Two-Level Linear Clustering Protocol Based on Wireless Sensor Networks

Aiming at the defects of the nodes in the low energy adaptive clustering hierarchy （LEACH） protocol, such as high energy consumption and uneven energy consumption, a two-level linear clustering protocol is built. The protocol improves the way of the nodes distribution at random. The terminal nodes which have not been a two-level cluster head in the cluster can compete with the principle of equivalent possibility, and on the basis of the rest energy of nodes the two-level cluster head is selected at last. The single hop within the cluster and single hop or multiple hops between clusters are used. Simulation experiment results show that the performance of the two-level linear clustering protocol applied to the Hexi corridor agricultural field is superior to that of the LEACH protocol in the survival time of network nodes, the ratio of success, and the remaining energy of network nodes.

Long-term system load forecasting based on data-driven linear clustering method

In this paper, a data-driven linear clustering(DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling.Then optimal autoregressive integrated moving average(ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.

CLUSTERING POPULATIONS BY MIXED LINEAR MODELS

Two mixed linear models are proposed for grouping populations by a dissimilarity coefficent which has two parameters for squared difference of marginal mean and variance component of interaction.Cluster trees can be constructed by the mixed linear model approaches for experimental data with sampling errors within populations or with some missing values.Unweighted pair-group method ( UPGM ) is suggested as fusion method. Sampling variances of estimated dissimilarity coefficient can be obtained by the jackknife procedure.A one-tail t-test is applicable for detecting significance of dissimilarity of populaions within specific group.Unbiasedness and efficiency for estimation of dissimilarity coefficients are proved by Monte Carolo simulations.Worked example from cotton yield data is given for demonstration of the use of these cluster methods.

Fast generation of cluster states in a linear ion trap

We propose a practical scheme to generate cluster states by simultaneously accomplishing two-qubit conditional gating on an array of equidistant ions by using transverse modes. Our operation is robust to heating and insensitive to Lamb-Dicke parameter. Meanwhile, as it is carried out in a geometric quantum computing fashion, our scheme enables the fast and high-fidelity generation of cluster states. The experimental feasibility is discussed with sophisticated ion trap techniques.

Configuration evolution of Al_n(n=3,4,6,13,19) clusters studied using linear synchronous transit method

The evolution of configurations of Aln (n=3,4,6,13,19) clusters were investigated using linear synchronous transit (LST) method. The stable structures of Al3, Al4, Al6, Al13, Al19 clusters were confirmed to be triangle, rhombus, octahedron, icosahedron and double icosahedron, respectively. For Al6 and Al19 clusters there are metastable structures of parallelogram and octahedron, respectively, whereas in the Al3, Al4 and Al13 clusters, no metastable configuration are validated. A large energy gap and a low energy barrier between the parallelogram and the octahedron of the Al6 cluster indicate the transformation from its metastable configuration to stable octahedron to be rather easy. By contrast, a small energy gap and a high energy barrier between the stable and metastable structures of Al19 cluster mean its configuration evolution from the octahedron to the double icosahedron occurs hardly, therefore the metastable octahedron configuration of Al19 cluster can be extensively detected in experiments and simulations.

Cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay

This paper deals with the cluster exponential synchronization of a class ot complex networks wlm nyorm coupm^g and time-varying delay. Through constructing an appropriate Lyapunov-Krasovskii functional and applying the theory of the Kronecker product of matrices and the linear matrix inequality （LMI） technique, several novel sufficient conditions for cluster exponential synchronization are obtained. These cluster exponential synchronization conditions adopt the bounds of both time delay and its derivative, which are less conservative. Finally, the numerical simulations are performed to show the effectiveness of the theoretical results.

A Criterion for Determining the Number of Clusters

Aiming to provide an appropriate number K of clusters, in this paper, we propose a new criterion function - H criterion function, whose three properties have also been proved. We validate the performance of the H criterion function on one artificial dataset and three real-world datasets, and the results are almostly consistent with a previous method. The nonparametric criterion we proposed is intuitive, simple and the computational cost is acceptable.

Time Series Forecasting of Hourly PM10 Using Localized Linear Models

The present paper discusses the application of localized linear models for the prediction of hourly PM10 concentration values. The advantages of the proposed approach lies in the clustering of the data based on a common property and the utilization of the target variable during this process, which enables the development of more coherent models. Two alternative localized linear modelling approaches are developed and compared against benchmark models, one in which data are clustered based on their spatial proximity on the embedding space and one novel approach in which grouped data are described by the same linear model. Since the target variable is unknown during the prediction stage, a complimentary pattern recognition approach is developed to account for this lack of information. The application of the developed approach on several PM10 data sets from the Greater Athens Area, Helsinki and London monitoring networks returned a significant reduction of the prediction error under all examined metrics against conventional forecasting schemes such as the linear regression and the neural networks.

