求解线性规划的宽邻域不可行内点算法

提出了一个求解线性规划的不可行内点算法.该算法的特点是:一方面使用了宽邻域,因此数值实验表明具有较好的计算效果;另一方面,通过分析获得它的多项式复杂度为O(n~(1.5)L),这是宽邻域不可行内点算法的最好复杂度.     

病态线性方程组的新解法:误差转移法

提出了一种求解病态线性方程组的简便有效的新算法，它的主要思想是将直接求解法中的计算误差转移到一个中间量上，从而使得最终解获得很好的精度，因此可极大地缓解一般算法条件预优的困难以及病态方程组的求解难度。数值计算的结果表明，算法对极其病态的线性方程组也可获得较好的精度和稳定性。     

人脸表情识别中滤波算法的研究

去噪滤波技术是数字图像处理中的一个重要内容,是计算机视觉技术、模式识别、图像数据分析的基础,因而受到广泛的研究;并在人脸识别、表情识别、遥感成像等领域获得广泛的应用。通过对多种去噪滤波算法进行比较,选定了一种改进的二维中值滤波快速算法将其应用到表情识别中,得到较好效果。     

基于稀疏表示的近邻传播聚类算法

借助稀疏表示具有能较好刻画样本之间相似度的特点,提出一种基于稀疏表示的近邻传播聚类算法.仿真 实验表明,本聚类算法较基于其它距离度量的算法能获得更好的聚类效果.     

基于形态学重建和极大值标记的分水岭分割算法

针对传统分水岭算法的过分割问题,结合形态学重建和极大值标记技术研究并实现一种新算法。该算法首先进行开闭重建预处理,较好地去除了各类噪声并保留目标原有的结构;然后进行局部极大值调整及标记,有效地避免了过分割;最后对局部极大值图像进行分水岭分割,取得了较好的实验效果。论文还探讨该算法的关键因素即结构元素的选择问题,经大量实验得出结论——选择与目标图像形状相似、大小相宜的结构元素可获得较好的分割效果。     

几种图象分割算法在棉铃虫图象处理中的应用

本文介绍了6种图象分割算法在棉铃虫图象分割中的应用。结果表明，平均值分割算法和迭代阈值分割算法能够获得较好的分割结果，其中迭代法分割结果较符合实际需要。而P－参数法虽最终能获得较好的分割结果，但需要人为干预阈值的选择过程；Johannsen方法能够正确分割出棉铃虫区域，但无法反映棉铃虫的斑纹特征；而Kapur法和Yager方法则将棉铃虫区域的很多内容分割为背景区域，难以反映出棉铃虫实际特征，本研究为进行昆虫图象的特征提取、特征测量及种类自动识别研究奠定了基础。     

近似三对角Toeplitz方程组的并行化实现

近似三对角Toeplitz矩阵在时间序列分析、线性逼近、图论的特征值分析等多个领域经常出现。本文对近似三角Toeplitz矩阵求解算法的串、并行算法进行了分析,并在流处理器平台上实现了算法的并行化,实验结果说明并行算法在流处理器平台上获得较好的加速。     

BP神经网络算法在土地覆盖分类中的应用研究——以新疆于田绿洲为例

BP神经网络算法结构简单、快捷,与传统的分类方法相比,分类精度较高,能较好地反映土地覆盖的分类结果.通过多年实地景观照片库结合土地利用图等地理辅助数据,采用BP神经网络算法对于田绿洲1999年和2002年两期ETM遥感影像进行土地覆盖分类.结果表明:地理辅助数据参与下的BP神经网络用于土地覆盖分类研究可获得较好的分类结果,平均分类精度可达到90;以上.通过精度分析认为神经网络算法在遥感图像分析与处理技术中具有很大的应用潜力.     

