一种有效的四步施训模式

介绍一种在企业管理干部培训实施中的四步操作模式。通过对该模式整体思路和四个步骤操作要点的阐释,说明在对企业管理干部的培训实施过程中如何将学员的学习特征与现代培训理念、培训元素进行有机的结合,使培训效果真正落实到提高学员的能力上,以使其工作得到改善。     

基于BOOSt结构下不连续导电模式的PFC电路

采用现代高频功率变换技术的有源功率因数校正（Power Factor Corrector,PFC)技术是解决高频开关变换器谐波污染的有效手段。与传统的PFC电路相比，有源PFC电路的输入电流接近正弦波且与输与电压同相位，能有效抑制电流波形畸变和谐波，因此避免了对同一电网设施的干扰。在PFC电路中，Boost变换器是研究和应用得最多的一种变换器。本文着重分析了Boost电路在不连续导电模式状态下，PFC电路的临界条件，对实际电路结构的设计有很好的指导意义。     

The stress intensity factors for a short crack partially penetrating an inclusion of arbitrary shape

Some approximate solutions for predicting the stress intensity factor of a short crack penetrating an inclusion of arbitrary shape have been developed under mode I and mode II loading conditions. The derivation of the fundamental formula is based on the transformation toughening theory. The transformation strains in the inclusion are induced by the crack-tip field and remotely applied stresses, and approximately evaluated by the Eshelby equivalent inclusion theory. As validated by detailed finite element (FE) analyses, the developed solutions have good accuracy for different inclusion shape and for a wide range of modulus ratio between inclusion and matrix material.     

Passivity‐based sliding mode control for nonlinear systems

Passivity with sliding mode control for a class of nonlinear systems with and without unknown parameters is considered in this paper. In fact, a method for deriving a nonlinear system with external disturbances to a passive system is considered. Then a passive sliding mode control is designed corresponding to a given storage function. The passivity property guarantees the system stability while sliding mode control techniques assures the robustness of the proposed controller. When the system includes unknown parameters, an appropriate updated law is obtained so that the new transformed system is passive. The passivation property of linear systems with sliding mode is also analysed. The linear and nonlinear theories are applied to a simple pendulum model and the gravity‐flow/pipeline system, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty

Three applications of sampling-based sensitivity analysis in conjunction with evidence theory representations for epistemic uncertainty in model inputs are described: (i) an initial exploratory analysis to assess model behavior and provide insights for additional analysis; (ii) a stepwise analysis showing the incremental effects of uncertain variables on complementary cumulative belief functions and complementary cumulative plausibility functions; and (iii) a summary analysis showing a spectrum of variance-based sensitivity analysis results that derive from probability spaces that are consistent with the evidence space under consideration.     

松辽盆地南部岩性圈闭识别技术及效果

寻找隐蔽油气藏一直是油气勘探中的难题,我国东部吉林油田的中浅层已进入高成熟勘探阶段,油气勘探已由早期的构造圈闭勘探转变为现今的以隐蔽圈闭勘探为主的阶段。本文针对松辽盆地南部的西部前缘带三维连片工区及伊通地堑岔路河断陷,综合应用构造精细解释、声波曲线重构波阻抗反演、层序地层学、地震相及沉积(微)相分析、模型正演、地震属性分析、频谱分解及三维可视化等技术手段,所提供的多口探井获得工业油气流或有良好的油气显示,取得了较好的解释效果。     

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.     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.