequately explain the dynamic production process of a decision making unit. The empirical evidence also conflicts with the lopsided view that the productivity paradox does not exist in developed countries.     

Conditional least squares and copulae in claims reserving for a single line of business

One of the main goals in non-life insurance is to estimate the claims reserve distribution. A generalized time series model, that allows for modeling the conditional mean and variance of the claim amounts, is proposed for the claims development. On contrary to the classical stochastic reserving techniques, the number of model parameters does not depend on the number of development periods, which leads to a more precise forecasting.Moreover, the time series innovations for the consecutive claims are not considered to be independent anymore. Conditional least squares are used to estimate model parameters and consistency of these estimates is proved. The copula approach is used for modeling the dependence structure, which improves the precision of the reserve distribution estimate as well.Real data examples are provided as an illustration of the potential benefits of the presented approach.     

A New Energy Inversion for Parameter Identification in Saddle Point Problems with an Application to the Elasticity Imaging Inverse Problem of Predicting Tumor Location

The primary objective of this work is a detailed theoretical and computational study of the elasticity imaging inverse problem for tumor identification within the human body. Apart from this inverse problem's important and interesting application, it also poses noteworthy mathematical challenges since the underlying mathematical model is a system of elasticity involving incompressibility. This gives rise to the “locking” effect and special treatment is necessary for both the direct and inverse problems. To study the inverse problem in an optimization framework, we introduce a general computational scheme for handling parameter identification in saddle point problems along with the introduction and analysis of a new energy output least-squares objective functionals. We also present a treatment of the identification of discontinuous elasticity coefficients using the total variation regularization method. General formulas for the computation of the coefficient-to-solution map and a complete convergence analysis are given for the continuous problem as well as for its discrete analogue. Discrete formulas and implementation issues are discussed in detail and numerical examples for smooth and discontinuous coefficients are given.     

Bipartite graphs with the maximum sum of squares of degrees

In this paper we determine all the bipartite graphs with the maximum sum of squares of degrees among the ones with a given number of vertices and edges.     

Asymptotic behavior of solutions for nonlinear elliptic problems with the fractional Laplacian

In this paper we study the asymptotic behavior of least energy solutions and the existence of multiple bubbling solutions of nonlinear elliptic equations involving the fractional Laplacians and the critical exponents. This work can be seen as a nonlocal analog of the results of Han (1991) [24] and Rey (1990) [35].     

最小二乘法在聚合物有效物含量评价方法研究中的应用

为了彻底排除各种无机盐杂质的影响,真实地评价聚合物产品的有效成分含量,研究了醇酸共洗法测试钻井液用聚合物类降滤失剂有效物的可行性。该方法不但可有效地去除样品中的残留碱、水溶性无机盐类,同时还可以有效地去除样品中的酸溶性无机盐(如碳酸盐等)。在进行实验数据处理时,考虑标准碳酸钙的加入量与样品有效物含量测试结果间有较明显的线性关系,运用最小二乘法及加标回测外推法等统计学工具,最大限度地降低了测试结果的不确定度,提高了方法的准确度与可靠性,形成了较完善的钻井液用聚合物类降滤失剂产品有效物含量的评价方法。目前该方法已成功地应用于Q/SHCG 35-201《2钻井液用合成聚合物降滤失剂技术要求》等多个石化行业一级企业技术标准。     

Local classification: Locally weighted–partial least squares-discriminant analysis (LW–PLS-DA)

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW–PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones.