Isobar separation at FRS-ESR – a development towards pure isomeric stored beams

One important goal of the ILIMA project at FAIR is the study of masses and decay properties of relativistic isomeric beams stored and cooled in the planned storage-ring complex. A new scheme is described, where a storage-cooler ring is used for high-resolution mass separation. Experimental results on the separation of the isobaric pair ¹⁴⁰Pr-¹⁴⁰Ce are presented. P. Beller, deceased.     

Selection of the number of regression variables; A minimax choice of generalized FPE

Summary A generalized Final Prediction Error (FPE_α)_ criterion is considered. Based onn observations, the numberk of regression variables is selected from a given range 0≦k≦K, so as to minimize . It is shown that if α tends to infinity withn, the selection is consistent but the maximum of the mean squared error of estimates of parameters diverges to infinity with the same order of divergence as that of α. A meaningful minimax choice of α exists for a regret type mean squared error, while for simple mean squared error it is trivially 0. The minimax regret choice of α converges to a constant, approximately 3.5 forK≧8 ifn−K increases simultaneously withn, otherwise it diverges to infinity withn.     

基于TMS320C5409的语音处理系统

介绍了一个基于DSP(TMS320C5409)的语音采集、编码、回放系统实例,对于DSP与CODEC芯片如何连接和配合工作提出了较为详细的解决方案,并从硬件、软件两个方面做出了说明.     

Solubility of Anthraquinone Derivatives in Supercritical Carbon Dioxide: New Correlations

Solubility of several anthraquinone derivatives in supercritical carbon dioxide was readily available in the literature, but correcting ability of the existing models was poor. Therefore, in this work, two new models have been developed for better correlation based on solid–liquid phase equilibria. The new model has five adjustable parameters correlating the solubility isotherms as a function of temperature. The accuracy of the proposed models was evaluated by correlating 25 binary systems. The proposed models observed provide the best overall correlations. The overall deviation between the experimental and the correlated results was less than 11.46% in averaged absolute relative deviation (AARD). Moreover, exiting solubility models were also evaluated for all the compounds for the comparison purpose.     

基于AIC方法的切换神经网络模型设计

将切换系统设计中的切换思想与神经网络相结合,构建了切换神经网络模型.根据模糊C均值（FCM）聚类方法将样本数据分为多组训练数据,每组数据对应训练一个单一神经网络模型,再利用赤池信息准则（AIC）制定相应的切换规则.根据输入数据特性,选择单一网络或多网络组合的输出作为模型输出,从而达到函数逼近目的.本模型更好地利用了各个子网络在特定区域具有较高逼近精度的特点.仿真结果表明,切换神经网络模型有较高的逼近精度.     

指数自回归模型的辨识的研究

本文讨论了指数自回归模型的辨识问题，证明了该模型的最小二乘问题的非凸性，并给出其保证凸性的条件，最后运用混合算法，辨识了该模型，并用数值算例加以说明。     

Multi-sample cluster analysis using Akaike's Information Criterion

Summary Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's Information Criterion (AIC). This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value. The multi-sample cluster problem is defined, and AIC is developed for this problem. The form of AIC is derived in both the multivariate analysis of variance (MANOVA) model and in the multivariate model with varying mean vectors and variance-covariance matrices. Numerical examples are presented for AIC and another criterion calledw-square. The results demonstrate the utility of AIC in identifying the best clustering alternatives. This research was supported by Office of Naval Research Contract N00014-80-C-0408, Task NR042-443 and Army Research Office Contract DAAG 29-82-K-0155, at the University of Illinois at Chicago.     

Regression Analysis for Segment Size and Mean Size of Cocoon Filament

In order to extract deterministic component from trend nonstationary time series, regression analysis by Akaike Information Criterion （AIC） for segment size and mean size of cocoon filament is introduced, and deterministic component is extracted from size series of cocoon filament by analysis result. Experiments of simulating deterministic components on 9 cocoon categories are carried out, and experimental result is analyzed. Through analysis and experiment, it is known that selecting the order and coefficients of regression equation by AIC is beneficial to accurately describe the relation between segment value and mean value. This study is also useful for pretreatment of nonstationary time series.     

Order Determination for Multivariate Autoregressive Processes Using Resampling Methods

LetX₁, …, X_nbe observations from a multivariate AR(p) model with unknown orderp. A resampling procedure is proposed for estimating the orderp. The classical criteria, such as AIC and BIC, estimate the orderpas the minimizer of the function[formula]wherenis the sample size,kis the order of the fitted model, Σ²_kis an estimate of the white noise covariance matrix, andC_nis a sequence of specified constants (for AIC,C_n=2m²/n, for Hannan and Quinn's modification of BIC,C_n=2m²(ln ln n)/n, wheremis the dimension of the data vector). A resampling scheme is proposed to estimate an improved penalty factorC_n. Conditional on the data, this procedure produces a consistent estimate ofp. Simulation results support the effectiveness of this procedure when compared with some of the traditional order selection criteria. Comments are also made on the use of Yule–Walker as opposed to conditional least squares estimations for order selection.     

Bayesian analysis of lymphatic spreading patterns in cancer of the thoracic esophagus

For the treatment of patients with cancer of the thoracic esophagus, lymphatic spreading is one important factor to infer how advanced their cancer is. We introduced a one-dimensional scale based on lymphatic spreading patterns, the stage of cancer, to express how advanced their cancer is, and we proposed a method to infer each patient's stage from his lymphatic spreading pattern by applying a Bayesian model. Our Bayesian model was built based on the assumption that lymphatic spreading in cancer could be explained as what was brought about by the advance of stage. In the modeling, we introduced the probability of what stage each patient was in as a prior distribution. We also introduced distribution functions of Weibull distributions to express the relation between the advance of stage and the increase of the probability of metastasis. Our model was applied to the data of nodal involvement obtained from 103 patients with cancer of the thoracic esophagus and the parameters were estimated with the maximum likelihood method. AIC was used to check that the data had enough information to be divided into the stages of a clinically reasonable number. With the estimated parameters, we inferred the probability of metastasis to each lymph node in each stage and calculated by Bayes' theorem with 31 new patients the probability of what stage they were in. The results well represented some characteristics of the lymphatic spreading and suggested the appropriateness of our approach.The present study was carried out under the ISM Cooperative Research Program (91-ISM·CRP-18).