Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

The Submanifolds with Parallel Mean Curvature Vector in a Locally Symmetric and Conformally Flat Riemannian Manifold

In the present paper we obtain the following result: Theorem Let M^R be a compact submanifold with parallel mean curvature vector in a locally symmetric and conformally flat Riemannian manifold N^(n+p)(p>1). If then M^n lies in a totally geodesic submanifold N^(n+1).

High-dimensional Tests for Mean Vector: Approaches without Estimating the Mean Vector Directly

Several tests for multivariate mean vector have been proposed in the recent literature.Generally,these tests are directly concerned with the mean vector of a high-dimensional distribution.The paper presents two new test procedures for testing mean vector in large dimension and small samples.We do not focus on the mean vector directly,which is a different framework from the existing choices.The first test procedure is based on the asymptotic distribution of the test statistic,where the dimension increases with the sample size.The second test procedure is based on the permutation distribution of the test statistic,where the sample size is fixed and the dimension grows to infinity.Simulations are carried out to examine the finite-sample performance of the tests and to compare them with some popular nonparametric tests available in the literature.

Superiority of empirical Bayes estimator of the mean vector in multivariate normal distribution

In this paper, the Bayes estimator and the parametric empirical Bayes estimator(PEBE) of mean vector in multivariate normal distribution are obtained. The superiority of the PEBE over the minimum variance unbiased estimator(MVUE) and a revised James-Stein estimators(RJSE) are investigated respectively under mean square error(MSE) criterion. Extensive simulations are conducted to show that performance of the PEBE is optimal among these three estimators under the MSE criterion.

Sign-based Test for Mean Vector in High-dimensional and Sparse Settings

In this article, we introduce a robust sparse test statistic which is based on the maximum type statistic. Both the limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives. Numerical studies are carried out to examine the numerical performance of the test and to compare it with other tests available in the literature. The numerical results show that the test proposed significantly outperforms those tests in a range of settings, especially for sparse alternatives.

ON DETECTION OF CHANGE POINTS USING MEAN VECTORS

In this paper,the authors consider the problem of change points within the framework of model selection and propose a procedure for estimating the locations of change points when the number of change points is known.The strong consistency of this procedure is also established. The problem of detecting change points is discussed within the framework of the simultaneous test procedure.The case where the number of change points is unknown will be discussed in another paper.

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.

面向众核处理器的阴阳K-means算法优化

传统阴阳K-means算法处理大规模聚类问题时计算开销十分昂贵。针对典型众核处理器的体系结构特征,提出了一种阴阳K-means算法高效并行加速实现。该实现基于一种新内存数据布局,采用众核处理器中的向量单元来加速阴阳K-means中的距离计算,并面向非一致内存访问(non-unified memory access, NUMA)特性进行了针对性的访存优化。与阴阳K-means算法的开源多线程实现相比,该实现在ARMv8和x86众核平台上分别获得了最高约5.6与8.7的加速比。因此上述优化方法在众核处理器上成功实现了对阴阳K-means算法的加速。

Use of Support Vector Regression Based on Mean Impact Value Model to Identify Active Compounds in a Combination of Curcuma longa L.and Glycyrrhiza extracts

A support vector regression based on the mean impact value (MIV) model was constructed to identify the bioactive compounds inhibiting proliferation of HeLa cells in a combination of turmeric (Curcuma longa L.) and liquorice (Glycyrrhiza) extracts. The quantitative chemical fingerprint from 50 batches of turmeric and liquorice extracts was established using high performance liquid chromatography hyphenated to an ultraviolet visible detector. Qualitative results were obtained using ultra performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry. A total of 46 peaks (peaks 1–15 from turmeric and 16–46 from liquorice) were selected as “common peaks” for analysis. The inhibitory effect of the combined extracts on HeLa cells was measured by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. It was found that 15 compounds (peaks: 8, 12, 30, 24, 46, 11, 14, 9, 3, 1, 44, 18, 7, 45 and 43) possessing high absolute MIV exhibited a significant correlation with the cytotoxicity against HeLa cells; most of these have already been confirmed with potential cytotoxicity in previous research. The important potential application of the present model can be extended to help discover active compounds from complex herbal medicine prior to traditional bioassay-guided separation. It is considered that this could be a useful tool for re-developing herbal medicine based on the use of these active compounds. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

FAST IMAGE ENCODING ALGORITHM BASED ON MEAN-MATCH CORRELATION VECTOR QUANTIZATION

A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3～0.4 dB compared to the full-search VQ.

基于主题词向量中心点的K-means文本聚类算法

K-means由于其时间复杂度低运行速度快一直是最为流行的聚类算法之一,但是该算法在进行聚类时需要预先给出聚类个数和初始类中心点,其选取得合适与否会直接影响最终聚类效果。该文对初始类中心和迭代类中心的选取进行大量研究,根据决策图进行初始类中心的选择,利用每个类簇的主题词向量替代均值作为迭代类中心。实验表明,该文的初始点选取方法能够准确地选取初始点,且利用主题词向量作为迭代类中心能够很好地避免噪声点和噪声特征的影响,很大程度上地提高了K-means算法的性能。

Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines

Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.

