Stability Criterion with Less LMI Variables for Linear Discrete-time Systems with Additive Time-delays

In this correspondence paper, an equivalent stability criterion with minimal number of linear matrix inequality （LMI） variables is presented for a delay-dependent stability criterion reported recently in the International Journal of Automation and Computing for a class of linear discrete-time systems with additive time delays. The reported stability criterion for the additive timedelay systems has more number of matrix variables in the LMI and, hence, demand additional computational burden. The proposed equivalent stability criterion, unlike the reported one, does not involve free-weighing matrices and encompass only the matrix variables that are associated in the Lyapunov-Krasovskii functional, making the criterion mathematically less complex and computationally more effective.

A Linearized and Unified Yield Criterion of Metals and Its Application

In this paper a linearized and unified yield crierion of metals is presented, which is in a form of a set of linear functions with two pararneters. The parameters are ex- pressed in terms of tension yield stress and so-called “shear-stretch ratio” and can bereadily determined from experimental data. It is shown that in stress space the set of yield functions is a set of polygons with twelve edges located between the Tresca’s hexagon and twin-shear-stress hexagon￣[1]. In this paper the present yield function isused to analyse the prestressiap loose running fit cylinders.

基于卷积神经网络的SAR图像舰船分类

针对合成孔径雷达图像中斑点噪声导致图像分类准确率低的问题,提出一种基于改进VGG16的分类算法。在卷积层中加入一层注意力层,专注于重要特征,抑制不重要特征,从而抑制斑点噪声。在目标函数中引入Fisher损失函数,用该函数对特征的类内距离和类间距离进行约束,从而使得由于斑点噪声所造成的分类错误减少。通过实验可知,相比于改进前的网络,分类准确率提高了5.63百分点,有效改善了因为斑点噪声所造成的分类准确率低的问题。

基于空间灰度重心推进的线结构光中心提取

利用线结构光对工件进行三维测量时,快速、准确提取激光光条中心是测量系统的关键技术之一。提出一种基于空间灰度重心推进的激光中心提取方法。该方法基于激光条纹空间灰度重心向前推进,结合八邻域判定法进行光条空间区域的选取,接着对选取的区域进行新的空间灰度重心坐标提取;在完成整幅图像提取后,采取3σ准则剔除异常中心点,最终获得激光中心坐标信息。实验结果表明,所提算法对不同类型条纹的适用性强,提取效果稳定。提取中心均方根误差为0.492pixel,相较于提取精度最优的Steger改进算法提升了9.8%,提取速度提高了5倍;相较于处理速度最优的内部推进算法,在保持提取速度基础上,提取精度提升了24.1%。同时,所提算法极大增强了对欠曝光光条图像的处理能力,有效降低了环境对线结构光测量的限制。

基于双HSIC和稀疏正则化的多标签特征选择

为了合理地利用多标签数据中的样本信息和标签信息,提高模型的分类性能,提出了基于双希尔伯特-施密特独立性准则(Hilbert-Schmidt independence criterion,HSIC)和稀疏正则化的多标签特征选择(DHSR)。该方法在线性映射的基础上引入双HSIC作为正则项,增强伪标签空间和特征空间之间的依赖关系,增强伪标签空间和真实标签空间之间的依赖关系。并使用L2,1范数作为稀疏正则项,以提高模型的泛化能力和减少模型的计算复杂度。最后,在多个经典多标签数据集上的对比实验结果验证了DHSR的有效性和优越性。

改进无模型自适应迭代学习的直线电机滑模控制

二维直线电机具有非线性、多变量及强耦合等特点,其精确模型无法获得,同时,在实际过程中由于参数摄动及扰动而造成的运行不稳定现象,致使对其跟踪控制十分困难。针对以上问题,根据无模型自适应迭代学习控制不依赖被控系统精确数学模型的特点,及滑模控制具有可设计且与对象参数和扰动无关的滑动模态,提出一种改进无模型自适应迭代学习的滑模结构复合控制策略。在无模型自适应迭代学习控制方案的准则函数中加入误差变化率,并对其收敛性进行分析论证;然后在紧格式动态线性化基础上,设计指数趋近律滑模控制,使改进无模型自适应迭代学习的滑模控制复合策略能够克服不稳定现象并具有很强的鲁棒性,从而进一步提高系统响应速度和控制精度。最后通过仿真和实物验证,控制精度稳定在1μm范围内,并与其他控制方案相比,验证了所提方法的准确性和有效性。

不同老化环境下粘接失效准则改进研究

文章通过筛选线性应力组合的方法建立了粘接结构失效准则,将失效准则嵌入到粘接接头仿真模型中,再通过试验与有限元仿真相结合的方法,验证了失效准则的准确性。对比试验与仿真结果,仿真模型展示了不同老化条件下胶层裂纹扩展及失效过程,与实物试验失效过程基本吻合;同时仿真模型计算得出的不同老化条件下失效峰值与试验测得的初始失效载荷相对误差均在20%以内。验证结果表明:该失效准则具有较高的准确性,可用于不同老化条件下粘接结构强度校核,具有较大的理论与工程应用价值。

基于自适应GMM阶数与混合特征的说话人识别研究

针对高斯混合模型(GMM)阶数选取缺陷和说话人特征信息不足的问题,提出了基于自适应GMM阶数和多种语音特征融合的说话人识别算法。首先,通过提取梅尔频率倒谱系数(MFCC)和线性预测梅尔频率倒谱系数(LPMFCC),并根据Fisher准则得到一个17维的MFCC和LPMFCC参数组合的混合特征参数,以增强说话人的特征信息。然后,根据自适应思想,在K-means聚类算法中计算簇内误差平方和(SSE)。最后,通过肘部法则自适应调整K值,以获得一个最优GMM阶数,使得系统在已有的声纹特征下获得最优的识别效果。结果表明,该算法不仅完善了说话人的特征信息,并且克服了对GMM阶数选取的缺陷。最终结合LPCC和MFCC两种特征算法,融合得到的混合特征LPMFCC+MFCC的识别率相比于LPCC和MFCC提升了26.34%和12.34%。

Selection of the Linear Regression Model According to the Parameter Estimation

In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.

