冗余度机器人的运动规划碰撞算法

在考虑了操作机的关节极限、自碰撞和静态障碍物的情况下,从给定的初始位形出发,发现一条到末端效应器目标位置和姿态的相连可到达路径。方法给出了机器人操作机点到点逆运动学问题求解算法,利用碰撞算法实现了冗余度机器人运动规划,仿真验证了该方法的有效性,并表明了该方法具有较大的实用价值。     

动静态属性数据相结合的客户分类方法研究

研究了一种客户动态、静态属性数据相结合的客户分类方法。提出了客户时间序列的加权处理方法,并应用客户时间序列的统计特征作为聚类特征向量,采用混合式遗传算法对客户聚类,使每一类客户具有相似的时序特征。在此基础上将聚类结果与客户的静态属性数据相结合,对客户进一步分类。实验结果表明,与传统的基于静态属性数据的客户分类方法相比,本文的方法提高了客户分类的准确性。     

《论语》释名辨正及《学而篇》校释考异

古今《论语》释名之分歧主要在“论”字上，对“论”的解释达八种之多，其中唯章太炎的“论”作“仑”说合乎历史实际。古今对《学而篇》的校勘、解释亦不乏分歧，故略作考察辨正：对“学而”章应作整体性、连贯性解释；“为仁之本与”之“仁”作“人”；“传不习乎”之“传”的本字为“专”；“贤贤易色”句意即“贤贤”“修然”；“主忠信，无友不如己者”句意即君子忠信，不与不忠不信的人交朋友。     

Bayesian analysis of a marked point process: Application in seismic hazard assessment

Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.     

遗传算法在入侵检测中的应用

介绍了基于模型推理和基于模型两种入侵检测系统,提出了一种新的基于智能体技术的入侵检测系统体系结构,解决了传统集中式入侵检测系统的弊病,将任务处理和数据分布到网络各个结点上,充分利用网络资源协同完成入侵检测任务;介绍了遗传算法在该系统中的应用,因系统安全的先验知识体现在对原始数据中有价值特征属性变量集的选择上,故利用遗传算法对特征属性变量子集的选择进行优化,找到相对最优的由特征向量表示的特征属性变量集,以降低入侵检测系统的负荷。     

非负权最短路问题的一种简便算法

提出了非负权最短路问题的一种新算法。与有名的Ｄｉｊｋｓｔｒａ算法相比，这种算法计算简便、容易理解、易于编程。     

基于遗传算法的有柔性路径的动态调度问题研究

建立了有柔性路径的FMS动态调度问题的模型．采用离线重调度的方法，提出一种结合离线重调度两种生成调度方式的方法来阐述FMS的有柔性路径动态调度问题．最后给出了该问题的遗传算法解决方法和计算示例．     

Improving flexibility and efficiency by adding parallelism to genetic algorithms

In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a distributed model consisting in a ring of GA islands. The analyzed features are the time complexity, selection pressure, schema processing rates, efficacy in finding an optimum, efficiency, speedup, and resistance to scalability. Besides that, we briefly discuss how the migration policy affects the search. Also, some of the search properties of cellular GAs are investigated. The selected benchmark is a representative subset of problems containing real world difficulties. We often conclude that parallel GAs are numerically better and faster than equivalent sequential GAs. Our aim is to shed some light on the advantages and drawbacks of various sequential and parallel GAs to help researchers using them in the very diverse application fields of the evolutionary computation.     

基于定位辅助按需拓扑维护的超宽带自组网路由算法

提出了一种基于定位辅助按需拓扑维护的超宽带自组网路由算法,该算法利用超宽带技术精确定位信息所获得的网络拓扑信息和路由信息进行分组转发,路由维护阶段在定位信息辅助下采用按需方式进行断链路由的修复和拓扑维护,通过基于位置信息的按需路由发现和限制路由查找范围,以及定位信息和网络拓扑信息的及时更新,在降低协议开销的同时保证了算法的有效性。仿真表明,该算法在分组丢失率、平均端到端时延和路由附加开销等方面具有良好性能,其优良的分布式控制特征能适应超宽带自组网的动态环境。     

Improving Convergence of the HastingsMetropolis Algorithm with an Adaptive Proposal

The HastingsMetropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental (or proposal) distribution. We present an inhomogeneous HastingsMetropolis algorithm for which the proposal density approximates the target density, as the number of iterations increases. The proposal density at the n th step is a non-parametric estimate of the density of the algorithm, and uses an increasing number of i.i.d. copies of the Markov chain. The resulting algorithm converges (in n ) geometrically faster than a HastingsMetropolis algorithm with any fixed proposal distribution. The case of a strictly positive density with compact support is presented first, then an extension to more general densities is given. We conclude by proposing a practical way of implementation for the algorithm, and illustrate it over simulated examples.