Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems

An approach to dealing with missing data, both during the design and normal operation of a neuro-fuzzy classifier is presented in this paper. Missing values are processed within a general fuzzy min–max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes. An emphasis is put on ways of quantifying the uncertainty which missing data might have caused. This takes a form of classification procedure whose primary objective is the reduction of a number of viable alternatives rather than attempting to produce one winning class without supporting evidence. If required, the ways of selecting the most probable class among the viable alternatives found during the primary classification step, which are based on utilising the data frequency information, are also proposed. The reliability of the classification and the completeness of information is communicated by producing upper and lower classification membership values similar in essence to plausibility and belief measures to be found in the theory of evidence or possibility and necessity values to be found in the fuzzy sets theory. Similarities and differences between the proposed method and various fuzzy, neuro-fuzzy and probabilistic algorithms are also discussed. A number of simulation results for well-known data sets are provided in order to illustrate the properties and performance of the proposed approach.     

Counting unrooted maps on the plane

A planar map is a 2-cell embedding of a connected planar graph, loops and parallel edges allowed, on the sphere. A plane map is a planar map with a distinguished outside (“infinite”) face. An unrooted map is an equivalence class of maps under orientation-preserving homeomorphism, and a rooted map is a map with a distinguished oriented edge. Previously we obtained formulae for the number of unrooted planar n-edge maps of various classes, including all maps, non-separable maps, eulerian maps and loopless maps. In this article, using the same technique we obtain closed formulae for counting unrooted plane maps of all these classes and their duals. The corresponding formulae for rooted maps are known to be all sum-free; the formulae that we obtain for unrooted maps contain only a sum over the divisors of n. We count also unrooted two-vertex plane maps.     

基于小波的光照补偿及其在人脸识别中的应用

提出了一种基于小波分析的人脸图像光照补偿技术,通过舍弃图像中的低频分量,仅用高频分量进行图像重构,便可较好地消除原始人脸图像中光照的影响.并将该技术应用于特征脸法人脸识别,在Yale库和自建库图像中,实验测得在较大尺度的情况下,图像识别率都有明显提高.     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

An improved exponential transformation for nearly singular boundary element integrals in elasticity problems

This paper presents an improved exponential transformation for nearly singular boundary element integrals in elasticity problems. The new transformation is less sensitive to the position of the projection point compared with the original transformation. In our work, the conventional distance function is modified into a new form in the polar coordinate system. Based on the refined distance function, an improved exponential transformation is proposed in the polar coordinate system. Moreover, to perform integrations on irregular elements, an adaptive integration scheme considering both the element shape and the projection point associated with the improved transformation is proposed. Furthermore, when the projection point is located outside the integration element, another nearest point is introduced to subdivide the integration elements into triangular or quadrilateral patches of fine shapes. Numerical examples are presented to verify the proposed method. Results demonstrate the accuracy and efficiency of our method.     

Multivariate linear regression with missing values

This contribution presents and discusses an efficient algorithm for multivariate linear regression analysis of data sets with missing values. The algorithm is based on the insight that multivariate linear regression can be formulated as a set of individual univariate linear regressions. All available information is used and the calculations are explicit. The only restriction is that the independent variable matrix has to be non-singular. There is no need for imputation of interpolated or otherwise guessed values which require subsequent iterative refinement.     

一种改进的AUVs变结构滑模控制策略研究

针对现有的变结构滑模控制切换面单一性会导致系统状态在原点处收敛缓慢的缺陷,对自主式水下机器人(AUVs)变结构滑模控制设计了折线型的切换面,并且进一步改进实现了速度控制和定点定速控制。改进的切换面由斜率不同的自线段构成,并在拐点处通过S型函数光滑过渡。将切换面的斜率减小到0时,则可以实现速度控制,同时为了消除速度控制的稳态误差,引进了采用能智积分方法的积分项。最后在某AUV上进行仿真实验,结果证实了应用折线型切换面可以减少控制系统的上升时间,提高反应速度;速度控制的稳态误差接近0,并很好地实现定点定速的控制效果。该方法可以有效用于AUVs的控制。     

TiO_2光催化剂的形貌与晶面调控

二氧化钛(TiO2)具有化学稳定性高、无毒、价格低廉、来源广泛及光电性能优异等优点,被广泛应用于太阳能电池和光催化等领域,尤其是在污染物的光催化降解方面,可很好地解决当前的环境污染问题。但一方面受带隙宽度限制,使其对太阳光的利用率不足5%,不能充分利用太阳光中的可见光;另一方面由于光生电子-空穴容易结合,催化效率低,从而使TiO2的实际应用受到限制。因此必须采取合适的措施,一方面要增强TiO2对可见光的吸收,提高对太阳光的利用率;另一方面要抑制光生电子-空穴的复合,提高光催化效率。目前越来越多的科学家通过控制TiO2的形貌、晶型、特殊晶面暴露等手段来提高TiO2光生电子-空穴的传输速率和光电转换效率。本文主要综述了近年来在TiO2光催化剂的特殊形貌和特殊晶面暴露等方面的研究进展,对未来的研究和发展方向作了展望。