Adaptive Identification of Logging Lithology Based on VPSO-ENN Hybrid Algorithm

Particle swarm optimization (PSO) was modified by variation method of particle velocity, and a variation PSO (VPSO) algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network (ENN) to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm (GA) and BP neural network (GA-BP), VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.     

基于混合粒子群优化的2维Otsu路面裂缝图像阈值分割方法

针对传统2维Otsu法由于计算量大而无法实时对路面裂缝图像进行分割的问题,将粒子群算法和遗传算法相结合,提出了一种基于混合粒子群优化的2维Otsu路面裂缝图像阈值分割方法。实验结果表明:与标准粒子群优化的2维Otsu阈值分割法相比,文中方法可以完整分割出图像中的裂缝目标;与传统2维Otsu法相比,文中方法减少了算法运行的时间,提高了路面裂缝图像的分割效率。     

A HOS-based blind signal extraction method for chaotic MIMO systems

A novel method to extract multiple input and multiple output （MIMO） chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic （HOS） technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.     

Feature extraction and modeling of welding quality monitoring in pulsed gas touch argon welding based on arc voltage signal

Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding （GTAW）. A method for online quality monitoring based on adaptive boosting algorithm is proposed through the analysis of acquired arc voltage signal. Two feature extraction algorithms were developed in time domain and frequency domain respectively to extract six statistic characteristic parameters before removing the pulse interference using the wavelet packet transform （WPT）, based on which the Adaboost classification model is successfully established to evaluate and classify the welding quality into two classes and the classified accuracy of the model is as high as 98.81%. The Adaboost algorithm has been verified to be feasible in the online evaluation of welding quality.     

Multimodal Evolution Approach to Multidimensional Intrusion Detection

An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.     

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脉冲耦合神经网络（PCNN）具有良好的图像分割特性,但神经网络参数的选取对分割效果有较大影响,如何自适应地选择网络参数是脉冲耦合神经网络应用研究的重要内容.本文首次从脉冲耦合神经网络的耦合特性出发,结合神经计算原理及图像局部区域的灰度特性,提出了脉冲耦合神经网络耦合参数的优化算法.首先利用Hebb学习规则对脉冲耦合神经网络模型的链接权值矩阵进行更新,然后利用图像局部区域的均方差自适应确定神经元链接强度系数,最后将优化的PCNN模型应用于运动车辆图像分割.通过耦合参数的优化,增强了神经元之间的耦合强度,与传统PCNN的车辆分割结果相比,较好地避免了过分割和欠分割现象,提高了运动车辆图像中车牌区域的分割质量,为后续车辆特征的提取奠定了良好的基础.     

Feature Selection for SVM Classifiers Based on Discretization

The rough sets and Boolean reasoning based discretization approach （RSBRA） is not suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A modified RSBRA for feature selection was proposed and evaluated with SVM classifiers. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of the RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accuracy and less training time than the original RSBRA.     

基于各向异性测度的路面三维图像裂缝识别

为准确而完整地识别路面裂缝,提出了基于1 mm/像素的路面三维图像裂缝自动识别算法．该算法主要包括各向异性测度计算与自适应优化阈值分割、深度验证和多分辨率去噪处理3个部分．首先,针对路面图像像素特征,基于0°、45°、90°和135°四个方向的线性邻域的均值和标准差计算每个像素的各向异性测度（表征方向性的强弱）,并应用最大类间方差法确定最优阈值,将路面图像分为强方向性和弱方向性像素两类;其次,根据半径为d的正方形邻域深度均值设定阈值,用方向性强且深度低于或等于该阈值的像素形成初步的裂缝图像;最后,将裂缝图像划分为多个子块,设计去噪模板对裂缝图像进行滑动窗口去噪处理,获得最终裂缝图像．基于166幅含有各类裂缝的三维路面图像（2048×2048像素）进行测试分析,结果显示,本文算法获得了较高的准确率（均值91．57％）和召回率（均值81．29％）,最终以84．26％的F1均值优于种子识别算法（F1均值69．19％）、Canny边缘检测（F1均值8．15％）和OTSU分割（F1均值5．11％）．     

基于OpenMP的并行遗传算法求解SAT问题

为了提高SAT （boolean satisfiability） 问题求解效率,在OpenMP （open multi-processing） 编程框架下,将遗传算法与局部搜索算法结合,改进了混合遗传算法中的选择算法,将原有选择操作的时间复杂度降低到O(N)级别. 算法采用OpenMP中的编译制导语句#pragma omp parallel粗粒度并行化驱动混合遗传算法,采用#pragma omp single语句块实现了子种群间个体的同步迁移操作. 与同类算法HCGA （hybrid cloud genetic algorithm）比较分析表明:改进算法HGA （hybrid genetic algorithm）以及并行后的混合遗传算法CGPHGA （coarse-grained parallel hybrid genetic algorithm）在求解成功率和求解效率上都有显著提高,部分问题求解成功率提高达5倍.      

Efficient Search in P2P File Sharing System

A new routing algorithm of peer-to-peer file sharing system with routing indices was proposed, in which a node forwards a query to neighbors that are more likely to have answers based on its statistics. The proposed algorithm was tested by creating a P2P simulator and varying the input parameters, and was compared to the search algorithms using flooding （FLD） and random walk （RW）. The result shows that with the proposed design, the queries are muted effectively, the network flows are reduced remarkably, and the peer-to-peer file sharing system gains a good expansibility.     