一种新颖的半脆弱图像水印认证技术

提出一种新颖的图像水印认证算法,利用量化的方法可以获得被保护图像的指纹信息,这部分信息被用来对给定图像进行认证．指纹信息的嵌入和抽取并不会影响到图像的质量．实验表明,该算法具有较好的图像认证能力,能够识别大多数对被保护图像进行的攻击．     

共享模式下物流联运的优化算法

在物流资源共享的背景下,物流联运优化是一个现代物流业急需解决的一个问题,本文基于图论提出一种优化算法,将物流联运网络定义成一种得到加权的网络。通过江苏省物流联运的实证分析,对本文算法与ABC算法、GA算法、BBO算法等同类型算法进行对比,在物流联运总运输量为10万t时,得到以下结果:本文算法优化后的物流联运平均成本最低,平均成本优化有较好的收敛特征。实验结果验证了本文算法的有效性。     

基于Kd树改进的高效K-means聚类算法

针对经典的K-means算法在多维数据聚类效率上还有待提高的问题，本文提出一种称为CK-means的改进聚类算法。该算法在k-means算法的基础上，通过引入Kd树空间数据结构，初始聚类中心从多维数据某一维的区间等间隔集中选取，以及在数据对象分配过程中采用剪枝策略来提高算法的运行效率。实验结果表明，CK-means聚类算法较经典的k-means聚类算法运行效率更高。     

基于孤立点和初始质心选择的κ-均值改进算法

介绍了在聚类中广泛应用的经典κ-均值算法,针对其随机选择初始质心和易受孤立点的影响的不足,给出了一种改进的κ-均值算法。首先使用距离法移除孤立点,然后采用邻近吸收法对初始质心的选择上进行了改进,并做了改进前后的对比试验。试验结果表明,改进后的算法比较稳定、准确,受孤立点和随机选择质心的影响也有所降低。     

基于Lab颜色空间下的小麦赤霉病图像分割

针对智能识别小麦赤霉病方法中分割患病麦穗图像效果不佳的问题,运用中值滤波方法对患赤霉病麦穗图像进行降噪预处理,采用基于阈值的最大类间方差算法(OTSU)、基于聚类的k-means算法在RGB、HSV和Lab颜色空间中对小麦扬花期到黄熟期感染赤霉病的麦穗图像进行分割,提取出麦穗的病害部分。采用试验田环境下扬花期到黄熟期200张患赤霉病的麦穗图像进行分割试验,结果表明:将图像从RGB颜色空间转化为Lab颜色空间并对a分量采用最大类间方差算法(OTSU)进行分割的效果最佳,误分率仅有1.11%。     

基于YOLOV4模型的果园樱桃实时检测研究

为解决在自然环境下对樱桃不同生长时期的状态监测受环境影响存在目标识别困难、检测准确率低的问题,提出了一种基于CSPDarknet53改进的卷积神经网络樱桃分类检测模型。经典YOLOV4所使用的特征提取网络层数较深,能够提取更高级的抽象特征,但是对目标局部感知能力较弱,通过在CSPDarknet53网络结构上融合CBAM注意力机制,增强了目标局部特征感知能力,进一步提升目标检测精度,其特征提取和目标检测能力优于原算法,调整特征提取网络的特征层输出,将第三层输出变为第二层输出以增加小目标语义信息的获取,利用k-means算法优化先验框尺寸以适应樱桃目标大小,并进行了消融实验分析。结果表明,改进的YOLOV4樱桃检测模型模型的平均精度达到了92.31%,F1分数达到了87.3%,优于Faster RCNN、YOLOV3和原来的YOLOV4算法,检测速度为40.23幅·s^-1,适用于自然环境下的樱桃监测,为实现果园水果生长状态自动监测提供了理论和技术基础。     

基于日较差K均值聚类的逐时温度预报

采用K均值聚类分析方法,将阜新蒙古族自治县气象局观测站2006～2012年1月的气温日较差进行分类,以不同的气温日较差分型代表不同的天气状况,并统计了相应分型下的逐小时温度变化幅度,在此基础上对未来逐时温度做出客观预报。实况证明该方法对于实现时间上的精细预报,具有一定的参考价值。     

