A spatially constrained fuzzy hyper-prototype clustering algorithm

We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.     

Supervised restoration of degraded medical images using multiple-point geostatistics

Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.     

化选矿硫酸浸出稀土工艺研究

化选矿经硫酸浸出,在合适的酸度、温度、反应时间条件下,稀土浸出率为90%。     

Identification of Piecewise Linear Parameters of Regression Models of Non-Stationary Deterministic Systems

We consider the problem of identifying unknown nonstationary piecewise linear parameters for a linear regression model. A new algorithm is proposed that allows, in the case of a number of assumptions on the elements of the regressor, to provide an estimate of unknown non-stationary parameters. We analyze in detail the case with two unknown parameters, which makes it possible to understand the main idea of the proposed approach. We also consider a generalization to the case of an arbitrary number of parameters. We give an example of computer simulation that illustrates the efficiency of the proposed approach.     

Correction to: A comprehensive evaluation of availability and operational cost for a virtualized server system using stochastic reward nets

The Journal of Supercomputing - The section “Acknowledgement” was incorrect in the original article. The correct section “Acknowledgement” is given below.     

THE VALIDATION OF THE ROBUST INPUT ESTIMATION APPROACH TO TWO-DIMENSIONAL INVERSE HEAT CONDUCTION PROBLEMS

A novel adaptive and robust input estimation inverse methodology of estimating the time-varying unknown heat flux, named as the input, on the two active boundaries of a 2-D inverse heat conduction problem is presented. The algorithm includes using the Kalman filter to propose a regression model between the residual innovation and the two thermal unknown boundaries flux through given 2-D heat conduction state-space models and noisy measurement sequence. Based on this regression equation, a recursive least-square estimator (RLSE) weighted by the forgetting factor is proposed to on-line estimate these unknowns. The adaptive and robust weighting technique is essential since unknowns input are time-varied and have unpredictable changing status. In this article, we provide the bandwidth analysis together with bias and variance tests to construct an efficient and robust forgetting factor as the ratio between the standard deviation of measurement and observable bias innovation at each time step. Herein, the unknowns are robustly and adaptively estimated under the system involving measurement noise, process error, and unpredictable change status of time-varying unknowns. The capabilities of the proposed algorithm are demonstrated through the comparison with the conventional input estimation algorithm and validated by two benchmark performance tests in 2-D cases. Results show that the proposed algorithm not only exhibits superior robust capability but also enhances the estimation performance and highly facilitates practical implementation.     

复合热稳定剂对绝缘纸协同抗老化效应的分子模拟研究

为了从微观层面探究热稳定剂的复合添加对绝缘纸协同抗热老化特性的影响机理,以双氰胺、三聚氰胺为热稳定剂研究对象,对老化过程中的热稳定剂、β-D-吡喃葡萄糖分子、水分子和小分子酸等进行了量子化学计算,利用前线轨道理论考察了不同分子之间的化学反应活性。此外,采用分子动力学模拟软件,搭建了末添加、单一添加和复合添加热稳定剂的四种纤维素含水含酸模型,从纤维素与水分子、蚁酸分子的氢键结合数、相互作用能这两个方面考察了不同改性体系的差异及抗老化性能差异的机理。结果表明:热稳定剂分子与水分子、小分子酸的化学反应活性要强于β-D-吡喃葡萄糖分子与这些物质的反应活性;两种热稳定剂分子分别与水分子、小分子酸的反应活性存在差异,复合添加可以集中二者的优势,起到协同延缓绝缘纸老化的效果;双氰胺与三聚氰胺的复合添加在降低纤维素分子对水分子、蚁酸分子的吸附能力上存在协同作用。