如何控制液压系统的噪声

本文对液压系统噪声产生的原因进行了分析，并相应地介绍了控制液压系统噪声的几项具体措施，指出了在实施噪声控制过程中应注意的事项。     

Simulating a Smartboard by Real-Time Gesture Detection in Lecture Videos

Gesture plays an important role for recognizing lecture activities in video content analysis. In this paper, we propose a real-time gesture detection algorithm by integrating cues from visual, speech and electronic slides. In contrast to the conventional “complete gesture” recognition, we emphasize detection by the prediction from “incomplete gesture”. Specifically, intentional gestures are predicted by the modified hidden Markov model (HMM) which can recognize incomplete gestures before the whole gesture paths are observed. The multimodal correspondence between speech and gesture is exploited to increase the accuracy and responsiveness of gesture detection. In lecture presentation, this algorithm enables the on-the-fly editing of lecture slides by simulating appropriate camera motion to highlight the intention and flow of lecturing. We develop a real-time application, namely simulated smartboard, and demonstrate the feasibility of our prediction algorithm using hand gesture and laser pen with simple setup without involving expensive hardware.      

带压垫片密封技术在我厂的应用

介绍了在化工生产中，对大的地漏点（大径法兰、管道等）应用不停车带压密封技术进行堵漏的原理及过程。     

ENR／CSM的自硫化研究

研究在没有任何硫化剂的情况下环氧化天然橡胶（ＥＮＲ－５０）和氯磺化聚乙烯（ＣＳＭ－４０）共混物的自硫化，以及共混比、填料品种及用量、间苯撑双马来酰亚胺与马来酸酐等活性剂、硫化温度和时间对交联程度和性能的影响。结果表明ＥＮＲ／ＣＳＭ共混体可以产生自硫化；共混比为５０／５０时交联密度最大，性能最佳，炭黑和白炭黑对该体系有好的补强作用，但白炭黑的补强作用比炭黑的好；间苯撑双马来酰亚胺及马来酸酐能活化自硫化体系；延长硫化时间及提高硫化温度均可增加交联作用，该自硫化体系没有返原现象。     

Incremental linear discriminant analysis for face recognition.

Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis (LDA) is one of the popular supervised dimensionality reduction methods, and many LDA-based face recognition algorithms/systems have been reported in the last decade. However, the LDA-based face recognition systems suffer from the scalability problem. To overcome this limitation, an incremental approach is a natural solution. The main difficulty in developing the incremental LDA (ILDA) is to handle the inverse of the within-class scatter matrix. In this paper, based on the generalized singular value decomposition LDA (LDA/GSVD), we develop a new ILDA algorithm called GSVD-ILDA. Different from the existing techniques in which the new projection matrix is found in a restricted subspace, the proposed GSVD-ILDA determines the projection matrix in full space. Extensive experiments are performed to compare the proposed GSVD-ILDA with the LDA/GSVD as well as the existing ILDA methods using the face recognition technology face database and the Carneggie Mellon University Pose, Illumination, and Expression face database. Experimental results show that the proposed GSVD-ILDA algorithm gives the same performance as the LDA/GSVD with much smaller computational complexity. The experimental results also show that the proposed GSVD-ILDA gives better classification performance than the other recently proposed ILDA algorithms.     

Measurement of binocular disparity in polarized based stereoscopic display by using digital camera

This work presents an image displacement measurement by using a box pattern and the edge spread function analysis in the polarized based stereoscopic displays. The measurement determines to identify three factors (i.e., box size, image displacement, and spatial frequency response (SFR)) from the box patterns. The polarized based stereoscopic displays might sacrifice pixels for producing 3D images, which might produce errors in the image displacement and then affect the depth perception indirectly. Based on the measurement, the errors can be quantified by using the three factors. Owing to the light leakage distorting the edge spread function to obtain an inferior SFR, exactly how light leakage affects the edge spread function can be resolved on the basis of the SFR results, especially at a frequency (1/display pixel) of 1.9 in the case of the horizontal displacement. Light leakage also induces the error of the box height in even and odd intervals of the vertical displacements more than 2 pixels and 0.5 pixels, respectively. The image displacement measurement provides a simple and comprehensive means of evaluating the optical characteristics, disparity, and sharpness of the stereoscopic displays, via the three factors.     

Orientation of graphitic planes during annealing of “dip deposited” amorphous carbon film: A carbon K-edge X-ray absorption near-edge study

Annealing effect of amorphous carbon thin films on Si(1 0 0) substrates is studied by normal incidence and angle dependent carbon K-edge X-ray absorption near-edge structure (XANES) spectroscopy. The angle dependence of the XANES signal shows that the graphitic basal planes are oriented perpendicular to the surface when the film is annealed at 1000 °C. Micro-Raman spectroscopy reveals two well-separated bands the D band at 1355 cm⁻¹ and G band at ∼1600 cm⁻¹, and their I_D/I_G intensity ratio indicates the formation of more graphitic film at higher annealing temperatures. X-ray diffraction pattern of 1000 °C temperature annealed film confirms the formation of graphite structure.     

Learning low-rank Mercer kernels with fast-decaying spectrum

Low-rank representations have received a lot of interest in the application of kernel-based methods. However, these methods made an assumption that the spectrum of the Gaussian or polynomial kernels decays rapidly. This is not always true and its violation may result in performance degradation. In this paper, we propose an effective technique for learning low-rank Mercer kernels (LMK) with fast-decaying spectrum. What distinguishes our kernels from other classical kernels (Gaussian and polynomial kernels) is that the proposed always yields low-rank Gram matrices whose spectrum decays rapidly, no matter what distribution the data are. Furthermore, the LMK can control the decay rate. Thus, our kernels can prevent performance degradation while using the low-rank approximations. Our algorithm has favorable in scalability—it is linear in the number of data points and quadratic in the rank of the Gram matrix. Empirical results demonstrate that the proposed method learns fast-decaying spectrum and significantly improves the performance.     

Face representation using independent component analysis

This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT AI Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.     

Shape estimation from topographic primal sketch

In this paper, a technique for estimating object shape from topographic primal sketch is investigated. Given a gray tone image of a three-dimensional object, a topographic labeling of the image indicates the peaks and pits, ridges and valleys, and flats and hillsides of the underlying, continuous, gray tone surface. The patterns of these topographic labels capture information about the original three-dimensional object in the scene and about the illumination. In order to determine if estimation of three-dimensional shape from a topographic labeling is feasible, we have both analytically and experimentally determined the topographic labelings for images of some mathematically generated surfaces with varied directions of illumination. Our results indicate that such patterns do exist and will be useful in determining three-dimensional shape from two-dimensional images. A scheme for partial classification of three-dimensional object surface is proposed. Preliminary results are illustrated.