NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.

An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.

基于改进Gabor算法的遮挡人脸智能识别方法

为提高有遮挡人脸的识别精度,提出基于改进Gabor算法的遮挡人脸智能识别方法。首先,对人脸图像动态范围压缩,并选择反锐化掩模滤波算法展开图像增强处理;其次,利用Gabor滤波器对信息保留较完整、亮度较高的半边脸进行特征提取;最后将提取到的Gabor特征输入到极限学习机中完成遮挡人脸的智能识别。实验结果表明,所提方法对处理遮挡人脸图像具有良好的效果,且其对人脸图像识别具有精准度高、识别时间短等优点。

Nonlinear Rayleigh wave inversion based on the shuffled frog-leaping algorithm

At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm （SFLA）, into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.

An Adaptive Non-Linear Map and Its Application

A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

A Novel Nonlinear Algorithm for Typhoon Cloud Image Enhancement

A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon＇s center or intensity because the contrast of the received typhoon cloud image may be bad.Our aim is to extrude the typhoon＇s eye in the typhoon cloud image.A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image.Differential evolution algorithm is used to choose the optimal nonlinear transform parameter.Finally,geodesic activity contour model is used to extract the typhoon＇s eye to verify the performance of the proposed method.Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon＇s eye.

An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya

An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.

煤矿井下自动加卸钻杆靶标图像清晰化方法研究

受煤矿井运输巷道空间限制,将钻孔机器人设计为灵活布局的钻进和钻杆加卸2个独立系统,利用基于合作靶标的单目视觉识别,将钻进和钻杆加卸2个系统关联,解决两系统递送钻杆的协同作业问题。然而,煤矿井下钻孔施工中场景复杂,存在孔口煤渣、污水污染合作靶标、粉尘浓度大能见度低、人造光源亮度低、光照不均匀使靶标图像对比度低,以及矿灯直射合作靶标反光等问题,会影响合作靶标图像清晰度,使基于靶标识别加卸钻杆的位置的精度降低,进而影响钻孔机器人自动加卸钻杆的成功率。因此,首先针对施工中孔口返出的煤渣、煤泥污染合作靶标的问题,利用形态学滤波中先腐蚀,再膨胀的开运算,清除煤渣间断斑块和突出靶标像素点;先膨胀再腐蚀的闭运算,消除小的孔洞和间断细小鸿沟,达到消除固体污染物对合作靶标的影响;其次针对粉尘、岩尘、水雾和污水影响图像对比度问题,通过暗通道先验求出初始透射率,采用梯度域导向滤波精细化透射率,利用导向滤波得到改进暗通道,并求大气光值,利用大气散射模型得到除雾后的图像,实现清晰化图像、增强图像对比度目的;最后对亚像素级角点和方向进行精细化,优化能量函数,使用种子棋盘格沿4个边缘生长棋盘格,用能量变化情况确认是否生长出最大的棋盘,补全因强光反射缺失的合作靶标图像。实验结果表明:将形态学滤波、去雾算法和棋盘格生长等方法相融合,可增强煤矿井下复杂场景钻孔施工中合作靶标图像的清晰度,提高基于视觉识别的图像精度,为钻孔机器人自动加卸钻杆的精确控制奠定基础。

基于并行运算的双层图像锐化方法

针对低清晰度照片或图像放大后边界模糊、画质差及人们对高清图像的实际需求,基于统一计算架构(CUDA)环境,提出了一个两层结构的图像并行锐化方法,设计并实现了一个基于GPU的并行锐化算法:第一层采用并行线性插值法,反复对图像非边界部分进行计算以及边缘区域锐化处理;第二层采用改进的梯度法对图像进一步优化。放大后的图像经该方法处理后,基本上可消除图像边缘区域的锯齿,使图像画质平滑、自然、清晰。经实验验证,设计的基于GPU的并行锐化算法在效率和画质上都优于目前流行的算法,提出的方法可应用于现有图像及照片放大后处理。

求解TSP的空间锐化模拟退火算法

１引言模拟退火算法已被成功地用于求旅行商问题（ＴＳＰ）的最优路径［１］．但当ＴＳＰ实例搜索空间的“地形曲面”较粗糙时，有限时操作模拟退火过程可能因最终落入一个较差的局部“陷井”无法跳出，而难以得到高质量解．不同于通常改进算法自身的思路，本文提出了一种...

基于FPGA的红外图像处理算法的测试系统

为识别和提取红外图像中的有用信息,红外系统常采用各种算法处理采集到的数据。随着红外技术的不断发展,与红外相关的图像处理算法层出不穷。除了常规的图像处理算法外,为了提高人眼对红外图像的识别率,伪彩色编码技术也常被使用。为了便于在实际应用选取最佳的红外图像处理算法,基于FPGA设计了红外图像处理算法的测试系统,该系统能集成与红外相关的常见图像处理算法和实时添加的自定义图像处理算法,并利用显示终端对红外图像数据进行实时验证,通过对彩虹编码、热金属编码与图像锐化算法的测试试验,表明了该测试系统的实用性。

