车牌识别机器学习方法的数学建模与实现

首先,针对不同光照、复杂背景和投影失真的车牌图像建立基于Adaboost算法和改进Haar特征的车牌检测模型;然后,运用Radon变换进行车牌校正,并结合3次B样条小波变换和识别反馈模型对字符进行粗和精分割;最后,根据汉字和数字字母的不同结构特征,采用不同的算法提取特征,特别是针对车牌字符特点,训练汉字、字母和数字字母3种神经网络模型用于建立字符识别模型.实验结果表明该模型是实用的.     

Structural Damage Identification Using an Adaptive Multi-stage Optimization Method Based on a Modified Particle Swarm Algorithm

An adaptive multi-stage optimization method utilizing a modified particle swarm optimization (MPSO) is proposed here to identify the multiple damage cases of structural systems. First the structural damage problem is defined as a standard optimization problem. An efficient objective function considering the first few natural frequencies of a structure, before and after damage, is utilized for optimization. A modified particle swarm optimization (MPSO) dealing with real values of damage variables is introduced to solve the optimization problem. In order to assess the performance of the proposed method, some illustrative examples with and without considering the measurement noise are tested. Numerical results demonstrate the high accuracy of the method proposed for determining the site and severity of multiple damage cases in the structural systems.     

基于DCT变换的车牌定位算法

车牌定位技术是车牌识别技术中最重要的部分,利用车牌图像在DCT域的水平和竖直能量值对图像进行进一步二值化处理,通过中值滤波和小区去除定位车牌.实验结果表明这种车牌定位方法具有算法实现简单、速度快、适应性强的特点.     

A novel particle swarm optimization algorithm based on particle migration

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At periodic stage in the evolution, some particles migrate from one complex to another to enhance the diversity of the population and avoid premature convergence. It further improves the ability of exploration and exploitation. Simulations for benchmark test functions illustrate that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool.     

修正主成分分析及其在车牌定位中的应用

车牌照识别技术是现代智能交通的重要组成部分.为了从彩色图像中定位及分割出汽车牌照,将图像中每一个像素点的红、绿、蓝值看成三维空间中的点(r,g,b),采用主成分分析对含牌照的彩色图像进行正交变换得到(r′,g′,b′),分别以r′,g′,b′作为灰度值得到三个主成分图像,牌照信息体现在第二主成分图像中.为了进一步提高车牌定位效率,对正交变换矩阵进行了修正,对修正后的第二主成分图像进行二值化,分割出白色区域即可定位出汽车牌照.实验表明,本算法对噪声不敏感,抗干扰性强,识别效率高.     

基于遗传算法的固定起讫点危险品配送路线优化

为科学选择危险品配送路线,保障运输安全,将传统TSP(Travelling SalesmanProblem)问题加以推广和延伸,建立以路段交通事故率、路侧人口密度、环境影响因子和路段运输费用为指标的固定起讫点危险品配送路线优化模型.以遗传算法基本框架为基础,引入新的遗传算子,构建了可用于实现模型的多目标遗传算法.实例仿真表明,所建模型和算法在求解固定起讫点危险品配送路线优化问题中有较好的实用性.     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

一种快速有效的混合倾斜车牌校正方法

车牌倾斜校正是车牌识别系统中的一关键技术,校正效果将直接影响到后续字符分割和识别的效果.为此,提出了一种快速有效的混合倾斜车牌校正方法.水平倾斜校正时,先进行Sobel垂直边缘检测算子提取字符的有效数据点,构造有效数据点矩阵,然后进行矩阵奇异值分解,利用特征向量矩阵导出车牌水平倾斜角度的表达式;垂直倾斜校正时,首先通过Sobel垂直和水平边缘检测有效去除边框及上下左右噪声干扰区域,然后通过形态学腐蚀提取有效反映字符水平错位的有效数据点,其次将这些有效数据点水平投影分成七个字符区域,求取每个区域错切变换后的最佳列坐标质心对应其区域垂直倾斜角,最后取区域垂直倾斜角的均值作为车牌的最佳垂直倾斜角.对大量复杂背景下的倾斜车牌进行实验,并做了精确度、运行时间、抗干扰性分析,实验结果表明:提出的混合倾斜校正方法精确度高、运行时间短、抗干扰性强.     

Fuzzy criteria for feature selection

The presence of less relevant or highly correlated features often decrease classification accuracy. Feature selection in which most informative variables are selected for model generation is an important step in data-driven modeling. In feature selection, one often tries to satisfy multiple criteria such as feature discriminating power, model performance or subset cardinality. Therefore, a multi-objective formulation of the feature selection problem is more appropriate. In this paper, we propose to use fuzzy criteria in feature selection by using a fuzzy decision making framework. This formulation allows for a more flexible definition of the goals in feature selection, and avoids the problem of weighting different goals is classical multi-objective optimization. The optimization problem is solved using an ant colony optimization algorithm proposed in our previous work. We illustrate the added value of the approach by applying our proposed fuzzy feature selection algorithm to eight benchmark problems.     

