基于机器视觉的离散傅里叶变换目标识别方法

提出了一种基于机器视觉与离散傅里叶变换的目标特征识别方法。利用计算机图像技术采集和处理图像信号;利用离散的傅里叶变换对图像数据提取特征,能够更好的辨别数据细节,从而可通过图像的比对来实现目标的识别。该方法在对实际的静止图像进行处理与计算后,能够很好的对图像的细节变化进行识别。     

自由曲面光学虚拟制造与检测系统的探讨

自由曲面光学产品设计、制造与检测的工艺流程，通常采取试凑法逐次逼近。由于加工 检测 再加工，循环往复，既费时，成本又高，产生了瓶颈问题。为了解决此弊端，本文运用虚拟制造技术，提出光学虚拟制造的基本构想，即虚拟制造系统结构模型，给出光学系统虚拟原型的构成和光学系统成像质量虚拟检测系统的构成，讨论光学成像质量的仿真检测以及敏度分析方法。研究结果表明：运用虚拟制造与检测技术，可缩短研发周期，降低成本，优化工艺并提高产品质量。     

基于SVM局部后验概率融合方法研究

研究了基于支持向量机的后验概率的应用,提出了对样本集进行分解,以产生局部后验概率,根据模式的稳健性对局部后验概率进行凸组合融合的方法.     

Design and Experimental Characterization of Low‐Volume PM10/2.5/1.0 Trichotomous Sampler Inlet

This paper presents the development and laboratory evaluation of a PM10/2.5/1.0 trichotomous sampling inlet that consists of two main parts: a previously designed PM10 size‐selective inlet part and a PM2.5/1.0 two‐stage virtual impactor, which was newly fabricated and attached serially to the PM10 size selective inlet part. Particles are collected in three locations through the trichotomous sampling inlet to provide for not only particle concentration measurements of PM10, PM2.5 and PM1.0, but also those of PM2.5–10 and PM1.0–2.5.     

激光二极管自混合干涉和微振动的实验观测

介绍了利用LabView软件虚拟示波器和信号源,对激光二极管自混合干涉和微振动进行的实验观测.该实验可以作为本科低年级学生的综合设计实验.     

Support vector machine via nonlinear rescaling method

In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see [Polyak in Math Program 54:177–222, 1992; Polyak in Math Program Ser A 92:197–235, 2002; Polyak and Teboulle in Math Program 76:265–284, 1997] and references therein). The formulation of the linear SVM based on the NR method leads to an algorithm which reduces the number of support vectors without compromising the classification performance compared to the linear soft-margin SVM formulation. The NR algorithm computes both the primal and the dual approximation at each step. The dual variables associated with the given data-set provide important information about each data point and play the key role in selecting the set of support vectors. Experimental results on ten benchmark classification problems show that the NR formulation is feasible. The quality of discrimination, in most instances, is comparable to the linear soft-margin SVM while the number of support vectors in several instances were substantially reduced.     

虚拟技术在实验教学中的应用

介绍了虚拟技术在实验教学改革中的作用与意义，探讨了电子信息系统实验教学改革的新思路与实现措施，并总结了实验教学改革的初步成果．     

Minkowski-type and Alexandrov-Type Theorems for Polyhedral Herissons

Classical H. Minkowski theorems on existence and uniqueness of convex polyhedra with prescribed directions and areas of faces as well as the well-known generalization of H. Minkowski uniqueness theorem due to A.D. Alexandrov are extended to a class of nonconvex polyhedra which are called polyhedral herissons and may be described as polyhedra with injective spherical image.     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.     

A study on the applications of the acoustic design sensitivity analysis of vibrating bodies

The determination of the sensitivity of the acoustical characteristics of vibrating systems with respect to the variation of the design parameters predicting these characteristics is a necessary and important step of the acoustic design and optimization process. Acoustic design sensitivity analysis includes the computation and evaluation of the sensitivity information required for this procedure. In this study, a boundary element code performing the sensitivity analysis of the acoustic pressure by using the matrix sensitivities with respect to different design variables has been developed. The effect of the precision of boundary element discretization on the acoustic pressure sensitivity is examined via this code. The formulation is applied to a multi-source system and the dimension sensitivity analysis of near field pressures of two-dilating-spherical source is performed. The last application is devoted to a real sound source: a washing machine sitting on the floor. Sensitivity of the field pressures to the machine’s dimensions (size), surface velocity and frequency is examined on the bases of the boundary element model of the machine and half-space condition. The impacts of these variables are compared; and a limiting speed for the machine responding both the acoustical and operational requirements is determined.