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。     

Terrain Modelling from Feature Primitives

We introduce a compact hierarchical procedural model that combines feature‐based primitives to describe complex terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain elevation function by using a construction tree. Leaves represent terrain features and they are generic parametrized skeletal primitives, such as mountains, ridges, valleys, rivers, lakes or roads. Inner nodes combine the leaves and subtrees by carving, blending or warping operators. The elevation of the terrain at a given point is evaluated by traversing the tree and by combining the contributions of the primitives. The definition of the tree leaves and operators guarantees that the resulting elevation function is Lipschitz, which speeds up the sphere tracing used to render the terrain. Our model is compact and allows for the creation of large terrains with a high level o detail using a reduced set of primitives. We show the creation of different kinds of landscapes and demonstrate that our model allows to efficiently control the shape and distribution of landform features.     

Signal-adaptive discrete evolutionary transform as a sparse time–frequency representation

Discrete evolutionary transform (DET) has usually been applied to signals in a blind-way without using any parameters to characterize the signal. For this reason, it is not optimal and needs improvement by using some information about the signal. In this paper, we propose an improvement for the discrete evolutionary transform in order to obtain a sparse representation and redefine the generalized time-bandwidth product optimal short-time Fourier transform as a special case of it. In case of linear FM-type signals, the optimized kernel function of the transform is determined according to signal parameters including the instantaneous frequency. The performance of the adaptive-DET is illustrated on three distinct cases. In case of multi-component LFM signals, when the concentration of the proposed distribution is compared to the ordinary sinusoidal-DET, the improvement is computed as 28% in terms of the ratio of norms. Furthermore we define a new and a general class of distribution functions named as the short-time generalized discrete Fourier transform (ST-GDFT) which is a larger set of signal representations including the adaptive-DET.     

分层特征计算和错误控制的层次分类方法

中文新闻信息分类标准中,类别数量大。在将其应用于新闻分类时,会出现训练模型大、训练时间长,尤其是当部分类别改变时需要全部重新训练等问题。由于分类标准中类别之间存在层次关系,因此层次分类方法可以作为解决方案。研究层次化的中文新闻分类方法,并从以下两方面改善层次化分类方法的效果:1)分层的新闻特征计算,解决了层次分类中新闻在分类类别下的特征向量的不同表示的问题;2)错误控制,解决了在上一层分类错误的情况下新闻不会分到正确的类别上的情况。实验结果表明,层次分类方法的效果比平面分类的准确度提高了约4%,进行多次特征权重计算的层次分类方法比普通的层次分类的准确度提高了约3%,同时进行错误控制的分类效果比普通层次的分类效果提高了大概3%。     

基于分层象限空间的P2P超级节点查找技术

在目前的非结构化超级节点查找中,采用简单的泛洪或随机游走消息传递方式,效率不高,同时系统没有将非结构模糊查询和结构化数据定位方式有效地结合。提出一种基于分层象限空间的新型超级节点结构Quad,并在Quad上实现了两种非结构化超级节点查找方法:一是回溯扩展查找方法,该方法将泛洪和随机游走方式进行折衷,兼顾了网络流量和查询长度;二是利用Bloom Filter技术对回溯扩展查找进行改进。此外,Quad同时支持结构化数据定位。模拟结果表明,Quad查找在查询成功率、查找成本方面优于现有的超级节点查找方法。Bloom Filter能有效提高Quad查询效率并降低查找长度,同时Quad具有高效的定位性能。     

无线传感器网络分簇路由协议的改进研究

针对无线传感器网络(WSN)中分簇路由协议LEACH算法中簇首分配不均以及簇首与Sink节点直接通信问题进行研究,提出一种基于LEACH成簇思想的分簇路由协议。该算法基于节点剩余能耗和已担任簇首时间选举簇头。簇头间采用贪婪算法形成一条链,在该链中又选出一个簇头,将整个网络的数据融合后转发给基站。MatLab仿真结果显示,改进后的算法在均衡网络节点能耗和网络的生存时间上比LEACH算法有很大提高。     

Particle Level Set Advection for the Interactive Visualization of Unsteady 3D Flow

Typically, flow volumes are visualized by defining their boundary as iso‐surface of a level set function. Grid‐based level sets offer a good global representation but suffer from numerical diffusion of surface detail, whereas particle‐based methods preserve details more accurately but introduce the problem of unequal global representation. The particle level set (PLS) method combines the advantages of both approaches by interchanging the information between the grid and the particles. Our work demonstrates that the PLS technique can be adapted to volumetric dye advection via streak volumes, and to the visualization by time surfaces and path volumes. We achieve this with a modified and extended PLS, including a model for dye injection. A new algorithmic interpretation of PLS is introduced to exploit the efficiency of the GPU, leading to interactive visualization. Finally, we demonstrate the high quality and usefulness of PLS flow visualization by providing quantitative results on volume preservation and by discussing typical applications of 3D flow visualization.     

Optimal replica placement in hierarchical Data Grids with locality assurance

In this paper, we address three issues concerning data replica placement in hierarchical Data Grids that can be presented as tree structures. The first is how to ensure load balance among replicas. To achieve this, we propose a placement algorithm that finds the optimal locations for replicas so that their workload is balanced. The second issue is how to minimize the number of replicas. To solve this problem, we propose an algorithm that determines the minimum number of replicas required when the maximum workload capacity of each replica server is known. Finally, we address the issue of service quality by proposing a new model in which each request must be given a quality-of-service guarantee. We describe new algorithms that ensure both workload balance and quality of service simultaneously.     

Transfer in variable-reward hierarchical reinforcement learning

Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs.