Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

Biologically Inspired Tensor Features

According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate's visual cortex. Therefore, it is time to reconsider face recognition by using biologically inspired features. In this paper, we represent face images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over S1 units by using a maximum operation to reserve only the maximum response of each local area of S1 units. The new representation is termed C1 Face. Because C1 Face is naturally a third-order tensor (or a three dimensional array), we propose three-way discriminative locality alignment (TWDLA), an extension of the discriminative locality alignment, which is a top-level discriminate manifold learning-based subspace learning algorithm. TWDLA has the following advantages: (1) it takes third-order tensors as input directly so the structure information can be well preserved; (2) it models the local geometry over every modality of the input tensors so the spatial relations of input tensors within a class can be preserved; (3) it maximizes the margin between a tensor and tensors from other classes over each modality so it performs well for recognition tasks and (4) it has no under sampling problem. Extensive experiments on YALE and FERET datasets show (1) the proposed C1Face representation can better represent face images than raw pixels and (2) TWDLA can duly preserve both the local geometry and the discriminative information over every modality for recognition.

基于参数化求和不变量与特征重整的形状匹配

从特征提取和特征匹配两方面考虑,提出了一种鲁棒的形状匹配方法。首先,基于求和不变量,设计了基于面积的形状参数化和归一化方法,提出了参数化求和不变量,该不变量基于形状局部描述且采用积分算子计算,具有较好的鲁棒性和仿射不变性。然后,为进一步提高形状匹配的鲁棒性,在特征匹配上,分析了参数化求和不变量的先验信息,设计了基于特征重整的匹配距离函数,并通过动态规划进行实现。仿真实验表明了所提方法的有效性。

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Incremental pairwise discriminant analysis based visual tracking

The distinguishment between the object appearance and the background is the useful cues available for visual tracking in which the discriminant analysis is widely applied However due to the diversity of the background observation there are not adequate negative samples from the background which usually lead the discriminant method to tracking failure Thus a natural solution is to construct an object-background pair constrained by the spatial structure which could not only reduce the neg-sample number but also make full use of the background information surrounding the object However this Idea is threatened by the variant of both the object appearance and the spatial-constrained background observation especially when the background shifts as the moving of the object Thus an Incremental pairwise discriminant subspace is constructed in this paper to delineate the variant of the distinguishment In order to maintain the correct the ability of correctly describing the subspace we enforce two novel constraints for the optimal adaptation (1) pairwise data discriminant constraint and (2) subspace smoothness The experimental results demonstrate that the proposed approach can alleviate adaptation drift and achieve better visual tracking results for a large variety of nonstationary scenes (C) 2010 Elsevier B V All rights reserved

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

L1-Norm-Based 2DPCA

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

Effect of annealing on the deformation mechanism of a styrene/n-butyl acrylate copolymer latex film investigated by synchrotron small-angle X-ray scattering

The deformation mechanism of a styrene/n-butyl acrylate copolymer latex film subjected to uniaxial tensile stress was studied by small-angle X-ray scattering. The influence of annealing at 23, 60, 80, and 100 degrees C for 4 h on microscopic deformation processes was elucidated. It was demonstrated that the microscopic deformation mechanism of the latex films transformed gradually from nonaffine deformation behavior to affine deformation behavior with increasing annealing temperature.

In Situ Observation of Tensile Deformation Processes of Soft Colloidal Crystalline Latex Fibers

The deformation mechanism or styrene/n-butyl acrylate copolymer latex films with fiber symmetric crystalline structure subjected to uniaxial stretching was studied using synchrotron small-angle X-ray scattering technique. The fibers were drawn at angles or 0, 35, and 55 degrees with respect to the Fiber axis. In all cases, the microscopic deformation within the crystallites was Found to deviate from affine deformation behavior with respect to the macroscopic deformation ratio. Moreover, the extent of this deviation is different in the three cases. This peculiar behavior can be attributed to the relative orientation of the (111) plane of the crystals, the plane of densest packing, with respect to the stretching direction in each case. When the stretching direction coincides with the crystallographic (111) plane, which is the case for stretching directions of 0 and 55 degrees with respect to the fiber axis, the microscopic deformation deviates less from affine behavior than when the stretching direction is arbitrarily oriented with respect to the crystallographic (111) plan.