Controlling optical responses through local dielectric resonance in nanometre metallic clusters

Optical responses in dilute composites are controlled through the local dielectric resonance of metallic clusters. We consider two located metallic clusters close to each other with admittances ε1 and ε2. Through varying the difference admittance ratio η[= （ε2 - ε0）/（ε1 - ε0）], we find that their optical responses are determined by the local resonance. There is a blueshift of absorption peaks with the increase of η- Simultaneously, it is known that the absorption peaks will be redshifted by enlarging the cluster size. By adjusting the nano-metallic cluster geometry, size and admittances, we can control the positions and intensities of absorption peaks effectively. We have also deduced the effective linear optical responses of three-component composites εe=ε0 （1＋∑^n n=1[（γn1＋ηγn2）/（ε0（s-sn））]） and the sum rule of cross sections：∑^n n=1（γn1＋ηγn2）=Nh1＋Nh2,, where Nh1and Nh2 are the numbers of εl and ε2 bonds along the electric field, respectively. These results may be beneficial to the study of surface plasmon resonances on a nanometre scale.

局部线性下的函数型主成分聚类算法

函数型聚类分析在统计学领域被广泛关注,其分析过程通常在降维目标实现后进行。为了有效解决函数型主成分聚类问题,文章结合局部线性嵌入算法(Locally Linear Embedding,LLE)在非线性空间下的适用性,提出了一种局部线性下的函数型主成分分析模型(LLE Function Principle Component Analysis,LFPCA)。首先,采用函数型主成分分析法作为降维目标方法,改进了FPCA的算法模型,通过将LLE算法的权重系数矩阵与函数型主成分定义相结合,构建出一个适用于非线性空间下的聚类算法;其次,在求解算法的过程中定义了函数型主成分得分,并结合EM算法构建出GMM模型来近似函数型算法的概率密度函数,使模型更高效且适用性更强;最后,通过随机模拟实验及应用分析验证了LFPCA算法模型在真实数据集上具有良好的聚类效能。

基于超像素快速模糊聚类的印刷品图像分割方法

针对当前彩色印刷品色差检测过程中效率低、复杂性高等问题,提出了一种基于超像素快速模糊聚类的印刷品图像分割方法(SFFCM)。先用简单线性迭代聚类(SLIC)算法将图像分割为紧密相邻的超像素区域。每个超像素区域被视为一个独立的聚类单元。随后,将模糊C均值聚类(FCM)算法应用于超像素的归属关系计算中,即引入隶属度值,允许超像素归属于多个聚类中心,并通过权衡归属度值来实现模糊聚类。实验结果表明,相对于其他算法,本文方法在保持良好实时性的同时,实现了较好的分割效果,有效平衡了算法复杂度与分割效果之间的关系。

Nonparametric Estimation in Linear Mixed Models with Uncorrelated Homoscedastic Errors

Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component in which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao’s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.

Electron-rich Polynuclear Transition Metal Clusters:Ⅱ.Synthetic and Structural Studies of Some Polynuclear Coinage Metal Cluster Compounds

The syntheses and structures of a novel series of polynuclear coinage metal cluster compounds are discussed. The most fascinating structural characteristic of the Au-Ag alloy clusters with Au_18Ag_19 and Au_18Ag_19 cores and phosphine ligands, which were prepared by the reduction of mononuclear coinage metal complexes R3PAuX and R3PAgX with NaBH4 in organic solution, is their construction from 13-atom gold-centred icosahedral Au_7Ag_6 building blocks. The structures of the polynuclear coinage metal clusters with R2dtc ligands are variable with either unlimited linear chain or triangular M3 units.

Shrinkage Estimation of Semiparametric Model with Missing Responses for Cluster Data

This paper simultaneously investigates variable selection and imputation estimation of semiparametric partially linear varying-coefficient model in that case where there exist missing responses for cluster data. As is well known, commonly used approach to deal with missing data is complete-case data. Combined the idea of complete-case data with a discussion of shrinkage estimation is made on different cluster. In order to avoid the biased results as well as improve the estimation efficiency, this article introduces Group Least Absolute Shrinkage and Selection Operator (Group Lasso) to semiparametric model. That is to say, the method combines the approach of local polynomial smoothing and the Least Absolute Shrinkage and Selection Operator. In that case, it can conduct nonparametric estimation and variable selection in a computationally efficient manner. According to the same criterion, the parametric estimators are also obtained. Additionally, for each cluster, the nonparametric and parametric estimators are derived, and then compute the weighted average per cluster as finally estimators. Moreover, the large sample properties of estimators are also derived respectively.

