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).

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and Ensemble Transform Kalman Filter Strategies in a Barotropic Model

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform Kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （HCFLL） based support vector machine（SVM） algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically dusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision.tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

Robust Source Localization in Shallow Water Based on Vector Optimization

Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem （originally intractable） can be reformulated in a convex form as the so-called second-order cone program （SOCP） and solved efficiently by using the well-established interior point method, such as the sottware tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.

State vector evolution localized over the edges of a square tight-binding lattice

We study thc time evolution of a state vector in a square tight-binding lattice, focusing on its evolution localized over the system surfaces. In this tight-binding lattice, the energy of atomic orbital centred at surface site is different from that at the interior （bulky） site by an energy shift U. It is shown that for the state vector initially localized on a surface, there exists an exponential law （y = ae^x/b ＋ Y0） determined by the absolute value of the energy shift, ｜U｜, which describes the transition of the state evolving on the square tight-binding lattice, from delocalized over the whole lattice to localized over the surfaces.

GENERALIZED VECTOR QUASI-EQUILIBRIUM PROBLEMSIN LOCALLY G-CONVEX SPACES

Some classes of generalized vector quasi-equilibrium problems ( in short, GVQEP) are introduced and studied in locally G-convex spaces which includes most of generalized vector equilibrium problems; generalized vector variational inequality problems, quasi-equilibrium problems and quasi-variational inequality problems as special cases. First, an equilibrium existence theorem for one person games is proved in locally G-convex spaces.. As applications, some new existence theorems of solutions for the GVQEP are established in noncompact locally G-convex spaces. These results and argument methods are new and completely different from that in recent literature.

Systems of generalized vector quasi-variational inclusions and systems of generalized vector quasi-optimization problems in locally FC-uniform spaces

In this paper, we introduce some new systems of generalized vector quasi-variational inclusion problems and system of generalized vector ideal （resp., proper, Pareto, weak） quasi-optimization problems in locally FC-uniform spaces without convexity structure. By using the KKM type theorem and Himmelberg type fixed point theorem proposed by the author, some new existence theorems of solutions for the systems of generalized vector quasi-variational inclusion problems are proved. As to its applications, we obtain some existence results of solutions for systems of generalized vector quasi-optimization problems.

Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

Generalized Bi quasi variational Inequalities in Locally Convex Topological Vector Spaces

In this paper we investigate generalized bi quasi variational inequalities in locally convex topological vector spaces. Motivated and inspired by the recent research work in this field,we establish several existence theorems of solutions for generalized bi quasi variational inequalities,which are the extension and improvements of the earlier and recent results obtained previously by many authors including Sun and Ding [18],Chang and Zhang [23] and Zhang [24].

Localization of Vector Field on Pure Geometrical Thick Brane

We investigate the localization of a five-dimensional vector field on a pure geometrical thick brahe. By introducing two types of interactions between the vector field and the background scalar field, we obtain a typical volcano potential for the first type of coupling and a Posehl-Teller potential for the second one. These two types of couplings guarantee that the vector zero mode can be localized on the pure geometrical thick brahe under certain conditions.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Transformation of Arabidopsis by Rice OsWRKY78::GFP Fusion Gene and Subcellular Localization of OsWRKY78 Protein

[Objective] The study was to understand the subcellular localization of OsWRKY78 protein in plants. [Method] Primers specific for OsWRKY78 gene were designed according to the OsWRKY78 full length sequence in Genbank. The gene was cloned by RT-PCR method. The gene was then recombined into a plasmid expression vector carrying green fluorescent protein (GFP) gene, pBinGFP. The recombinant was confirmed by PCR and enzyme digestion. The recombinant plasmid pBinGFP-OsWRKY was transformed into Arabidopsis through Agrobacterium tumefaciens strain GV3101 and transgenic plants were obtained. [Result] Measured by fluorescence microscopy, the expression of OsWRKY78 and GFP fusion protein in root tip cells was localized in the nucleus. [Conclusion] This study laid the foundation for further investigating the function of OsWRKY78 gene and its role in related signal transduction and provided theoretical basis for exploring the relation between OsWRKY78 gene and brown planthoppers.

