The Strong Property of Morphological Connected Alternated Filters

This paper studies connectivity aspects that arise in image operators that process connected components. The focus is on morphological image analysis (i.e., on increasing image operators) and, in particular, on a robustness property satisfied by certain morphological filters that is denominated the strong property. The behavior of alternated compositions of openings and closings is investigated under certain assumptions, particularly connectedness and a connected component preserving condition. It is shown that these conditions cannot in general guarantee the strong property of certain connected alternated filters because of issues related to the locality of the filters. As treated in the paper, there have been a series of misunderstandings in the literature concerning this topic, and it is important to clarify them. The root cause of those problems is discussed, and a solution is indicated. The class of connected openings and closings used to build connected alternated filters should therefore be defined to avoid such situations, since the strong property of alternated filters should be a distinctive characteristic of this class.

一种具有双重进化空间的扩展粒子群优化算法

为了使粒子群优化(PSO)适于求解更多类问题,提出一种由动力空间和制导空间共同进化的改进粒子群优化算法-具有双重进化空间的扩展粒子群优化算法(简记EPSO).在EPSO中,在演化转换映射的作用下,首先将动力空间中对粒子辅助位置的进化转换为制导空间中对主导位置的进化,然后基于对主导位置的择优选择操作实现算法的进化过程.EPSO克服了PSO仅适于求解连续域最优化问题的缺陷,也非常适于求解离散组合优化问题.对于随机3-SAT问题、背包问题和TSP问题,通过与PSO、ACO和GA等算法的计算对比表明:EPSO是一种继承了PSO优点的高效、扩展演化算法.     

An ant direction hybrid differential evolution heuristic for the large-scale passive harmonic filters planning problem

This article presents a heuristic which combines the orthogonal array experiment technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) for planning of a large-scale passive harmonic filters problem. The addressed problem has a multi-bus and under abundant harmonic current sources in the system. In this study, an orthogonal array is first conducted to obtain the initial solution. Next, an ant direction hybrid differential evolution (ADHDE) is applied to search for a near optimum solution. The objective is to minimize the cost of the filter, the total demand distortion of harmonic currents and the total harmonic distortion of voltages at each bus simultaneously. In order to determine a set of weights of each term in the objective function, the simplest and most efficient form of triangular membership functions has been considered. The designed heuristic ADHDEOA is applied to a practical harmonic problem in a steel plant, and three design schemes are compared to demonstrate the performance of the ADHDEOA.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

基于微粒群的K均值聚类算法在图像分类中的应用

提出一种新的图象分类算法椈谖⒘Ｈ旱腒均值聚类图象分类算法.将此算法和K均值聚类算法以及微粒群图像分类算法分别应用于MRI人脑图象的分类,并进行了比较.实验结果表明:基于微粒群的K均值聚类图象分类算法具有较好的全局收敛性,不仅能有效克服K均值算法易陷入局部极小值的缺点,且全局收敛性能优于微粒群图像分类算法.     

基于混沌序列的自适应粒子群优化算法

提出一种改进粒子群局部搜索能力的自适应优化算法。通过大量仿真试验,考察粒子平均速度和收敛性之间的关系,给出一种新的自适应调整权重策略。以粒子平均速度作为反馈信息,动态调整权重因子,控制粒子速度并使其沿理想速度曲线下降。在搜索过程中引入混沌序列以改进算法的局部搜索能力。对经典函数的测试结果表明,改进的混合算法通过微粒自适应更新机制确保了全局搜索性能和局部搜索性能的动态平衡,在稳定性和精度上均优于普通PSO算法。     

基于眨眼持续时间的司机疲劳检测方法

利用红外敏感的摄像机获得司机脸部图像,通过可变性模板匹配的方法对眼睛进行定位,然后利用卡尔曼滤波的方法跟踪眼睛,得到司机的眨眼持续时间参数,以此为依据判断司机是否疲劳.主要研究了其中的图像处理方法--特征提取算法和眼睛定位、跟踪算法.实验结果证明,眨眼持续时间判断是否疲劳的有效指标.     

自适应背景提取算法的比较

介绍几种背景自适应提取算法的原理,分析算法在交通视频检测中的仿真效果.依据交通场景提取中实时性、准确性等关键技术指标,采用基于"真理图"的量化评估方法,在多种复杂交通场景中利用recall,precision评价参数对背景提取算法的效果进行比较分析,明确被评价算法的性能特点与适用的交通场景.     

自动变速器换档规则的粒子群优化提取方法

针对神经网络“黑箱”模型的缺陷,利用粒子群优化的换档规则提取算法,将规则编码为粒子的方法,通过粒子群优化算法的“位置-速度”搜索模型生成换档规则集。实验分析了标准粒子群与惯性递减粒子群在最优解搜索过程中的性能差异,并验证了该方法的有效性。     

一种用于浮点乘法的边界筛选伪随机测试方法

复杂测试空间中难以命中的冷僻细节,导致在IEEE浮点算法测试过程中难以验证单条浮点运算的正确性。该文针对传统直接随机测试方法的缺点,提出一种边界筛选生成案例的测试方法。该方法对待测算法边界条件建模,求解边界条件,利用解来构造筛选操作数的标准,并通过筛选操作数实现测试。实验证明该方法比直接随机测试方法更可靠。