HBase大对象数据调度自整定控制算法

提出一种基于趋化重采样的HBase大对象数据调度自整定控制算法,在数据接收端进行数据包接受、重组并解码,提高拆包、组包的效率,构建基于UDP协议的HBase大对象数据调度总体结构模型。定义趋化值与量化值之间的差值进行熵编码,在接收端先把数据包进行重组,得到HBase大对象数据的实时传输系统,实现数据调度自整定控制。仿真实验表明,采用了该自整定控制算法,HBase大对象数据传输的准确性和稳定性都有明显的提高,自整定控制方法能有效降低丢包数,避免延时与丢帧,能有效地对冗余信息和失稳波动信息进行整定处理。     

Studying School Architecture: some reflections

Abstract

This article reports on the findings of an investigation in which 30 secondary schools rated the priority being given to and the likely impact on teaching and learning of a selection of 23 issues currently on the agenda for school improvement. Results are considered for the whole sample, and according to the role of respondents and school context. Schools are giving high priority to the implementation of national policies and school‐generated initiatives directed at improved classroom practice. However, the perceived impact of externally generated change is less than that of change for which school staff feel a sense of ownership. Policies directed at a consideration of resource issues, professional development and salary changes are accorded low priority and are not considered to have a marked impact on teaching and learning. There is a potential for frustration and loss of morale when policies that are thought to be of limited value within schools require time and sometimes extensive consideration. The article's conclusions for policy‐makers are directed at minimising this risk.     

一种改进的新型非均匀方向滤波器组

为了提高图象信号处理水平,可以采用一种新的多分辨率、多方向的图象变换方法。该变换采用非分离的钻石形和扇形滤波器进行方向滤波,利用统一的采样矩阵进行样本减（增）采样和方向重排,通过级联双通道树型结构得到多方向的频谱分割。该方法使每层分解后的图象具有一个低频子带和十二个高频子带,这些子带都为规则的方形矩阵,可以通过对低频子带的迭代分解实现多分辨率分析,具有临界采样和理想重构性质。     

Application of a new resampling method to SEM: a comparison of S-SMART with the bootstrap

Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method (called S-SMART) and compare the statistical performance of it with that of the bootstrap through an application of them to the most advanced modelling technique, SEM, as an example. The evaluation of the statistical performances of S-SMART and the bootstrap with respect to the standard errors of the parameter estimates was conducted through a Monte Carlo simulation study. This work, while potentially benefiting educational and behavioural research, conceivably would also provide methodological support for other research areas, such as bioinformatics, biology, geosciences, astronomy, and ecology, where large samples are hard to obtain.     

一种多尺度重采样粒子滤波算法

粒子滤波过程中通过引入重采样消除粒子匮乏现象,但是重采样过程却削弱了粒子的多样性,导致粒子贫化.为协调粒子多样性和样本贫化之间的冲突,提出一种多尺度重采样粒子滤波算法,粒子空间重采样划分多个尺度,然后重新定义各尺度粒子权重并重采样,用尺度熵值度量重采样粒子的多样性,指导重采样.仿真实验结果表明,多尺度重采样粒子滤波算法有效提高了精度,适用于高精度系统滤波计算,并将应用于精细果业中数据同化。     

A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select novel pseudo-relevant documents based on Lavrenko’s relevance model approach. The main idea is to use overlapping clusters to find dominant documents for the initial retrieval set, and to repeatedly use these documents to emphasize the core topics of a query.     

一种基于改进粒子滤波的水下采矿导航方法

针对水下采矿导航系统所面临的噪声具有非高斯性和频率随机性的问题,提出基于粒子滤波的深海采矿导航算法,并针对粒子滤波的粒子退化和贫化提出一种新的重采样算法。结合湖试数据,仿真实验表明新的重采样算法在获得更好的滤波精度的同时可以避免粒子贫化现象。最后,将基于改进的粒子滤波的深海采矿导航算法与基于无迹卡尔曼滤波算法的导航算法进行对比。结果表明本文提出的算法具有较高的精度和优良的鲁棒性。     

Working with Atypical Samples

Thanks to COVID-19, schools were closed and tests were canceled. The result is that we may not see test-taking data typically seen before. For some analyses, sample sizes may not meet the minimum requirement. For others, the sample of test-takers may be different from previous years. In some situation, there may be no data at all. What do we do in these and other similar situations? Several ideas are presented in this article. Directions are suggested for not only dealing with challenges like this but also preparing for them.     

几何操作中重采样问题的数据并行实现

通过SIMD PE阵列之间的数据并行传送,数据并行实现重采样,使实现重采样的复杂度由存储器数据流访问实现方法的Ο（MN）降低到Ο（M＋N）,从而大大提高了处理速度,能更好地满足图像快速实时处理的需要.     

Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map

A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e. g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research.