混合优化RBF-BP网络的板形缺陷识别

针对传统板形模式识别方法存在精度低、鲁棒性弱的问题,提出了一种混合优化RBF-BP组合神经网络板形模式识别方法。首先利用自组织映射网络(SOM)对样本聚类,利用聚类后的网络拓扑结构确定RBF的中心,并计算RBF的宽度,克服了传统聚类算法随机选取中心导致聚类结果不稳定的问题。然后利用遗传算法(GA)良好的全局搜索能力优化整个网络的权值。RBF-BP组合神经网络是由一个RBF子网和一BP子网串联构成的,该网络同时具备BP神经网络能较好地预测未知样本的能力以及RBF神经网络的逼近速度快的优点。并以某900HC可逆冷轧机板形识别为应用背景,在MATLAB2010a环境下进行仿真实验,结果表明混合优化RBF-BP组合神经网络的板形模式识别方法能够识别出常见的板形缺陷,提高了板形缺陷识别精度并具有较好的鲁棒性,可以满足板带轧机高精度的板形控制要求。     

区域集群下板材订单配置模型及算法研究

针对集群企业板材资源滞留、无法共享、加工旺季材料短缺等问题,依据区域板材特性和区域企业集群地理相关优势,建立以减少需求方板材订单采购费用最小化为目标的板材订单分配模型,采用以粒子群、免疫算法相结合的混合调度算法。计算过程中,将订单分配对应企业编号作为免疫系统的抗体基因,通过比较适应度函数解与订单预算成本的关系,将抗体群区分为支配解与非支配解,提高算法对抗原的免疫能力和最优解的选择概率。最后以板材订单分配实例进行试验仿真,分别采用PSO算法与IA-PSO算法进行试验对比,对平台上6家订单发布企业寻找合适地理位置相近和价格相对低廉的供应商。试验结果表明,IA-PSO算法能够有效地解决区域集群内板材订单的匹配问题,并且在寻找价格更低和位置更合适的供应商上更有优势。     

Lasso变量选择的分布式算法

Lasso是机器学习中比较常用的一种变量选择方法,适用于具有稀疏性的回归问题.当样本量巨大或者海量的数据存储在不同的机器上时,分布式计算是减少计算时间提高效率的重要方式之一.本文在给出Lasso模型等价优化模型的基础上,将ADMM算法应用到此优化变量可分离的模型中,构造了一种适用于Lasso变量选择的分布式算法,证明了...     

A discrete particle swarm optimization method for feature selection in binary classification problems

This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance.     

时延网络控制系统的协同设计方法研究

在搭建的虚拟平台上对多任务的网络控制系统调度和嵌入LQG控制算法进行了仿真.探索了网络控制系统中调度与控制协同设计方法,讨论系统采样周期对网络控制系统的影响.以优化控制系统的性能为目标,以网络的可调度性为条件,结合系统控制和调度算法,对网络控制系统进行静态性能指标估计和动态调度仿真相结合.结果表明该方法既满足了控制系统的性能,又优化了网络的调度,提高了网络的资源率.     

Discovering model structure for partially linear models

Partially linear models (PLMs) have been widely used in statistical modeling, where prior knowledge is often required on which variables have linear or nonlinear effects in the PLMs. In this paper, we propose a model-free structure selection method for the PLMs, which aims to discover the model structure in the PLMs through automatically identifying variables that have linear or nonlinear effects on the response. The proposed method is formulated in a framework of gradient learning, equipped with a flexible reproducing kernel Hilbert space. The resultant optimization task is solved by an efficient proximal gradient descent algorithm. More importantly, the asymptotic estimation and selection consistencies of the proposed method are established without specifying any explicit model assumption, which assure that the true model structure in the PLMs can be correctly identified with high probability. The effectiveness of the proposed method is also supported by a variety of simulated and real-life examples.

     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

基于数学形态学与Radon变换的车牌定位法

从粗定位和精定位方面考虑车牌定位.利用腐蚀、膨胀和差运算将RGB车牌图像转换为车牌图像位置为彩色的而其余位置是灰色的图像,将所获得的图像转化为灰度图像,利用Robert算子进行边缘检测,再进行腐蚀和膨胀运算,根据阈值获得连通区域,利用颜色特征实现车牌的粗定位.将粗定位后的图像进行二值化、差运算、Radon变换,再进行腐蚀运算,图像的填充等步骤,再考虑图像的上下左右边界实现车牌的精定位,对185幅车牌图像进行定位.通过实验,车牌定位准确率达到91.89%.说明所提出的方法简单易行,具有一定的实效性.     

Optimization of laminated composite plates for maximum fundamental frequency using Elitist-Genetic algorithm and finite strip method

In the present paper, fundamental frequency optimization of symmetrically laminated composite plates is studied using the combination of Elitist-Genetic algorithm (E-GA) and finite strip method (FSM). The design variables are the number of layers, the fiber orientation angles, edge conditions and plate length/width ratios. The classical laminated plate theory is used to calculate the natural frequencies and the fitness function is computed with a semi-analytical finite strip method which has been developed on the basis of full energy methods. To improve the speed of the optimization process, the elitist strategy is used in the Genetic algorithm. The performance of the E-GA is also compared with the simple genetic algorithm and shows the good efficiency of the E-GA algorithm. A multi-objective optimization strategy for optimal stacking sequence of laminated box structure is also presented, with respect to the first natural frequency and critical buckling load, using the weighted summation method to demonstrate the effectiveness of the E-GA. Results are corroborated by comparing with other optimum solutions available in the literature, wherever possible.     

Adaptive Splines and Genetic Algorithms

Most existing algorithms for fitting adaptive splines are based on nonlinear optimization and/or stepwise selection. Stepwise knot selection, although computationally fast, is necessarily suboptimal while determining the best model over the space of adaptive knot splines is a very poorly behaved nonlinear optimization problem. A possible alternative is to use a genetic algorithm to perform knot selection. An adaptive modeling technique referred to as adaptive genetic splines (AGS) is introduced which combines the optimization power of a genetic algorithm with the flexibility of polynomial splines. Preliminary simulation results comparing the performance of AGS to those of existing methods such as HAS, SUREshrink and automatic Bayesian curve fitting are discussed. A real data example involving the application of these methods to a fMRI dataset is presented.     

A multilevel image thresholding using the honey bee mating optimization

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu’s method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient.