基于Kalman滤波和模板匹配的目标跟踪技术研究

针对模板匹配算法计算量大,难以解决目标形变、光照变化、遮挡等情况下的跟踪问题,提出了一种结合Kalman滤波的模板匹配跟踪算法。通过Kalman滤波预测下一帧图像中目标的位置,缩小目标搜索范围,以减少模板匹配计算量,满足目标跟踪的实时要求。采取自适应模板更新策略,根据目标的变化情况更新模板。在目标被短暂遮挡时,采用Kalman滤波预测目标的状态,继续稳定跟踪。实验结果表明,算法明显优于单独使用模板匹配算法,当目标被遮挡时仍可以稳定、实时跟踪,具有良好的鲁棒性。     

Differences of crystal structure and dynamics between a soft porous nanocrystal and a bulk crystal

We have demonstrated downsizing effects of the soft porous crystal, [Zn(isophthalate)(4,4'-bipyridyl)](n) (CID-1), on the adsorption behavior between CID-1 and CID-1 nanocrystal (NCID-1). The difference results from the packing crystal structures and the dynamics of the frameworks.     

斜拉桥拉索-阻尼器系统非线性响应分析

考虑索的抗弯刚度、垂度及几何非线性的影响,得出了索一阻尼器系统的空间非线性振动偏微分方程,用中心差分法将微分方程在空间内离散,导出了系统的非线性振动常微分方程组。结合Newmark法及虚拟力法提出了一种用于求解非线性振动瞬态响应的杂交分析算法。并以典型的斜拉桥拉索为研究对象,给出了数值算例,并与Runge—Kutta直接积分法进行了比较,说明了杂交算法的准确性及有效性。     

Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction

This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.     

Long-range ordered magnet of a charge-transfer Ru2(4+)/TCNQ two-dimensional network compound

Two isostructural 2D assemblies composed of [Ru2(O2CCF3)4] and TCNQ or TCNQF4 in a 2:1 ratio were synthesized: [{Ru2(O2CCF3)4}2TCNQ].3(p-xylene) (1b) and [{Ru2(O2CCF3)4}2TCNQF4].3(p-xylene) (2). In 1b, the TCNQ moiety is assigned to be neutral because of no significant electron transfer from the Ru24+ units. Therefore, the magnetic property of 1b can be understood to be a paramagnetic nature of the isolated Ru24+ unit, while, a fully charge-transfer from the Ru2 units to the TCNQF4 molecules in 2 leads to charge delocalization spreading over the 2D network and allows a long-range magnetic order to occur. On the basis of an idea of the resonance scheme in charge-transfer D2A network systems, the first successful example of a charge-transferred magnet in the Ru2-TCNQ system was rationally designed by tuning redox between [Ru2]4+ and the TCNQ molecule changing from TCNQ to TCNQF4.     

Coordination-network-based ionic plastic crystal for anhydrous proton conductivity

An ionic coordination network consisting of protonated imidazole and anionic one-dimensional chains of Zn(2+) phosphate was synthesized. The compound possesses highly mobile ions in the crystal lattice and behaves as an ionic plastic crystal. The dynamic behavior provides a proton conductivity of 2.6 × 10(-4) S cm(-1) at 130 °C without humidity.