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。     

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.     

湿地松对松针褐斑病的抗性测定

从松针褐斑病（Ｌｅｃａｎｏｓｔｉｃｔａ ａｃｉｃｏｌａ）重病林分中选出３６个湿地松（Ｐｉｎｕｓ ｅｌｌｉｏｔｔｉｉ）抗病表现型优树无性系，用人工喷洒病菌孢子液接种法对其进行抗病测定，结果表明，有２２个是高度抗病的。与用松针褐斑病产生的毒素粗提液处理上述无性系的离体针叶进行抗病性测定的结果基本一致，相关系数为０．８０。     

自级联式感应电动机控制系统的研制

描述了自级联式感应电动机的数学模型及其控制系统的结构,研制成功该类电机矢量控制系统样机.根据实验结果,分析了其动态和静态品质,得出此种类型电机控制系统完全可以达到与直流电动机控制系统相同性能的结论,提出了自级联式感应电动机进一步的应用前景.     

加热炉推钢机系统的振动分析

根据加热炉推钢机的工作原理和特点，对推钢机推力进行校核计算并分析了其影响因素，阐述了加热炉推钢机系统的振动问题，指出了生产振动的原因，提出了改进措施，对现场解决加热炉推钢机系统的振动问题具有一定的指导意义．     

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.     

基于稳健关联向量回归的黑液波美度软测量

针对造纸工业碱回收蒸发工段黑液波美度不易在线实时测量的现状,提出一种基于稳健关联向量回归的软测量方法,建立黑液波美度的软测量模型,实现黑液波美度的在线测量。稳健关联向量回归方法通过最大化一个建立在稳健子集上的似然函数得到模型的参数,同时保证回归的稳健性。构造了迭代算法来计算稳健关联向量回归,在优化超参数的同时,寻找稳健子集。实验结果表明,用该方法建立波美度软测量模型不仅是可行的和有效的,而且能够克服异常点的影响。     

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.     

基于v-支持向量机的边际电价预测及置信区间估计

引入v-支持向量机，通过构造和求解一个凸优化问题，同时实现了对边际电价的预测和对置信区间的估计，且无需假定预测偏差的概率分布．在v-支持向量回归中，当ε〉0时，v是错误样本的个数占总样本个数份额的上界．利用该性质，边际电价预测的置信度和置信区间可以很自然地用参数1-v和变量ε来表示，这为发电公司进行竞价方案的风险分析打下了很好的基础．算例仿真表明，该方法具有较好的泛化性能和较高的预测精度．