基于视觉注意机制 PCNN 模型的车牌 图像分割方法

车辆图像中车牌具有所占比例小、位置不固定、大小不一,以及分割易受光照影 响的特点.因此,车牌图像的分割始终是车辆跟踪、车辆识别等领域中的难点问题.针对以 上问题,本文提出了基于视觉注意机制脉冲耦合神经网络模型的车牌图像分割方法.该方 法将视觉注意机制中的数据驱动模式和任务驱动模式相结合. 数据驱动模式中,通过对 PCNN 模型细胞感受野功能的完善,使优化 PCNN 模型具有了尺度性和方向性.任务驱 动模式中,针对不同尺度的分割,利用组合不变矩和局部灰度熵,自适应地确定目标的特 征尺度和最佳尺度,并确定该目标最终的分割结果.经实验验证,该方法对车牌图像具有 较好的分割效果.     

Saliency motivated pulse coupled neural network for underwater laser image segmentation

The detection range of underwater laser imaging technology achieves 4—6 times of detection range of conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea exploration and robotic works. However, the special features in underwater laser images, such as speckle noise and non-uniform illumination, bring great difficulty for image segmentation. In this paper, a novel saliency motivated pulse coupled neural network(SM-PCNN) is proposed for underwater laser image segmentation. The pixel saliency is used as external stimulus of neurons. For improvement of convergence speed to optimal segmentation, a gradient descent method based on maximum two-dimensional Renyi entropy criterion is utilized to determine the dynamic threshold. On the basis of region contrast in each iteration step, the real object regions are effectively distinguished,and the robustness against speckle noise and non-uniform illumination is improved by region selection. The proposed method is compared with four other state-of-the-art methods which are watershed, fuzzy C-means, meanshift and normalized cut methods. Experimental results demonstrate the superiority of our proposed method to allow more accurate segmentation and higher robustness.     

一种基于遗传算法的最优阈值图像分割算法

为了提高图像分割效率,提出一种基于遗传算法的最优阈值搜索方法OTSGA.OTSGA算法对图像的灰度级进行二进制编码,生成初始种群,求出每个个体的二维最大熵,然后根据设定的寻优准则进行相应的遗传操作以搜索阈值最优解.为了避免在求解过程中出现早熟现象,OTSGA算法将交叉操作得到的个体群与上一代种群混合,得到新的种群进行遗传操作,避免了个别个体在遗传运算的最初迭代时就在种群中占据主导地位,导致求解过程的过早收敛.实验结果表明,OTSGA最优阈值搜索方法不仅降低了运算开销,而且获得了满意的图像分割效果.     

基于Kittler最小误差分割算法的小视场星图分割

小视场星图易受光照不均和噪声影响,常用阈值分割算法存在处理效果不佳或效率较低的不足.针对星图灰度的高斯分布特征,基于贝叶斯最小误差理论,提出利用Kittler最小误差分割算法处理小视场星图.以视频测量机器人为测量平台,以"优度法"、区域一致性、区域对比度和时间复杂度为评价指标,对比了常用的阈值分割算法和一维最大熵法,验...     

遗传算法在图像分割中的应用

图像分割是图像分析的预处理阶段,被认为是计算机视觉中的一个瓶颈.基于扩展的Otsu最优阈值图像分割方法,提出用一种改进遗传算法进行图像分割的方法,并给出了遗传算法中参数的设定.仿真结果表明,改进算法的计算速度不仅明显优于传统的Otsu方法,而且算法的分割效果也很好.     

图像分块下的隧道裂缝识别方法

针对隧道衬砌表面不均匀光照、渗水和噪声等强视觉干扰,设计了基于图像分块的隧道衬砌裂缝检测算法;根据中国西部地区的地理特征和隧道衬砌的外观病害,研制开发出一种快速、自动化的非接触式智能隧道结构物外观检测系统; 以非均匀光照下隧道图像数据集为研究对象,在图像分块的基础上提出一种适用于隧道裂缝特征提取的图像识别算法;研究了电子元件产生的噪声,并分析和总结了隧道衬砌的灾害特征;根据裂缝特征和分辨率将图像矩阵划分为适当数量的区域块,根据区域块的灰度特征将原始图像划分为目标背景区、目标病害区、病害背景区和其他区域,通过最大类间方差法和局部阈值法分割得到了隧道裂缝的粗图像,在此基础上进行了粗图像裂缝特征提取;对原始图像的每个区域块进行了对比度受限的自适应直方图均衡操作和局部阈值分割,得到了细节图像;将细节图像和粗图像的重叠区域设为理想裂缝二值化图像;结合隧道结构物外观检测系统对不同方向的裂缝图像进行了二值化试验,并通过隧道裂缝定位和投影法得到了隧道衬砌图像中裂缝的位置信息和方向。研究结果表明:提出的算法对隧道裂缝识别的准确值、召回率和F值可分别达90.34%、98.78%和94.37%,既可以保证隧道裂缝的完整性,也可以在非均匀光照下最大程度地保留目标裂缝的细节,可用于处理一般灰度图像的二值化问题。      

Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses Gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify Gabor energy spectra to get the center number of fuzzy C-means(FCM) clustering. Moreover, the user-defined Gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack,damage, pit and other defects on the magnetic tile surface.     

Research on confirmation of basic technological parameters of tension levellers based on neural network and genetic algorithm

Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts' experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. In this paper, a method is used based on neural network and genetic algorithm. Neural network has a good ability to extract rules from work process of tension levellers. Then using neural network, which has learned from a lot of working samples, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.     

基于遗传算法(GA)优化BP神经网络的船舶交通流量预测

为了提高船舶交通流量的预测精度,在BP神经网络的基础上,结合遗传算法(GA)建立一个新的预测模型.该模型利用GA自适应搜索能力和较快的收敛速度,进而确定BP神经网络中的最优权值和阈值.以青岛港2011—2019年船舶交通流量统计数据为例,进行仿真实例验证.结果表明,与传统的BP神经网络相比,该模型能显著地提高船舶交通流量的预测精度,用于预测船舶交通流量具有一定可行性.