Analysis of longitudinal multispin orders in strongly coupled spin systems

Longitudinal multispin orders provide an effective way for measurement of scalar couplings and also to probe molecular interactions and dynamics. Analysis of longitudinal orders has been made in strongly coupled AB and ABX spin systems to determine the dependence of strong coupling parameter on these orders. Experimental and simulated spectra of various longitudinal orders are illustrated for these spin systems. This general procedure can be extended to broad range of spin systems to understand the influence of strong coupling on longitudinal orders.     

An improved transmission line laser model for multimode laser diodes incorporating thermal effects

An improved transmission line model to study the thermal effects in semiconductor laser diodes is reported in this paper. The temperature effects in the laser characteristics are obtained by incorporating temperature dependent gain and carrier density equations for the laser cavity. These primary factors are introduced in the regular transmission line laser model to estimate the static and dynamic characteristics of an 1.3μm InGaAsP double heterostructure laser diode. The results show good agreement with the experimental observation and solution of rate equations referred in the literature. The key feature of this model is that it provides the laser spectra at various temperatures. Based on the model, time dependent evolution of optical spectrum, temperature dependent optical output and frequency chirp are evaluated. Further the distribution of photon and electron density within the cavity is also determined.     

A note on Herglotz’s theorem for time series on function spaces

In this article, we prove Herglotz’s theorem for Hilbert-valued time series. This requires the notion of an operator-valued measure, which we shall make precise for our setting. Herglotz’s theorem for functional time series allows to generalize existing results that are central to frequency domain analysis on the function space. In particular, we use this result to prove the existence of a functional Cramér representation of a large class of processes, including those with jumps in the spectral distribution and long-memory processes. We furthermore obtain an optimal finite dimensional reduction of the time series under weaker assumptions than available in the literature. The results of this paper therefore enable Fourier analysis for processes of which the spectral density operator does not necessarily exist.     

Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.     

近红外光谱小波分析在土壤参数预测中的应用

从田间采集了150个田间土壤样本,在分析了所有样本的土壤参数统计特征之后,对原始近红外光谱数据进行了聚类分析,分别得到了50个土壤全氮和50个土壤有机质的等价样本及其对应光谱。对样本光谱曲线进行8层Biorthogonal小波包分解,分解得到的最低低频[80]结点对应着土壤水分以及土壤质地的光谱变化趋势,最高高频[8 255]结点对应着土壤粒度、光谱仪精度等引起的高频震荡。对以上两个结点进行重构并从样本光谱曲线中剔除以上影响成分,得到了对应的土壤参数特征光谱。基于特征光谱建立了土壤参数偏最小二乘回归模型：全氮偏最小二乘预测模型的预测系数r_c达到了0.960,验证系数r_v达到了0.920;有机质偏最小二乘预测模型的预测系数r_c达到0.922,验证系数r_v达到0.883。模型精度明显提高,满足实际生产的需要。     

光谱反射率重建中代表颜色分步选取方法

光谱反射率重建一般指采用标准色卡上的颜色样本,对成像系统进行光谱表征(Spectral character-ization),进而从系统响应中精确地求出物体表面的光谱反射率.由于标准色卡的颜色样本数量较大,在实际成像系统中使用时有诸多不便.考虑到颜色样本之间存在较大冗余,可从中选出具代表性的少数样本用于光谱表征.针对已有方法未考虑实际成像系统特性的不足,文章提出了一种代表颜色的分步选取算法,即首先通过假设一个虚拟成像系统,根据全局误差最小的原则,挑选出部分最具代表性的颜色,估计出实际成像系统的光谱响应函数,然后在此基础上继续选择其余的代表颜色.实验表明,对于窄带多光谱成像系统及宽带彩色扫描仪而言,文章提出的方法在光谱精度及色度方面均明显优于先前方法.     

Group methodology in production management: A solution based on a hierarchical classification method

We consider the problem submitted by Garcia and Proth and we propose for it a solution obtained by simply applying our very general method of hierarchical classification based on the likelihood of the links, in the case of an incidence table of data. This application is done on each of the two sides of the incidence data table. In our approach, the common number p of classes has not to be fixed arbitrarily, but is an observed possible result—provided by the notions of ‘significant’ levels and nodes of the classification tree—for the ‘natural’ association between two partitions. We compare our solution with the previous one.     

Spectral Changes Due to Performance Environment in Singers, Nonsingers, and Actors

From postrecording interviews of professional singers, it was hypothesized that recording environments, i.e., sound-treated environment versus an auditorium, may induce different vocal behaviors. To test this hypothesis, three groups consisting of nonsingers, singers, and actors were recorded in two different recording environments: a sound-treated booth (IAC) and an auditorium (AUD). Three recordings were obtained from each participant: recording one (IAC) and two (AUD1) required the participants to read in a normal voice; recording three (AUD2) required participants to pretend that they were "performing" before a full house. Results indicated that only the singers and the actors exhibited significant spectral and/or frequency/duration differences from one recording environment to another, with the most dramatic differences exhibited by the singers. It was concluded that the environment in which we record experimental samples from professional voice users, especially singers, should be considered as a variable that can affect results.     

Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.     

Spectral estimation of the Lévy density in partially observed affine models

The problem of estimating the Lévy density of a partially observed multidimensional affine process from low-frequency and mixed-frequency data is considered. The estimation methodology is based on the log-affine representation of the conditional characteristic function of an affine process and local linear smoothing in time. We derive almost sure uniform rates of convergence for the estimated Lévy density both in mixed-frequency and low-frequency setups and prove that these rates are optimal in the minimax sense. Finally, the performance of the estimation algorithms is illustrated in the case of the Bates stochastic volatility model.