Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

基于HDASOM的预测技术在大众消费领域中的应用研究

突破传统研究方法,把消费心理特性与预测技术相结合,根据某消费者商品历史兴趣度的采样结果,应用GSM建立模型研究该消费者购买主导商品的可能性,并结合HDASOM和关联分析方法研究购买相关商品的可能性,从而预测未来一周内该消费者的购买行动。实验结果证明,预测结果符合大众消费观。     

Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.     

基于SOM网络的股票聚类分析方法

无监督的自组织映射(SOM)神经网络是用于聚类的主要人工神经网络模型之一.在SOM网络的基础上改进了网络中的邻域函数,并将其用于对股票进行分析和选择,得到了令人满意的结果.为了提高解的精度,避免多个输入样本映射到同一输出节点还提出了禁忌映射的方法.数值模拟表明该模型对于上市公司的聚类结果令人满意,对于股民客观、准确地选出真正具有投资价值的股票具有指导意义.     

基于SOM神经网络聚类的车牌号码倾斜校正算法

经过摄像机摄入的图像会发生倾斜,给车牌的准确识别带来了困难.针对此问题,利用SOM神经网络良好的聚类性能,在水平倾斜校正时,把车牌号码图像中的像素坐标聚成两类,拟合成一条直线,计算出该直线倾斜角,完成水平校正;按照以上同样方法进行垂直倾斜校正.实验结果表明,该方法能准确获取车牌号码的倾斜角,算法结构简单,抗干扰能力较强,符合汽车牌照图像的特点,具有较好的处理效果.     

人工神经网络在并行计算机集群上的设计研究

人工神经网络在集群上的并行化设计和实现能够充分发挥ANN并行处理的特点,缩短训练时间,降低算法复杂度。随着并行技术的日益成熟,在并行集群上以软硬件相结合的方式设计神经网络的重要性也不断提高。从软硬件平台的多方面讨论了并行集群技术对人工神经网络设计的支持,提出了一种SOM神经网络在并行集群上的设计方法和基础框架,并就并行集群上神经网络训练效率的问题进行了深入讨论。该方案可广泛应用于多种神经网络模型的并行计算机实现。     

SOM‐based projection module for mobile displays

Abstract— The development of a compact, efficient VGA projection module to be embedded in mobile devices is reported. The design incorporates laser/laser diode (LD) light sources, Schlieren optics, and a one‐dimensional diffractive spatial optical modulator (SOM). During development, the optical parameters were determined and the relationships between the parameters to optimize the optical specifications were derived. The resulting optimized specifications enable us to manufacture two types of optical modu les as compact as 13 cc and with as little as 10% speckle contrast ratio.     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.