Fractal roughness characterization of super-ground Mn-Zn ferrite single crystals

The surface of superground Mn-Zn ferrite single crystal may be identified as a self-affine fractal in the stochastic sense. The rms roughness increased as a power of the scale from 10(2) nm to 10(6) nm with the roughness exponent alpha = 0.17 +/- 0.04, and 0.11 +/- 0.06, for grinding feed rate of 15 and 10 mu m/rev, respectively. The scaling behavior coincided with the theory prediction well used for growing self-affine surfaces in the interested region for magnetic heads performance. The rms roughnesses increased with increase in the feed rate, implying that the feed rate is a crucial grinding parameter affecting the supersmooth surface roughness in the machining process.

亚历山大藻分子鉴定和荧光原位杂交检测方法研究

近年来，世界沿海国家有害赤潮发生的频率、规模及危害都有上升趋势，有害赤潮已经成为重要的近海环境问题之一。要有效防范有害赤潮带来的危害效应，建立和发展可靠、有效的赤潮监测手段非常重要。目前，对于赤潮藻种的监测主要依靠显微观察的方法，在实际应用中经常遇到困难。首先，亲缘关系相近的物种在形态上差异很小，如甲藻门亚历山大藻属的一些种类，仅细胞壁上个别甲片的结构有细微差别，并且这些形态学指标还容易受环境条件及生长阶段的影响。另外，这种以形态学为基础的分析方法，分析速度慢、耗时长，对操作人员的要求较高，难以满足浮游植物种群动力学监测“量大、连续”的要求。因此，本研究将分子生物学的技术和方法应用于赤潮监测，力求提高赤潮藻种鉴定的准确性和检测工作的效率。 亚历山大藻是一类重要的有害赤潮藻，该藻属中一些产毒特性差别很大的藻种，单从表形特征难以明确区分，从而限制了基于形态观察的监测技术的应用。本研究中，我们尝试应用分子生物学技术与方法，开展了该藻属藻种分子鉴定和荧光原位杂交检测方法的研究。在亚历山大藻的分子鉴定方面，我们采用了核糖体RNA基因（rDNA）序列分析的方法，首次测定了9株分离自中国沿海的（以及实验室保有的其它两株）亚历山大藻的rDNA序列全长，其中包括核糖体小亚基（SSU）rDNA、大亚基（LSU）rDNA、5.8S rDNA及内转录间隔区（ITS）区序列。序列分析结果显示，这些藻株包含了5种核糖体类型，分别是塔玛复合种亚洲温带（Temperate Asian）核糖体类型（TSC-TA），塔玛复合种西欧（West European）核糖体类型（TSC-WE），相关亚历山大藻（A. affine）核糖体类型（AF），微小亚历山大藻（A. minutum）葡萄牙（Portugal）核糖体类型（M-PO）和微小亚历山大藻新西兰（New Zealand）核糖体类型（M-NZ）。将测获的rDNA序列划分为若干保守性不同的区段，分别进行系统发育分析（结合GenBank数据库中保存的其它亚历山大藻相关序列）。结果显示，LSU rDNA D1-D2区是对该藻属藻种进行分子鉴定和系统发育研究的较好区段。同时，为解决建立亚历山大藻克隆培养的困难，我们应用单细胞rDNA序列分析方法，对亚历山大藻单个细胞直接进行了种类鉴定。结果表明，该方法适用于不同生活史阶段的亚历山大藻。 在亚历山大藻的检测技术方面，我们进一步扩展和完善了针对完整细胞的荧光原位杂交检测方法。首先，通过对不同核糖体类型藻株rDNA序列信息的对比分析，针对各自特异的序列位点，设计了特异性rRNA标记探针。经荧光原位杂交实验检验，实现了对5种核糖体类型亚历山大藻的特异性标记。其中，针对WE、M-PO及M-NZ核糖体型的特异性探针为首次获得，另外两个探针是针对TA和AF核糖体类型rRNA新的位点所设计。同时，对影响探针标记效果的诸多因素进行了分析和探讨。此外，在2007年春季长江口海域赤潮调查中，首次应用特异性核酸探针和荧光原位杂交检测方法，调查了该海域亚历山大藻的丰度。结果表明，在4月4日-4月10日的样品中，亚历山大藻达到了较高的密度，最高密度达到103cells/L。同时发现，实验中样品的保存方法有待改进。随后的研究表明，盐醇固定方法及多聚甲醛/甲醇固定方法，可以较好的保持rRNA不被降解并适宜杂交（至少3个月时间）。 总之，本研究首次测定并分析了11株亚历山大藻（9株分离自中国沿海）的rDNA全序列信息。在此基础上，获得了5种核糖体类型亚历山大藻的特异性rRNA标记探针，其中3种为首次获得。另外，实验证明，单细胞rDNA分析技术和荧光原位杂交检测方法，在自然水体中亚历山大藻的直接鉴定及丰度调查中，均具有良好的应用前景。这一工作为我国近海亚历山大藻的鉴定和检测提供了理论依据和方法学基础，希望对该藻赤潮的监测工作有推动作用。 关键词：亚历山大藻 遗传探针 rRNA rDNA 荧光原位杂交 系统发育

