基于H.264/AVC的帧内4×4预测模式快速选择算法

在H.264/AVC视频编解码标准中,帧内预测利用周围像素预测当前块来降低空间冗余,能极大地提高H.264/AVC的编码效率。然而,帧内预测的4×4预测有9种模式,为选出最佳模式,全搜索算法需要花费极大的计算量。为了降低帧内预测的复杂度,文章结合9种预测模式的各自特点和整数变换的线性性质,提出一种适用于H.264/AVC帧内4×4模式选择的快速变换和量化算法来减少对应模式变换和量化操作步骤的计算量。实验结果表明,优化算法可以有效地减少整数变换和量化操作的计算量。     

基于线性回归分析的视频质量评估方法

文章提出了一种利用线性回归分析方法的无参考视频序列质量评估算法。该算法主要利用帧间编码帧的比特数和该帧与其参考帧的差异两个参数进行线性回归分析来评估视频质量。该方法不需要原始参考视频,算法简单。通过对标准视频序列的仿真实验,该算法可有效评价不同视频的编码质量,使用该质量评估方法测得的失真视频客观质量评分与其主观质量评分有很好的一致性。     

边坡临界滑动面搜索的奖惩蚁群算法研究

边坡滑动面搜索是边坡稳定计算中一项关键的问题,其实质为安全系数最小滑动路径的搜索问题,本文对采用路径搜索的蚁群算法引入奖惩策略,加大较优路径和普通路径上信息素的差异,分化各条路径上的信息素,以克服其收敛速度慢、早熟收敛的缺点。通过把边坡滑动面搜索模型离散化,采用奖惩蚁群算法解决滑动面搜索问题,提出了一种临界滑动面搜索的新方法。最后对一个简单边坡和复杂边坡的典型算例及一个大坝边坡工程的应用实例进行了计算,验证了新算法的有效性及其高效性。结果表明,无论是对简单边坡还是复杂边坡,本文算法都能以更快的速度搜索到结果更好的临界滑动面,工程应用效果良好。     

基于遗传算法的田间尺度土壤水力参数与溶质运移参数优化

合理确定田间尺度土壤水力参数和溶质运移参数是保证农田土壤水盐动态模拟正确性的重要前提。本文开展了基于遗传算法(Genetic Algorithms,GA)与农田水文模型SWAP(Soil-Water-Atmosphere-Plant)耦合进行土壤水力参数和溶质运移参数优化的方法研究。在已有GA基础上引入了子体优生策略,并以完全嵌入方式耦合GA与SWAP模型。采用河套灌区曙光实验站的土壤剖面分层含水率(θi)和溶液浓度(cmli)、表土含水率(θsur)、实际腾发量(ETa)等观测资料,开展了田间尺度土壤水力参数和溶质运移参数优化的数值试验与相应分析。结果表明:(1)采用土壤分层信息(θi和cmli)作为观测数据,GA参数优化效果很好;(2)仅采用ETa作观测数据时,参数优化效果相对欠佳,需慎重使用,而结合ETa与θsur后可提高优化精度;(3)引入子体优生策略可提高GA的参数优化效率和精度。综上,结合GA与SWAP模型是优化田间尺度土壤水力参数和溶质运移参数的一种实用方法。     

关于酉对称矩阵的QR分解的注记

研究了行(列)酉对称矩阵的性质,修正了行(列)酉对称矩阵的QR分解的公式和快速算法.结果可减少行(列)酉对称矩阵的QR分解的计算量与存储量,并且不会丧失数值精度.     

Kriging代理模型在对地观测卫星系统优化中的应用

结构优化是对地观测卫星系统(Earth observation satellite system,EOSS)性能提高的关键,但其覆盖性能难以解析计算.为实现EOSS优化,提出了仿真优化的求解思路:构建Kriging代理模型对仿真数据进行拟合,采用代理模型最优和最大化期望提高相结合的机制选择更新点,并定义单位距离的函数改进对更新点进行过滤;提出了改进广义模式搜索算法求解代理模型,搜索步采用遗传算法和序列二次规划算法实现,筛选步采用不完全动态筛选.最后,通过仿真实例和对比实验验证了本文方法的有效性.     

Analysis of a clock synchronization protocol for wireless sensor networks

The Dutch company Chess develops a wireless sensor network (WSN) platform using an epidemic communication model. One of the greatest challenges in the design is to find suitable mechanisms for clock synchronization. In this paper, we study a proposed clock synchronization protocol for the Chess platform. First, we model the protocol as a network of timed automata and verify various instances using the Uppaal model checker. Next, we present a full parametric analysis of the protocol for the special case of cliques (networks with full connectivity), that is, we give constraints on the parameters that are both necessary and sufficient for correctness. These results have been checked using the proof assistant Isabelle. We report on the exhaustive analysis of the protocol for networks with four nodes, and we present a negative result for the special case of line topologies: for any instantiation of the parameters, the protocol will eventually fail if the network grows.     

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics

Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.