融合多颜色空间分量的自适应彩色图像分割

提出了一种新的简单有效的融合多颜色分量的分割方法,首先在六个不同的颜色空间中选择最佳的待分割颜色分量,然后应用直方图和空间模糊C均值（SFCM）技术对不同颜色分量进行自适应初始分割,最后融合分割结果并进行区域合并。利用该算法在Berkeley图像库上进行了大量实验,实验结果表明,与当前一些经典分割算法Mean-shift、FCR、CTM等相比,利用该方法能够获得更好的分割结果以及更优的性能指标。     

多标记分类和标记相关性的联合学习

提出了多标记分类和标记相关性的联合学习(JMLLC),在JMLLC中,构建了基于类别标记变量的有向条件依赖网络,这样不仅使得标记分类器之间可以联合学习,从而增强各个标记分类器的学习效果,而且标记分类器和标记相关性可以联合学习,从而使得学习得到的标记相关性更为准确.通过采用两种不同的损失函数:logistic回归和最小二乘,分别提出了JMLLC-LR(JMLLC with logistic regression)和JMLLC-LS(JMLLC with least squares),并都拓展到再生核希尔伯特空间中.最后采用交替求解的方法求解JMLLC-LR和JMLLC-LS.在20个基准数据集上基于5种不同的评价准则的实验结果表明,JMLLC优于已提出的多标记学习算法.     

基于相关向量机的MEMS陀螺仪随机漂移补偿

提出了一种基于相关向量机的MEMS陀螺仪随机漂移预测方法。针对MEMS陀螺仪随机漂移误差的非线性、不确定性等特点,建立了相关向量机预测模型,并采用EM算法获得模型的参数。针对随机漂移的混沌特性,利用相空间重构技术,将重构后的漂移序列作为输入变量进行模型的训练和预测。训练和测试结果均表明,该方法具有很好的预测效果,优于常用的时间序列分析法和支持向量机法。利用预测结果对随机漂移进行补偿,有效地提高了陀螺的使用精度。     

Multimodel analysis and controller design for nonlinear processes

Multimodel analysis and controller design for nonlinear processes via gap metric is discussed. It is shown that the loop-shaping H_∞ approach can integrate the procedure of selecting operating points and the local controller design. The local controllers can guarantee not only stability but also performance specified by the pre- and/or post-compensators. Thus, at each operating points, local controllers can have similar performance, and the global performance of the system can be predicted.     

论环境陶艺空间的要素特性

环境陶艺空间有三大要素特征：其一，思维空间在环境陶艺空间中的特性；其二，环境空间在环境陶艺空间中的特性；其三．陶艺作品空间与心理空间在环境陶艺空间中的特性。通过以上分析，可以看出环境陶艺空间各要素之间相互作用、具有相互依存的空间系统化的关系。     

质量交换网络集成的状态空间法

State space approach is an effective method to mass-exchange network (MEN) synthesis. By decomposing the network into two interactive parts, a distribution network and a process operator, the synthesis problem can be formulated into a mixed integer nonlinear programming (MINLP) model. In this article, a generalized state space model based on typical MEN is established and verified in two cases. A new asymmetrical operator and cost index are also adopted to speed up the solution process. The results demonstrate the efficiency of the proposed approach.     

State space approximation for general fractional order dynamic systems

Approximations for general fractional order dynamic systems are of much theoretical and practical interest. In this paper, a new approximate method for fractional order integrator is proposed. The poles of the approximate model are unrelated to the order of integrator. This feature shows benefits on extending the algorithm to the systems containing various fractional orders. Then a unified approximate method is derived for general fractional order linear or nonlinear dynamic systems via combining the proposed new method with the distributed frequency model approach. Numerical examples are given to show the wide applicability of our method and to illustrate the acceptable accuracy for approximations as well.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Implementation-aware selection of the custom instruction set for extensible processors

This paper presents an approach for incorporating the effect of various logic synthesis options and logic level implementations into the custom instruction (CI) selection for extensible processors. This effect translates into the availability of a piecewise continuous spectrum of delay versus area choices for each CI, which in turn influences the selection of the CI set that maximizes the speedup per area cost (SPA) metric. The effectiveness of the proposed approach is evaluated by applying it to several benchmarks and comparing the results with those of a conventional technique. We also apply the methodology to the existing serialization algorithms aimed at relaxing register file constraints in multi-cycle custom instruction design. The comparison shows considerable improvements in the speedup per area compared to the custom instruction selection algorithms under the same area-budget constraint.     

A Projective Framework for Polyhedral Mesh Modelling

We present a novel framework for polyhedral mesh editing with face‐based projective maps that preserves planarity by definition. Such meshes are essential in the field of architectural design and rationalization. By using homogeneous coordinates to describe vertices, we can parametrize the entire shape space of planar‐preserving deformations with bilinear equations. The generality of this space allows for polyhedral geometric processing methods to be conducted with ease. We demonstrate its usefulness in planar‐quadrilateral mesh subdivision, a resulting multi‐resolution editing algorithm, and novel shape‐space exploration with prescribed transformations. Furthermore, we show that our shape space is a discretization of a continuous space of conjugate‐preserving projective transformation fields on surfaces. Our shape space directly addresses planar‐quad meshes, on which we put a focus, and we further show that our framework naturally extends to meshes with faces of more than four vertices as well.