教育理论的分类框架与发展反思

教育理论的繁荣没有带来与之对称的教育实践改进是不争的事实,这使教育理论蕴藏信誉危机.这些事实促使我们必须对教育理论做出深刻反思.但反思必须是辩证的、精细的、建设性的.对教育理论进行合理分类,并在此基础上对各类教育理论进行反思,对教育理论发展是至关重要的.依傍科学标准与价值标准建立起来的教育理论,可以依据其成立的标准划分成四类教育理论:实然的教育理论、价值的教育理论、逻辑的教育理论和科学的教育理论.在相对精细的划分框架下对各类理论的正确评价可以促进各类教育理论的理性发展.     

快速傅立叶变换在数字信号处理器上的实现研究

结合数字信号处理器的性能特点,对基2、基4、分裂基和Bruun FFT等快速傅立叶变换算法及其在TMS320C30上的实现进行了研究,开发出高效的FFT算法和程序。     

C2C网上交易中信号机制的有效性分析

本文应用Berry等(1995)^[2]提出的离散选择需求模型和差异产品的伯川德竞争模型,并利用淘宝网上的交易数据,对声誉、消费者保障计划、保修服务及信息披露四种信号策略在网上交易中的作用进行了实证研究。需求估计结果表明消费者保障计划中的"7天无理由退换货"计划以及保修服务可以作为产品质量的信号;而在其他有效信号策略存在的情况下,消费者保障计划中的"先行赔付"计划以及卖家声誉作为质量信号的作用则被削弱。成本分析表明,信息披露虽然可以提高消费者购买的可能性,但是由于发出信号的成本太低,容易被低质量卖家所模仿,因此并不能作为有效的质量信号。本文首次从供给和需求两个方面分析了信号机制在信息不对称情况下的效应及其作用机制。     

中国朝鲜族文献整理研究

介绍了朝鲜族文献的类型,朝鲜族文献整理的意义,并从学科、出版时间、文献来源等角度对朝鲜族文献进行分析,最后对朝鲜族文献整理提出几点思考。     

Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

The objective of this paper is to study the Phase I monitoring and change point estimation of autocorrelated Poisson profiles where the response values within each profile are autocorrelated. Two charts, the SLRT and the Hotelling's T², are proposed along with an algorithm for parameter estimation. The detecting power of the proposed charts is compared using simulations in terms of the signal probability criterion. The performance of the SLRT method in estimating the change point in the regression parameters is also evaluated. Moreover, a real data example is presented to illustrate the application of the methods.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

Assessing direct and indirect seasonal decomposition in state space

The problem of whether seasonal decomposition should be used prior to or after aggregation of time series is quite old. We tackle the problem by using a state-space representation and the variance/covariance structure of a simplified one-component model. The variances of the estimated components in a two-series system are compared for direct and indirect approaches and also to a multivariate method. The covariance structure between the two time series is important for the relative efficiency. Indirect estimation is always best when the series are independent, but when the series or the measurement errors are negatively correlated, direct estimation may be much better in the above sense. Some covariance structures indicate that direct estimation should be used while others indicate that an indirect approach is more efficient. Signal-to-noise ratios and relative variances are used for inference.     

On the improvement of one-sided S control charts

The most common charting procedure used for monitoring the variance of the distribution of a quality characteristic is the S control chart. As a Shewhart-type control chart, it is relatively insensitive in the quick detection of small and moderate shifts in process variance. The performance of the S chart can be improved by supplementing it with runs rules or by varying the sample size and the sampling interval. In this work, we introduce and study one-sided adaptive S control charts, supplemented or not with one powerful runs rule, for detecting increases or decreases in process variation. The properties of the proposed control schemes are obtained by using a Markov chain approach. Furthermore, a practical guidance for the choice of the most suitable control scheme is also provided.     

BOOST－PFC电路反馈环节的优化设计

介绍了平均电流控制ＢＯＯＳＴ─ＰＦＣ电路的原理，建立了电流环和电压环的小信号简化模型，讨论了双环反馈环节的设计原则和方法，提出最优设计的思想和数学模型，并以一种ＢＯＯＳＴ─ＰＦＣ电路为例进行优化设计，计算模拟结果显示优化后有关指标好于优化前。     

Improved Approximation for Breakpoint Graph Decomposition and Sorting by Reversals

Sorting by Reversals (SBR) is one of the most widely studied models of genome rearrangements in computational molecular biology. At present, is the best known approximation ratio achievable in polynomial time for SBR. A very closely related problem, called Breakpoint Graph Decomposition (BGD), calls for a largest collection of edge disjoint cycles in a suitably-defined graph. It has been shown that for almost all instances SBR is equivalent to BGD, in the sense that any solution of the latter corresponds to a solution of the former having the same value. In this paper, we show how to improve the approximation ratio achievable in polynomial time for BGD, from the previously known to for any > 0. Combined with the results in (Caprara, Journal of Combinatorial Optimization, vol. 3, pp. 149–182, 1999b), this yields the same approximation guarantee for n! – O((n – 5)!) out of the n! instances of SBR on permutations with n elements. Our result uses the best known approximation algorithms for Stable Set on graphs with maximum degree 4 as well as for Set Packing where the maximum size of a set is 6. Any improvement in the ratio achieved by these approximation algorithms will yield an automatic improvement of our result.