基于BERT-LDA和K-means聚类的绘画作品价值评估指标体系构建

针对目前绘画领域缺乏标准的价值评估指标体系,提出了基于BERT-LDA和K-means聚类的绘画作品价值要素挖掘方法。运用超平面法对绘画文献进行了停用词筛选,基于BERT-LDA模型构建了包含文本语义信息的融合特征向量,运用K-means算法对融合特征向量进行降维可视化,随之构建了绘画作品价值评估指标体系。结果表明,基于BERT-LDA模型和K-means算法识别的主题及主题词相比传统LDA模型的查准率、查全率和F值分别提升了28.5%、10%和21.5%。通过随机森林等算法对指标体系进行验证,验证了构建的绘画作品价值评估指标体系的科学性。

MOTION VECTOR RECOVERY METHOD BASED ON MEAN SHIFT PROCEDURE

This letter presents a novel Motion Vector (MV) recovery method which is based on Mean Shift (MS) procedure. According to motion continuity, MVs in local area should be similar. If projecting MV into 2-D feature space, local MVs in the feature space tend to cluster closely. To estimate the lost MVs in local area, recovery of lost MVs is modeled as clustering operation. MS procedure is applied to enforce each lost MV in the feature space to shift to the position where dominant MVs are gathered. Meanwhile, bandwidth estimation is statistically characterized by the variation of local standard de-viations; weighted value calculation is determined by estimation of overall standard deviation. Simu-lation results demonstrate their better performance when compared with other MV recovery ap-proaches and low computation cost.

一种用于文本聚类的改进k-means算法

k-means是目前常用的文本聚类算法,针对其最终搜索的局部极值与全局最优解偏差较大的缺点,采用一种基于局部搜索优化的思想来改进算法,并推导出目标函数的变化公式。根据目标函数值的改变对聚类结果作再次划分后,继续k-means迭代,拓展其搜索范围。理论分析和实验结果表明修改后的算法能有效地提高聚类的质量,且计算复杂度仍与数据集文本总数呈线性变化。

面向CPU/MIC异构架构的K-Means向量化算法

在大数据背景下,以K-Means为代表的聚类分析对于数据分析和挖掘十分重要。海量高维数据的处理给K-Means算法带来了性能方面的强烈需求。最新提出的众核体系结构MIC(many integrated core)能够为算法加速提供众核间线程级和核内指令级并行,使其成为K-Means算法加速的很好选择。在分析K-Means基本算法特点的基础上,分析了K-Means算法的瓶颈,提出了可利用数据并行的K-Means向量化算法,优化了向量化算法的数据布局方案。最后,基于CPU/MIC的异构架构实现了向量化K-Means算法,并且探索了MIC在非传统HPC(high performance computing)应用领域的优化策略。测试结果表明,K-Means向量化算法具有良好的计算性能和扩展性。

一种基于运动矢量分析的Mean shift目标跟踪算法

Mean shift算法作为一种非参密度估计算法,目前已被广泛应用于视频运动目标的跟踪。该算法具有运算效率快,对目标变形、旋转不敏感,在部分遮挡的情况下有一定鲁棒性等特点,但该算法在运动目标速度过快的情况下,由于没有考虑利用目标的运动方向和速度信息,因此在跟踪快速运动目标时容易造成跟踪丢失。针对此问题,提出了一种基于运动矢量分析与Mean shift跟踪算法相结合的新方法,即首先对视频编码过程中产生的运动矢量进行概率统计分析,以获取目标运动方向与运动速度的估计值,再以此修正Mean shift运动候选区域的中心位置,使每次搜索开始时,候选中心位置更接近实际目标中心位置。通过与传统的Mean shift算法的跟踪实验比较可见,新算法不仅提高了快速运动目标跟踪的精度,而且减少了算法的搜索迭代次数,从而提高了运算效率。该算法可适用于智能视频监控设备中的视频编码与目标跟踪同时计算的情况,实验结果表明,该算法是有效可行的。

一种改进的Mean Shift指纹图像分割算法

基于传统指纹图像分割算法,提出一种改进的Mean Shift指纹图像分割算法.该算法利用指纹图像固有的方向性特性,把经过分割后的每个指纹图像区域抽象为一个样本点,将区域内像素点的灰度均值作为均值向量,从而有效地实现了指纹图像分割.实验结果表明,该算法能准确地将指纹图像中的模糊区域和背景区域分离,提高了指纹图像分割的精确度,并且对于多数指纹图像准确性较好.

基于K-means和SVM的蓝牙室内定位算法

为了克服指纹定位过程中,由于信号不稳定所造成的定位精确不高,指纹漂移等问题,利用卡尔曼滤波对采集的蓝牙接收信号强度(RSS)数据进行预处理,并通过K-means算法对数据进行初始聚类;计算待测数据与各聚类中心的距离,将与待测数据临近的类簇数据进行融合。针对融合形成的新数据子集,训练出对应的支持向量机(SVM)模型,并完成待测数据的分类。经过测试,结果表明:算法的定位精度稳定在1. 5m以内,达到预期目标。

面向交通流检测的Mean Shift多目标自适应跟踪算法

随着社会的发展,交通问题越来越严重,交通流检测技术是解决这一问题的重要途径,而目标跟踪又是交通流检测中必不可少的一部分。目前在目标跟踪领域Mean Shift算法是被广泛采用的技术,但是传统的Mean Shift跟踪算法计算量很大,难以实现交通流检测中的多目标实时追踪。鉴于此,提出一种基于线性预测的Mean Shift跟踪算法。该算法引入一个预测矢量,用来预测目标在下一帧可能出现的位置,在跟踪时算法从预测的位置开始迭代,直至收敛于目标真实位置。实验结果表明,该算法从很大程度上提高了原有算法的效率,有利于实时跟踪。而且,为了解决Mean Shift跟踪算法中核函数带宽自适应的问题,还提出了一种基于比较目标中心灰度比例变化来实现带宽自适应更新的新方法。