直线式电机在有杆抽油设备中的应用

通过对游梁式抽油机的效率、启动特性与直线式电机驱动的有杆抽油机特性的对比，论述了直线抽油机的优越性及应用前景。     

膨化硝酸铵晶体特性研究

膨化硝酸铵是一种改性硝酸铵晶体,具有显著自敏化特征,成功应用于各类膨化硝铵炸药及震源药柱中.在膨化剂(表面活性剂)的作用下改善了膨化硝酸铵的物理性能和晶体结构及其特征.本文通过硝酸铵固体表面接触角的测量,算出了膨化硝酸晶体表面能;利用差热分析(DSC)技术给出了膨化剂对硝酸铵晶变的影响;采用x射线衍射分析了膨化硝酸铵晶体的晶格特性.     

一种联合信源信道匹配的视频传输方法

介绍了一种实现联合信源-信道编码的通用的新方法—联合信源-信道匹配。该方法思想巧妙,通过将有限的通信系统资源在信源和信道编码器之间进行巧妙合理的分配,从而获得最优的系统整体传输性能。并且,该方法并不仅局限于特定的信源-信道编码器对,可适用于任何一种信源-信道编码器对共享有限系统资源的情况,具有很好的通用性。仿真实验证明了JSCM通用视频传输系统能获得比传统的固定码率分配系统更高的整体性能增益。     

大长径比自旋弹箭横向自振特性的有限元计算方法与结果分析

本文研究大长径比自旋弹箭在飞行时横向振动的自振特性。本文将转子动力学应用于弹道学 ,采用有限元法 ,建立自旋弹箭横向振动自振特性方程。同时给出了计算方法。利用该方程和该方法 ,开发了相应的计算程序。通过数值计算 ,可给出细长旋转弹箭飞行时的进动转速和临界转速 ,以及相应的位移和角度振型。利用该程序 ,进行了实例计算和分析。     

A new iterative order reduction (IOR) method for eigensolutions of large structures

Order reduction is a computationally efficient method to estimate some lowest eigenvalues and the corresponding eigenvectors of large structural systems by reducing the order of the original model to a smaller one. But its accuracy is limited to a small range of frequencies that depends on the selection of the retained degrees of freedom. This paper proposes a new iterative order reduction (IOR) technique to obtain accurately the eigensolutions of large structural systems. The technique retains all the inertia terms associated with the removed degrees of freedom. This hence leads to the reduced mass matrix being in an iterated form and the reduced stiffness matrix constant. From these mass and stiffness matrices, the eigensolutions of the reduced system can be obtained iteratively. On convergence the reduced system reproduces the eigensolutions of the original structure. A proof of the convergence property is also presented. Applications of the method to a practical GARTEUR structure as well as a plate have demonstrated that the proposed method is comparable to the commonly used Subspace Iteration method in terms of numerical accuracy. Moreover, it has been found that the proposed method is computationally more efficient than the Subspace Iteration method. Copyright © 2003 John Wiley & Sons, Ltd.     

Elimination of organic matter present in wastewaters from the cork industry by membrane filtration

BACKGROUND: The first stage of the cork industrial process generates great volumes of wastewater with moderate to high organic pollutant content that must be purified using different procedures, such as filtration by membranes. RESULTS: The tangential filtration of these wastewaters was studied using two different laboratory equipments. In the first one, three ultrafiltration (UF) membranes were tested, with molecular weight cut‐off (MWCO) 100 kDa and 30 kDa, and two operating modes were used: total recycling of permeate and retentate streams, and in continuous mode, without recycling both streams. In the total recycling UF experiments, the influence of the operating variables on the permeate flux was first established. The effectiveness of the different membranes was determined by evaluating the rejection coefficients for several parameters that measure the global pollutant content of the effluent. The values found for these rejection coefficients were in the following order: ellagic acid and color > absorbance at 254 nm > tannic content > COD (chemical oxygen demand). In the continuous mode experiments, the fouling mechanism for each membrane was established by fitting the experimental data to various filtration fouling models given in the literature. The operating mode in the second equipment was batch concentration, and additional experiments were carried out with an UF membrane (2 kDa), and with a NF membrane (with MWCO in the range 150–300 Da). CONCLUSIONS: The three operating modes tested provided different rejection levels of organic matter; among them, the most effective procedure tested was batch concentration mode using a NF membrane. Copyright © 2007 Society of Chemical Industry     

新型刮刀钻头的设计及现场试验研究

在理论分析的基础上，综合PDC钻头和刮刀钻头的优点，设计了一种新型的刮刀钻头，并进行了现场试验。现场试验结果表明，该钻头运转平稳，采用低钻压、高转速，钻进速度快，井眼规则通畅，有较明显的防斜作用，该种钻头的推广应用对提高钻井工程的整体效益具有十分重要的意义。     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.     

Analysis of S-SEED''s characteristics in optical switch

This article introduces the basic structure of a symmetric self-electrooptic effect device （S-SEED）, and applies the Kirchoff＇ s current law and a purely equivalent capacitive model, to analyze S-SEED＇s switch characteristics. Linear approximation and N-segment approximation are utilized to obtain S-SEED＇s voltage-time （V-T） and characteristics. Theoretical analysis is verified by simulations, and the results demonstrate that the precision of S-SEED＇s switch time can satisfy the requirement in applications with linear approximation. Moreover, the simulations compare S-SEED＇s switch characteristics with different input powers and input contrast ratios, which reveal that increasing input contrast ratio is an effective way to improve S-SEED＇s switch characteristics.     

High training set size reduction by space partitioning and prototype abstraction

Instance-based learning methods like the nearest neighbour classifier generally suffer from the indiscriminate storage of all training instances, resulting in large memory requirements and slow execution speed. In this paper, new training set size reduction methods based on prototype generation and space partitioning are proposed. Experimental results show that the new algorithms achieve a very high reduction rate with still an important classification accuracy.