Knowledge Discovery in Learning Management System Using Piecewise Linear Regression

Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.

基于电子鼻的海产干制品风味研究及品质鉴定

要:采用电子鼻采集干贝、墨鱼干、鱿鱼干、虾米、小鱼干、花蛤干的挥发性物质,分析不同种类的气味物质成分差异和特征,构建了线性判别模型和聚类分析树图,实现六种海产干制品的分类鉴别,进一步构建了干贝的品质判别模型用于未知样本的大小鉴定。为海产干制品的风味研究、鉴定、加工生产提供新的思路和方法,有望在海产干制品的品质研究、风味形成、加工指导等方面发挥更大的作用。

基于自适应聚类和线性规划最优选点的彩色图像低成本打印方法

彩色点画是一种从视觉上由大量小像素点构建图像的艺术技术,像素个数的多少直接影响着构图的成本。其优化选点构图方法为实现低成本打印提供了一个重要的方式。目前,点画生成存在着多通道采样点难以均匀分布,颜色层次难以兼顾等难点,并耗费大量的计算成本。对此,提出了一种基于超像素自适应聚类和线性规划最优选点的彩色点画生成方法,该方法在初步超像素划分图像的基础上,使用基于颜色密度峰值的自适应聚类方法得到最佳聚类个数,并进一步划分子聚类,然后根据每个子聚类的颜色均值作为子聚类内部选点的最佳间隔距离,在选点的同时依据SSIM指标,建立目标优化模型,通过数学优化器Gurobi实现模型选点,使点保留最少个数的目标基础上,同时保持聚类内部分布均匀和颜色渐变层次,以提高所生成的点画图像的可视化效果。实验结果表明,本文算法极大地降低了像素个数并在生成的点画的平均结构相似性(mean structural similarity index measure,SSIM)、峰值信噪比(peak signal to noise ratio,PSNR)等评价指标方面均优于当前方法。

基于L0梯度平滑与图像分块聚类的海天线检测

海天线检测在海洋工程安防活动中具有重要的意义,真实海洋环境中的海天线检测易受云朵、海浪、光照变化、目标遮挡物、边界模糊等外界干扰。为了实现对真实海洋环境中海天线的检测,本研究提出一种基于L0梯度平滑和图像分块聚类的海天线检测算法。首先,对图像进行L0梯度平滑滤波,以增强海天线边缘,弱化非海天线因素干扰;接着,将图像沿着竖直方向分割成若干等宽图像块,以降低整体环境干扰,加强局部海天线检测效果;然后,通过Canny算子和霍夫变换提取每个分割图像块中的直线段;最后,采取K-means聚类算法提取每个图像块中的海天线段,拟合生成完整海天线。实验结果表明,在真实的海天线数据集中,本研究方法获取的矩形框重叠率平均精度为93.22%,角度差平均精度为7.66%,均高于文中选取的近年典型对比算法。满足实际海天线检测抗干扰强、准确率高、适应性广等要求。

An Adaptive Non-Linear Map and Its Application

A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

基于场景线性化重塑的用户侧储能容量高效规划方法

推进用户侧分布式光储一体化发展是提高新型电力系统新能源消纳能力的重要举措。然而,储能高昂的投资成本阻碍了用户侧光储系统的推广进程,亟需合理的储能容量优化配置方案为用户提供更加理性的决策依据,以提升系统经济性和环境友好性。现有容量规划方法往往基于典型场景进行储能运行模拟,未考虑全年每个典型场景之间的功率波动特征以及某些极端场景对于容量配置结果的影响,难以保证规划结果的准确性。为此,文中提出了一种基于场景线性化重塑的用户侧储能容量高效规划方法。首先,以聚类结果中的典型场景为基,通过最小二乘法近似地线性表示全年中的极端场景;其次,将场景线性化结果嵌入储能全年运行约束中,并通过重新构造决策变量来减小优化问题的规模;最后,通过实际算例分析了用户侧配置储能的经济效益,并将所提出的规划方法与已有方法进行仿真结果对比。结果表明,所提出的规划方法能够减小精确模型运行所需的决策变量规模,在提升计算效率的同时取得比已有近似方法更准确的规划结果。