对比度和细节增强显著性检测方法研究

针对现阶段已经存在的显著性检测算法存在对比度不鲜明,显著图的图像细节不够明显,背景抑制不彻底的不足,提出一种基于对比度拉伸算法和用拉普拉斯算法改进Robert交叉梯度锐化的全频域显著性检测CRT算法,该方法利用FT算法自身运算速度快,具有去噪效果的优点以及对比度拉伸提升显著图对比度以及Robert梯度锐化算法提升边缘细节的特点以及提出的背景修正算法对背景能够进一步抑制的特点,最终将获得对比度更高,显著图细节更明显,背景抑制效果更好的显著图。该算法使用MSRA10K和THUR15K数据集做显著性检测检验,与其他6种流行的显著性检测方法做对比。从主观上得到显著图的对比度,图像细节均优秀于与它对比的6种算法。客观的指标显示,该算法用MSRA10K数据集进行显著性检测得到的MAE值是0.12,在THUR15K数据集上得到的MAE值是0.06,均优于与它对比的6种算法。平均结构性指标S-measure值为0.8,只略低于HC算法,优于其他5种算法。即该算法得到的显著图既具有对比度和图像细节增强,而且具有背景抑制效果更好的优点。

宽带CIC抽取滤波器的一种改进方法

经典CIC抽取滤波器处理宽带信号时幅频响应不理想,满足带外衰减指标时,通带衰减过大,难以满足抗混叠性能要求。针对该问题,提出了一种改善性能的方法,在已有的分级抽取滤波器的基础上利用锐化技术提高滤波器阻带衰减,并利用补偿滤波器在采样率降低后对通带进行额外补偿,减少计算量的同时使带内更平坦。仿真分析表明,改进后的滤波器的通带衰减度均大于0.002 dB,阻带衰减小于57 dB,抗混叠性能优良。

一种组合边缘检测算法实现红外图像增强

红外图像增强技术是夜视技术领域一项重要内容。由于红外热成像系统的输出图像成像模糊且非均匀性明显,研究针对其图像的锐化增强算法具有重大意义。重点探讨了图像增强中的锐化算法,深入分析了现有算法在实现过程中锐化增强与大量引入噪声的矛盾根源,在大量仿真实验基础上,提出了一种有效的改进算法。改进算法取得了良好的实验效果,减弱了图像非均匀性对锐化的影响,较好的解决了图像锐化增强与噪声加重这对矛盾。

基于局部复杂度和方差的自适应图像锐化算法

为解决传统的锐化算法对噪声敏感,易在强边缘处产生伪影效应,且对灰度变化较小的微弱细节锐度不足的问题,提出了一种基于图像局部复杂度和方差的自适应图像锐化算法。考虑了图像局部灰度变化的频率和强度,针对图像灰度剧变区和级别丰富区,应用图像局部复杂度和方差自适应调节增益函数,在背景噪声与原图一致的情况下,有效提升了图像中弱边缘和纹理细节的表现力。仿真结果表明,该算法处理后的图像边缘突出,纹理清晰,较好抑制了伪影的产生和背景噪声的放大。

基于离散小波变换的图像素描生成算法

针对现有的图像素描生成算法生成的图像出现画面不够细腻、素描图像生成效率低下的问题,提出了一种基于离散小波变换(DWT)的图像素描生成算法;首先将真彩色图像转换为灰度图像,通过快速小波正变换,去除低频系数、快速小波反变换等处理提取图像边缘;接着对每点灰度值倒置求反变换,再对图做锐化处理及平滑处理来实现图像的素描效果。实验结果证明:基于离散小波变换的图像素描生成算法快速有效,很好地保留了输入图像的细节特征。

红外热成像技术在亚平面缺陷检测中的应用

根据红外成像无损检测原理,利用机器视觉技术,通过实验将红外热像仪采集的亚表面红外缺陷图像进行一系列的处理,包括滤波降噪,图像增强,边缘提取等,将缺陷检测出来。文中用matlab和Visual C++2010联合编程的方式对红外缺陷进行识别,通过窗口可视化将图像和图像中的缺陷位置,大小提取出来。通过多次实验,本方法能够检测大多的亚平面缺陷,具有较高的应用价值。

基于位图图像锐化处理算法的设计与实现

随着计算机技术的不断发展,数字图像处理也越来越重要,原始的数字化图像包含很多信息,为了进行图像信息的进一步处理,往往需要对原始的数字图像进行后续处理。论文主要针对常见的BMP位图图像,基于拉普拉斯算法设计并实现了一套锐化处理机制,并把该机制应用到具体的社会实际应用中。从实际的实验结果可以得到锐化后的图像在对比度、清晰度以及灰度特征等方面都有明显的改善,非常适合后续的数字图像处理工作,有较强的社会应用价值。

基于拉普拉斯算法的图像锐化算法研究和实现

该文提出了一种基于拉普拉斯算法的图像锐化方法,并在DSP上实现其算法。首先研究拉普拉斯算子锐化图像的基本原理,并推导出图像锐化的拉普拉斯算子。其次,根据拉普拉斯算子,在CCS2软件上运用C语言编写主函数和读取图像数据的Read-image子函数、初始化图像的InitImage子函数和对图像锐化的计算Laplace子函数等子函数来实现基于拉普拉斯算法的图像锐化程序。最后采用三副模糊图像验证图像锐化的效果。比较实验结果,可知运用该算法锐化处理的图像比原来图像清晰。