基于i-vector局部加权线性判别分析的说话人识别

基于i-vector的说话人识别系统通常采用LDA来消除训练和测试语音之间信道失配,不能保证样本在待识别语音近邻区域内具有最佳的分离度,这就使得目标说话人和其近邻间的得分差异较小,进而导致识别准确性下降。针对该问题,提出基于i-vector局部加权线性判别分析的说话人识别方法(LWLDA)。在计算类内和类间散度时,增加待识别语音近邻样本权重。在此基础上,通过提高待识别语音近邻域局部类间的分辨能力,尽可能减少因信道差异而产生的识别错误。在不同语音库上的实验结果表明:LWLDA在复杂信道环境下能够保持良好的鲁棒性;在交叉信道条件下的识别准确率比LDA平均提高3.6%。

基于局部化原理和概率模型的LVQ改进算法

利用局部化原理和概率模型的优化方法 ,提出一种LVQ改进算法———基于局部化原理和概率模型的LVQ算法 (LocalizationprincipleandProbabilitybasedLVQ ,LoPLVQ) .与传统LVQ算法相比 ,不仅缩短训练时间 ,而且具有较高的识别率 .实验结果表明改进算法可用来解决大规模的模式识别问题 .

基于自适应反馈机制的小差异化图像纹理特征信息数据检索

针对小差异化图像纹理相似度和噪声等因素导致纹理特征挖掘效果较差的问题,设计一种自适应反馈结合局部二值机制的小差异化图像纹理特征挖掘方法.使用规范割策略将图像数据各点拟作节点,使用节点间的连接线权重计算2点的相似度,采用支持向量机训练图像属性参数分类图像属性,进一步归纳图像类别.运用跳跃连接方法传输图像数据,将数据引入卷积神经网络剔除图像噪声.将中心点像素值当作反馈因子,创建自适应反馈判定条件,利用局部二值模式实现小差异化图像纹理特征挖掘.在MATLAB平台进行试验,从卷积神经网络收敛性、图像频谱纹理单元数、平均准确率、图像数据匹配度等方面进行了分析,分析结果表明:随着迭代次数不断增加,精度损失逐渐降低,基本收敛到稳定值,达到了预期训练效果;所提出方法挖掘的图像频谱纹理单元数3800个以上,更贴合人眼视觉信息;平均准确率为0.87,准确率@1、准确率@5和准确率@10的平均值分别为0.90、0.84和0.85;挖掘耗时低于5 s,图像数据匹配度高于90.3%,验证了所提出方法可在图像纹理特征识别操作中发挥应有作用.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Large scale classification with local diversity AdaBoost SVM algorithm

Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

Application of Improved LMD, SVD Technique and RVM to Fault Diagnosis of Diesel Valve Trains

Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of singular value decomposition(SVD) technique, a new method based on improved local mean decomposition(LMD), SVD technique and relevance vector machine(RVM) was proposed for the identification of diesel valve fault in this study. Firstly, the vibration signals were acquired through the vibration sensors installed on the cylinder head in one normal state and four fault states of valve trains. Secondly, an improved LMD method was used to decompose the non-stationary signals into a set of stationary product functions(PF), from which the initial feature vector matrices can be formed automatically. Then, the singular values were obtained by applying the SVD technique to the initial feature vector matrixes. Finally, slant binary tree and sort separability criterion were combined to determine the structure of multi-class RVM, and the singular values were regarded as the fault feature vectors of RVM in the identification of fault types of diesel valve clearance. The experimental results showed that the proposed fault diagnosis method can effectively extract the features of diesel valve clearance and identify the diesel valve fault accurately.

Local spatial properties based image interpolation scheme using SVMs

Image interpolation plays an important role in image process applications. A novel support vector machines （SVMs） based interpolation scheme is proposed with increasing the local spatial properties in the source image as SVMs input patterns. After the proper neighbor pixels region is selected, trained support vectors are obtained by training SVMs with local spatial properties that include the average of the neighbor pixels gray values and the gray value variations between neighbor pixels in the selected region. The support vector regression machines are employed to estimate the gray values of unknown pixels with the neighbor pixels and local spatial properties information. Some interpolation experiments show that the proposed scheme is superior to the linear, cubic, neural network and other SVMs based interpolation approaches.