一种复杂背景下双层框架背景的建模方法

提出了一种经过改进的,适用于动态变化环境的背景建模和前景检测方法。该方法把背景分为动态层和静态层。利用能量描述子来分析动态层中的运动状态。同时,使用网格滤波器来减弱由于光照突变造成的影响。实验结果显示,对于包含光照突变和复杂背景的视频序列,该方法与3种经典的算法相比,有着更好的效果。     

二次规划问题的神经网络方法及其数值仿真

针对具有边界约束的凸二次规划问题,利用离散神经网络模型的建模原理,构造了一个新的离散投影神经网络模型.为了优化求解,运用李亚普诺夫稳定性理论,通过构造一个新的广义李亚普诺夫函数,得到了神经网络模型全局指数稳定的线性矩阵不等式条件.同时将结果的保守性与传统方法所得结果进行了比较.数值仿真表明,改进方法采用新条件,有求解方便,保守性弱的特点,具有较强的实用性.     

RGB-D Hand-Held Ob ject Recognition Based on Heterogeneous Feature Fusion

Ob ject recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying o...     

A solving method based on neural network for a class of multi-leader–follower games

In this paper, we present a new solving approach for a class of multi-leader–follower games. For the problem studied, we firstly propose a neural network model. Then, based on Lyapunov and LaSalle theories, we prove that the trajectory of the neural network model can converge to the equilibrium point, which corresponds to the Nash equilibrium of the problem studied. The numerical results show that the proposed neural network approach is feasible to the problem studied.

     

一种基于FPGA的实时NPR系统

在进行非真实感渲染(NPR)处理时需要大量的计算,这对高帧率、高分辨率的视频做实时的NPR渲染是一个难题。根据FPGA以并行运算为主的特点,对传统的NPR算法做了改进和简化,最终设计了一种基于FPGA的实时NPR系统。实验结果表明,该系统对高帧率、高分辨率的视频做实时NPR处理具有运算速度快、系统稳定等特点,同时还具有体积小、便于携带等优点。     

一种改进的分层路网的路径规划算法应用

为了提高路径规划效率,提出一种改进的分层路网的路径规划算法。首先,城市路网进行分层处理,以经典A*算法为核心,在高层路网上使用改进机制,评估函数做相应调整,然后,对其权值设置上下限阈值,提高算法的搜索精度及搜索效率。实验结果表明,规划的路径并非Dijkstra算法的最短,但是改进的算法使快速路段所占比例达90%以上,实际运行最优。     

基于Hyperworks的平衡轴支架拓扑优化设计

拓扑优化是一种根据载荷、约束及优化目标寻求结构材料最佳分配的优化方法,既可用于全新产品的概念设计,又可用于已有产品的改进设计;基于Hyperworks平台采用变密度法进行重卡平衡轴支架的拓扑优化设计,对优化后的重卡平衡轴支架进行有限元分析,将其应力分布、质量与原设计做比较;研究表明,经拓扑优化后的重卡平衡轴悬轴支架,应力大小大幅度减少,应力分布更加均匀,质量大幅度减少,实现了轻量化,为平衡轴支架结构的改进设计提供了重要的技术信息。     

The ring authenticated encryption scheme – How to provide a clue wisely

Though cryptography is being used more and more widely in reality, it seems that there exists no scheme or a concatenation of some existing schemes that could deal soundly with such practical situations as providing a clue, where the provider of the clue may want to reserve his beneficial rights while keeping his identity secret. To address this problem, inspired by the two notions of the ring signature and the authenticated encryption signature, we propose a new type of authenticated encryption scheme, which we call the ring authenticated encryption scheme, which can enable any member of a group of persons to provide a clue to some designated recipient wisely.     

一种统一的资源预留策略

资源预留可以保证任务在开始执行时获得资源,而这在网格这样资源动态性很强的环境中是很可贵的,因而资源预留是网格任务调度中一种普遍采用的策略。当前资源预留的研究主要集中在计算资源预留的情况,文中提出了一个网格环境中资源预留的统一策略,该策略对其它资源的预留和计算资源的预留一起加以考虑,经过模拟验证,该策略能够很好地和我们先前设计的一种基于资源预留的任务映射算法结合起来,适用于网格的动态、异构性环境。     

A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.