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover (FVC) is an important parameter to measure crop growth. In studies of crop growth monitoring, it is very important to extract FVC quickly and accurately. As the most widely used FVC extraction method, the photographic method has the advantages of simple operation and high extraction accuracy. However, when soil moisture and acquisition times vary, the extraction results are less accurate. To accommodate various conditions of FVC extraction, this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index (NDVI) greyscale image of wheat by using a density peak k-means (DPK-means) algorithm. In this study, Yangfumai 4 (YF4) planted in pots and Yangmai 16 (Y16) planted in the field were used as the research materials. With a hyperspectral imaging camera mounted on a tripod, ground hyperspectral images of winter wheat under different soil conditions (dry and wet) were collected at 1 m above the potted wheat canopy. Unmanned aerial vehicle (UAV) hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera. The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat, and the extraction effects of the two methods were compared and analysed. The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered, while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated. The absolute values of error were 0.042 and 0.044, the root mean square errors (RMSE) were 0.028 and 0.030, and the fitting accuracy R² of the FVC was 0.87 and 0.93, under dry and wet soil conditions and under various time conditions, respectively. This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.     

混合颜色特征下番茄叶霉病病斑双层K-means聚类分割方法

提出了混合颜色特征下双层K-means聚类分割方法,首先在I分量将图像像素采用K-means聚类分割方法聚为4类;取聚类中心最大的2种像素的a*b*分量,进行第2次K-means聚类,得到病斑图像。对采集的21幅病害图片的试验结果表明,该方法分割结果的平均重合系数为97.53%,平均假阳性率为1.22%,平均假阴性率为3.52%。该研究可为进一步病害特征提取识别与病害程度诊断研究提供技术参考。     

High-throughput mapping of a dynamic signaling network in mammalian cells

Signaling pathways transmit information through protein interaction networks that are dynamically regulated by complex extracellular cues. We developed LUMIER (for luminescence-based mammalian interactome mapping), an automated high-throughput technology, to map protein-protein interaction networks systematically in mammalian cells and applied it to the transforming growth factor-beta (TGFbeta) pathway. Analysis using self-organizing maps and k-means clustering identified links of the TGFbeta pathway to the p21-activated kinase (PAK) network, to the polarity complex, and to Occludin, a structural component of tight junctions. We show that Occludin regulates TGFbeta type I receptor localization for efficient TGFbeta-dependent dissolution of tight junctions during epithelial-to-mesenchymal transitions.     

基于简化基因组测序的越橘杂交后代鉴定

[目的]针对遗传群体测序数据开发一种杂交后代鉴定方法,以获得继承双亲基因的真杂种后代,为果树杂交育种、遗传分析及遗传图谱构建奠定基础.[方法]本研究以越橘正反交群体共计318个F1子代和2个亲本为试材,利用SLAF技术进行简化基因组测序并比对越橘参考基因组获得群体SNP数据,通过稀有等位变异分析和基于PCA、K-mea...     

Oenological significance of vineyard management zones delineated using early grape sampling

Early definition of oenologically significant zones within a vineyard is one of the main goals of precision viticulture, as it would allow an increase in profitability through the adaptation of agronomic practices to the specific requirements of each zone, and/or segregation of the harvest into different batches to produce wines with different qualities. The aim of this work was to evaluate whether early grape sampling is a relevant tool for within-vineyard zone definition. The study was carried out in 2010 and 2011 in a 4.2 ha vineyard, where a grid of 60 sampling points was defined. 300-berry samples were picked from each sampling point after veraison and at harvest, post-veraison information being used to define zones within the vineyard after fuzzy k-means analysis and subsequent application of a zoning procedure that took into account membership degree and neighbourhood criteria. Two variations of the zoning procedure were used, standard (StdZ) and top (TopZ) zoning. Each was designed to meet different requirements of wineries; StdZ gave the same oenological relevance to all the zones, and TopZ differentiated the zones producing “top class” grapes, minimizing the within-zone variability in the top-class zone. Grape composition obtained at harvest from the zones delineated post-veraison was compared. Zone delineation using post-veraison data was proved to be oenologically relevant, provided sampling is performed once veraison is completed. The two zoning algorithms designed were shown to be suitable for objective zone delineation according to the goals intended for each.