Quantitative vibration amplitude measurement with time-averaged digital speckle pattern interferometry

A new method of using time-averaged digital speckle pattern interferometry for the quantitative measurement of vibration amplitude was developed. Signal processing techniques especially the Hilbert transformation for quantitative evaluation of the Bessel fringes obtained in time-averaged digital speckle pattern interferometry were explored. The quadrature signal after Hilbert transformation is equivalent to a 90° phase-shifted interferogram for a monotonically increasing or decreasing phase function. An algorithm was developed for Bessel fringe contrast enhancement and phase extraction. The techniques were tested numerically and experimentally. Sub-fringe quantification of the time-averaged vibration fringes is realised with the proposed method. Compared with the commonly used phase shift method which requires a minimum of two images for image processing, this method requires only one fringe pattern for data extraction.     

A New Method for Rotation and Brightness Invariant Pattern Recognition Based on Modified Multiple Circular Harmonic Expansions

A new method for rotation and brightness invariant pattern recognition was proposed by applying multiple circular harmonic expansions to the joint transform correlator. The amplitudes of the multiple orders of circular harmonic expansions made from a detecting image were synthetically modified to respond to the same auto-correlation peaks. These modified circular harmonic expansions were arranged in the input plane as reference patterns together with an arbitrary target pattern, and the correlation signals between them were calculated in the subtracted joint transform correlator. The fraction of the correlation-peak intensities between the target and the references were extracted as a new discrimination parameter. This new parameter performs pattern recognition under rotation and brightness invariance with good discriminability. Its high discriminability has been proved in computer simulations using the face image patterns of many individuals.     

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Solitary wave solutions of nonlinear financial markets :data-modeling-concept-practicing

This paper seeks to solve the difficult nonlinear problem in financial markets on the complex system theory and the nonlinear dynamics principle, with the data-model-concept-practice issue-oriented reconstruction of the phase space by the high frequency trade data. In theory, we have achieved the differentiable manifold geometry configuration, discovered the Yang-Mills functional in financial markets, obtained a meaningful conserved quantity through corresponding space-time non-Abel localization gauge symmetry transformation, and derived the financial solitons, which shows that there is a strict symmetry between manifold fiber bundle and guage field in financial markets. In practical applications of financial markets, we have repeatedly carried out experimental tests in a fluctuant evolvement, directly simulating and validating the existence of solitons by researching the price fluctuations (society phenomena) using the same methods and criterion as in natural science and in actual trade to test the stock Guangzhou Proprietary and the futures Fuel Oil in China. The results demonstrate that the financial solitons discovered indicates that there is a kind of new substance and form of energy existing in financial trade markets, which likely indicates a new science paradigm in the economy and society domains beyond physics.      

小长径比垂直管气液两相流动特性分析

实验观察了小长径比垂直上流管内流型及特点,并对管入口处的压力波动特性和系统的压差波动特性进行了试验研究.结果表明:小长径比(L/D)垂直管内流型表现为泡状流、塞状流、乳沫状流、环状流和液束环状流;分别增加管线中的气量、液量,或者同时增加气液流量,均会造成垂直管入口处压力波动的均值和最大压力的增加;压力信号的概率密度(PDF)大部分呈双峰分布,也存在单峰和多峰分布;差压信号的概率密度符合正态分布,其功率谱密度同压力信号相比具有频率波动范围宽、幅值小的特点.     

关于由衍射片的背面反射光形成的衍射图样的讨论

在光的衍射实验中,我们看到的一般是由通过衍射片的直射光形成的衍射图样,但在衍射片的另一面,同样存在着另一个衍射图样,它是由衍射片的背面反射光形成的,两个衍射图样关于衍射片对称.本文给出了这种实验现象,并用巴比涅原理解释了这种现象.     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.     

双相干点光源干涉的一般分析

讨论了双相干点光源的三维空间干涉场的一般数学描述,分析了3个不同截面处的干涉强度的空间分布情况,并给出了相干相长点的计算机模拟图样.该结果也适用于其他类型波的相干叠加场分布.     

扫描隧道显微镜研究石墨表面的莫尔图

利用扫描隧道显微镜研究石墨表面的大尺度周期性图样．研究结果表明,莫尔图起源于石墨深层的缺陷,实验结果与理论完全吻合,并且第一次在实验上证明了纳米波可以穿透多层石墨而没有明显衰减．