亚历山大藻（非PSP产毒株）毒理的初步研究

本文以一株不产PSP毒素的相关亚历山大藻（AC-1）为对象，研究了该藻株对褶皱臂尾轮虫、卤虫、黑褐新糠虾急性毒性效应和对糠虾的慢性毒性影响，同时对AFT毒素成分进行了研究，研究结果如下： 通过相关亚历山大藻（AC-1）对褶皱臂尾轮虫、卤虫、黑褐新糠虾的急性毒性影响研究，发现随着相关亚历山大藻（AC-1）密度的升高，轮虫、卤虫、糠虾的存活率逐渐降低，其96hLC50分别为：1500cells/ml，90cells/ml，5000cells/ml。比较研究三种生物对相关亚历山大藻（AC-1）敏感性可以看出，三种生物对该藻的敏感性顺序为：卤虫>轮虫>糠虾。 相关亚历山大藻（AC-1）对黑褐新糠虾的生长及种群繁殖有显著影响，我们发现在密度为50cells/ml藻液中，糠虾的繁殖就受到了不利影响。实验进行到63d结束时，糠虾日最高产虾数、总产幼虾数、总产虾天数都明显减少，初次产虾时间推迟，繁殖中断增加。且该藻对黑褐新糠虾亲虾的存活、生长也有一定的影响，糠虾亲虾的存活率为对照的71%，而体长和体重分别为对照组的87.3%和97.8%，但差异尚不显著（P>0.05）。 研究相关亚历山大藻（AC-1）各组分的毒性（藻液、藻细胞重悬液、藻细胞培养过滤液、内容物），发现藻液和藻细胞重悬液对褶皱臂尾轮虫种群数量及轮虫、卤虫、糠虾的存活率均有显著影响，表明相关亚历山大藻活体藻细胞的毒性最强。过滤液和内容物也显著降低了轮虫和卤虫的存活率，其对糠虾也有影响，但不显著，表明AFT毒素可能来源于细胞内，能分泌到细胞培养液中。 研究相关亚历山大藻（AC-1）AFT毒素的热稳定性、酸碱稳定性、去蛋白组分毒性、分子量范围、极性、多糖组分鉴定，表明AFT毒素为极性较强的多糖类物质，对热和酸碱是稳定的，其分子量范围在5K~50K之间。 以上结果表明相关亚历山大藻（AC-1）虽然不产生PSP毒素，但能产生极性多糖类毒素，对甲壳类等浮游动物的种群数量和资源补充产生不利影响。本研究为以后进一步研究AFT毒素的作用机制和毒素的化学结构奠定了基础，为全面评价亚历山大藻赤潮的危害提供了科学依据。