CONSENSUS ANALYSIS AND DESIGN OF LINEAR INTERCONNECTED MULTI-AGENT SYSTEMS

We deal with the state consensus problem of a general Linear Interconnected Multi-Agent System （LIMAS） under a time-invariant and directed communication topology. Firstly, we propose a linear consensus protocol in a general form, which consists of state feedback of the agent itself and feedback form of the relative states between the agent and its neighbors. Secondly, a state-linear-transformation is applied to equivalently transform the state consensus problem into a partial stability problem. Based on the partial stability theory, we derive a sufficient and necessary criterion of consensus convergence, which is expressed via the Hurwitz stability of a real matrix constructed from the parameters of the agent models and the protocols, and present an analytical formula of the consensus function. Lastly, we propose a design procedure of the gain matrices in the protocol by solving a bilinear matrix inequality.

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.

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.

Delay-dependent robust stabilization for a class ofneutral systems with nonlinear perturbations

This note deals with the problem of stabilization/stability for neutral systems with nonlinear perturbations. A new stabilization/stability scheme is presented. Using improved Lyapunov functionals, less conservative stabilization/stability conditions are derived for such systems based on linear matrix inequalities （LMI）. Numerical examples are provided to show that the proposed results significantly improve the allowed upper bounds of the delay size over some existing ones in the literature.

Fast Computation of Pareto Set for Bicriteria Linear Programs with Application to a Diet Formulation Problem

In case of mathematical programming problems with conflicting criteria, the Pareto set is a useful tool for a decision maker. Based on the geometric properties of the Pareto set for a bicriteria linear programming problem, we present a simple and fast method to compute this set in the criterion space using only an elementary linear program solver. We illustrate the method by solving the pig diet formulation problem which takes into account not only the cost of the diet but also nitrogen or phosphorus excretions.

Derivation of Plastic Work Rate Done per Unit Volume for Mean Yield Criterion and Its Application

In Haigh Westergaard stress space linear combination of twin shear stress and Tresca yield functions is called the mean yield （MY） criterion. The mathematical relationship of the criterion and its plastic work rate done per unit volume were derived. A generalized worked example of slab forging was analyzed by the criterion and its corresponding plastic work rate done per unit volume. Then, the precision of the solution was compared with those by Mises and Twin shear stress yield criterions, respectively. It turned out that the calculated results by MY criterion were in good agreement with those by Mises criterion.

基于k-近邻局部线性邻域重建的多视角聚类算法

多视图聚类旨在利用不同视图间互为差异、互相补充的信息对数据对象进行聚类,如何融合不同视角的数据是多视角聚类算法的重要问题之一。为了能更准确有效地刻画视角间的相似关系,提出一种基于k-近邻局部线性邻域重建的多视角聚类算法。首先,利用数据点间的距离分配概率近邻,得到各视角数据对应的相似矩阵;其次,通过引入k-近邻,对各视角相似矩阵进行局部线性邻域重建后融合为统一的相似矩阵;同时,引入HSIC刻画不同视角的多样性。通过将统一图的学习与多样性学习整合在统一的框架中,本模型有能力输出一个包含了各视图多样信息的融合图。通过交替迭代算法,所提模型可以被很好地优化。多个公开数据集上的对比实验证明了所提出算法的有效性优于其他已有算法。

Analysis and Experiments on Two Linear Discriminant Analysis Methods

Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n-k+1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covariance-orthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA.

基于线性预测分析的金属材料超声识别方法

针对金属材料中超声波的背向散射与其微观结构特征密切相关,即使材料相同,超声信号也会因金属晶粒的尺寸和径向分布的随机性而产生差异的问题。计算了金属样品超声信号的线性预测系数,以此作为其超声指纹,结合拉伊达判别准则确定的阈值,完成了对形状、材料完全一致的金属样品的识别。结果表明:基于线性预测分析的超声金属材料识别方法可以快速、准确地识别出各样品。该方法对贵重金属材料、金属文物的识别保护具有一定的参考应用价值。

飞机飞行载荷的简化计算方法研究

飞行载荷分析是一项复杂而繁重的工作,研究一种适用于方案设计阶段飞行载荷的快速分析方法,对于提高飞行载荷的计算效率具有重要意义。基于小扰动线性分析理论,归纳翼身气动载荷、平尾气动载荷、垂尾气动载荷、舵面铰链力矩的理论计算方法,以及升力面的气动载荷分布、惯性载荷分布、剪力和弯矩的工程计算方法,即飞行载荷的简化计算方法。针对某型单座竞技飞机的飞行载荷,以外形尺寸、质量特性和气动导数作为输入,通过Matlab仿真分析,得到各个部件的气动载荷、惯性载荷、舵面铰链力矩、剪力和弯矩等参数响应。结果表明:该简化方法能够根据较少的输入数据快速求解出各个动力学参数,计算结果可以作为方案设计阶段结构设计的载荷输入。