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。     

基于Karhunen-Loève变换和小波谱特征矢量量化的三维谱像数据压缩

提出了基于Karhunen Lo埁ｖｅ变换的小波谱特征矢量量化三维谱像数据压缩方法耍幔颍瑁酰睿澹?Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化方法应用了Karhunen Lo埁ｖｅ变换的消除谱相关性优良性能 ,应用二维小波变换消除空间相关性 ,在小波变换域内应用二维集分割嵌入块编码和一维谱特征矢量量化对三维谱像数据压缩 ,获得较高的压缩性能。实验结果表明 :Karhunen Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化编码比Karhunen Lo埁ｖｅ变换 /小波变换 /改进对块零树编码和Karhunen Lo埁ｖｅ变换 /小波变换 /快速矢量量化编码方法在同样压缩比条件下 ,峰值信噪比提高 2dB和 1dB以上 ,而速度提高了 1.5和 8倍 ,整体压缩性能有较大的提高     

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.     

广义向量均衡问题的解与解集的凸性

引进集值映射的锥真拟凹概念，讨论一类具有集值映射的广义向量衡问题解的存在性与解集的凸性。     

Monotone Mappings and Flows of Viscous Media

We establish solvability of the boundary-value problems describing stationary and periodic flows of viscoplastic media. In the case of stationary flows we study the question of convergence of the Galerkin method. For the problem of periodic flows we prove a version of the second Bogolyubov theorem.     

Proper Efficiency in Vector Optimization on Real Linear Spaces

In this paper, we use an algebraic type of closure, which is called vector closure, and through it we introduce some adaptations to the proper efficiency in the sense of Hurwicz, Benson, and Borwein in real linear spaces without any particular topology. Scalarization, multiplier rules, and saddle-point theorems are obtained in order to characterize the proper efficiency in vector optimization with and without constraints. The usual convexlikeness concepts used in such theorems are weakened through the vector closure.     

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．     

基于拉格朗日插值的签名方案的研究

提出基于拉格朗日插值的签名方案存在小数位问题,并通过引入向量计算机制以消除数字签名中的小数位问题,最后对其进行了证明.     

用于高数值孔径物镜的可调光瞳滤波器

根据矢量衍射理论，提出了一种用于提高高数值孔径显微物镜纵向分辨率的可调二元环形光瞳滤波器，通过改变滤波器的环半径获得不同的纵向分辨率，并利用共焦显微术点扩散函数的乘积性大大地降低了旁瓣相对主瓣的比值。模拟结果表明，这种新型滤波器不仅可以提高高数值孔径物镜的纵向分辨率，同时基本上不影响横向分辨率。