激光快速成形Rene′88DT高温合金的时效强化研究

研究了时效时间对激光快速成形后时效硬化镍基高温合金Rene′88DT中沉淀相组织演化和力学性能的影响。发现随时效时间的增加，沉淀相y’略有长大，颗粒之间发生大颗粒吞噬小颗粒的粗化，时效40h后沉淀相颗粒粗化明显。对经不同时间时效后的激光快速成形Rene′88DT试样进行了硬度测试。结果显示，在时效时间为28h时，硬度有一峰值HRC49。在此基础上，采用1165℃，2h固溶、空冷＋760℃，28h时效的热处理工艺对激光快速成形标准拉伸棒样进行了热处理，拉伸结果显示成形件的抗拉强度较激光沉积态提高了400MPa，屈服强度接近同种材料的粉末锻造水平。     

Multifocus image fusion method of Ripplet transform based on cycle spinning

The curvelet transform can represent images at both different scales and different directions. Ripplet transform, as a higher dimensional generalization of the curvelet transform, provides a new tight frame with sparse representation for images with discontinuities along C² curves. However, the ripplet transform is lack of translation invariance, which causes the pseudo-Gibbs phenomenon on the edges of image. In this paper, the cycle spinning method is adopted to suppress the pseudo-Gibbs phenomena in the multifocus image fusion. On the other hand, a modified sum-modified-laplacian rule based on the threshold is proposed to make the decision map to select the ripplet coefficient. Several experiments are executed to compare the presented approach with other methods based on the curvelet, sharp frequency localized contourlet transform and shearlet transform. The experiments demonstrate that the presented fusion algorithm outperforms these image fusion works.     

Leader-following consensus for uncertain second-order nonlinear multi-agent systems

This paper studies the leader-following consensus problem for a class of second-order nonlinear multi-agent systems subject to linearly parameterized uncertainty and disturbance. The problem is solved by integrating the adaptive control technique and the adaptive distributed observer method. The design procedure is illustrated by an example with a group of Van der Pol oscillators as the followers and a harmonic system as the leader.     

随机森林理论浅析

随机森林是一种著名的集成学习方法,被广泛应用于数据分类和非参数回归。本文对随机森林算法的主要理论进行阐述,包括随机森林收敛定理、泛化误差界以和袋外估计三个部分。最后介绍一种属性加权子空间抽样的随机森林改进算法,用于解决超高维数据的分类问题。     

Extension of the gap statistics index to fuzzy clustering

The well-known gap statistic index proposed by Tibshirani et al. has successfully applied in many clustering evaluations. However, the gap statistic index cannot evaluate the clustering partitions from any fuzzy clustering algorithm. This is because fuzzy clustering cannot provide the within-cluster similarity measure that is used in the gas statistic index. Thus, the applicable range of the gap statistic index is very limited. In this paper, we present a new method that extends the gap statistic index to fuzzy clustering by using fuzzy membership notations. Our proposed method can extend the applicability of the gap statistic index, and outperform other existing fuzzy indices in several aspects. Experiments on eight sets of synthetic and real datasets are used to verify the applicability and efficiency of the proposed method.