Evaluating part designs for automatic feeding in robotic assembly systems: A geometric analysis of the nesting of polyhedral objects

One of the problems encountered by automatic feeding devices, such as vibratory bowl feeders, in robotic assembly systems is nesting which leads to inseparability of parts and jamming. In this paper, we describe a design evaluation procedure to determine whether a part is prone to nesting and to quantify the degree of nesting. The proposed methodology reduces the 3-D protrusion-hole polyhedral containment problem of nesting to a 2-D polygon containment problem by employing a loop-based feature recognition scheme. Subsequently, the 3-D containment configuration is reconstructed from that of the 2-D by using a strategy calleddrop, pull and push. In this context, we introduce the notion of axis as a characteristic direction of a feature that facilitates the analysis of generalized 3-D polyhedral containment. The algorithms are applicable to generic B-Rep CAD data, and their time and space complexities are polynomial.     

基于神经网络的智能过滤系统的研究与设计

首先对现有的网络安全隐患和现有的关于安全过滤方法作了简单介绍,提出现有网络过滤技术的不足,接着提出利用信息检索、人工智能和遗传算法相结合的技术,能有效提高网络过滤的性能.     

基于遗传算法的土钉支护结构优化设计研究

通过建立三维有限元模型,对土钉支护的变形和受力性能进行分析,得出土钉力的合理分布。在此基础上,考虑土钉支护的水平位移,采用遗传算法,对土钉支护的结构优化设计进行研究,建立了土钉支护结构的优化设计数学模型,并编制了相应的计算程序。通过算例分析,并与基于极限平衡分析的优化结果相比较,得出合理的土钉支护结构设计的参考结论。     

Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.     

Cycle detection using a stack

We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.     

Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.     

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.     

遗传算法在CDMA网络基站分布规划中的应用

CDMA网络的基站分布规划属于多目标组合优化问题，为此在遗传算法的基础上提出了一种基于成本控制的CDMA网络基站分布规划算法。该算法综合考虑了在CDAM网络规划实际工程中的多种规划目标和要求，能够找到符合CDMA网络建设需要的基站分布方案。     

基于频率采样技术的FIR数字滤波器优化设计

本文提出了一种FIR数字滤波器满意优化设计方法，将滤波器过渡带样本值作为优化变量，通过设计通带、过渡带和阻带性能指标满意度函数和综合满意度函数，构造出满意优化模型，并用本文提出的新量子遗传算法搜索满意解，FIR高通和带阻数字滤波器的设计结果表明，采用满意优化方法设计的FIR滤波器的性能优于传统方法。