障碍空间中基于网格的不确定数据聚类算法

为了解决已有研究成果无法有效解决动态障碍空间中的不确定数据聚类问题,根据障碍集合是否发生变化,分别解决静态障碍和动态障碍空间下的聚类问题。提出了静态障碍空间中的不确定数据聚类算法(DBSCAN clustering algorithm for static obstacles in grid space,STA_GOBSCAN)、障碍物动态增加情况下的不确定数据聚类算法(DBSCAN clustering algorithm for dynamic increase of obstacles in grid space,DYN_GOCBSCAN)、障碍物动态减少情况下的不确定数据聚类算法(DBSCAN clustering algorithm for dynamicreduction of obstacles in grid space,DYN_GORBSCAN)和障碍物动态移动情况下的不确定数据聚类算法(DBSCAN clustering algorithm for dynamic movement of obstacles in grid space,DYN_GOMBSCAN),采用KL距离对不确定数据进行相似性度量,并利用网格对数据空间进行划分。理论研究和实验结果表明所提出的算法具有较高的效率和准确率。     

基于模糊C均值聚类算法的碳纤维木质复合材料图像分割

引入模糊C均值聚类(FCM)图像分割算法,解决传统方式下图像分割中未考虑临域像素信息的问题,将该法与小波变换综合使用,实现对碳纤维木质复合材料在微观层面的分析。基于改进FCM算法,可以明确聚类中心,提出适用于欧氏距离的计算函数,在此基础上参考邻域像素的信息,实现对像素特征值的合理调整,最终得到科学的目标函数计算方式。由实际结果得知,该算法的应用效果较佳,图像分割边缘保持相对清晰的状态,在抑制噪声方面能力较强,有效减少了迭代次数,因此全程运行效率有所提升。     

连铸圆坯非均匀传热/凝固行为的无网格计算方法

基于移动最小二乘近似和变分原理,推导并建立了基于无网格Galerkin (EFG)法的连铸圆坯二维横截面传热/凝固计算模型。以实测结晶器热流为边界条件,模拟分析了圆坯的非均匀凝固行为,重点讨论了节点布置和支持域尺寸等影响无网格模型适用性和计算精度的关键问题。结果表明,建立的EFG模型在节点布置上十分灵活,提出的直角坐标系下的"同心圆"式节点布置方案,可以很好地处理圆坯曲线边界及坯壳凝固界面推进的问题。在支持域尺寸为平均节点间距的1.7倍时,均匀和随机2种节点布置方式下,模型都达到了较高的计算精度。研究结果证实了EFG模型应用于铸坯非均匀传热、凝固计算的可行性和正确性,在凝固界面追踪中体现出显著优势,为后续铸坯传热-力学行为耦合和裂纹预测的研究提供了基础。     

类圆形区域的分割与统计分析

为了对类圆形区域进行统计分析,文章以彩色粘连药片图像为例,提出并分析了一种类圆形图像的分割与统计分析方法。首先,在预处理后,选择合适的图像分割算法,去除阴影噪声的影响,完整分割出类圆形区域是关键;其次,使用数学形态学处理算法,平滑图像边界和分割后的毛刺;最后,连通域标记并计数。实验结果表明,文章方法准确率更高。文末还分析了算法的一些应用领域。本研究可为其他相关研究提供参考。     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.     

花卉图像识别的设计与研究

在花卉培养和种植领域,往往需要对一些花卉品种进行识别和分类以及监测其生长。这种传统的方法不仅浪费大量的劳动力和物质资源,而且效率极其低下,很大程度上限制了花卉培养和发展。由于不同花朵之间存在相似度,所以使用传统的图像分类方法很困难,因此对花朵进行分类来解决此问题。本文通过从花朵图像中提取子特征信息,并研究可以表达花朵图像中包含的更多信息的识别方法来实现高精度,完成花卉图像的识别,以此辅助花卉鉴定和花卉生长的自动监测,减少花农的工作,提高花农的生产力。     

基于支持向量机模型的地表下沉系数计算参数寻优

为准确快速地对地表下沉系数进行预测分析,提出基于支持向量机(SVM)的地表下沉系数的SVM预测模型,结合我国典型的地表移动观测站实际案例,分别采用网格参数寻优算法、遗产算法(GA)、粒子群算法(PSO)对SVM模型进行参数寻优,并采用考虑交叉概率的改进GA算法对地表下沉系数进行预测.结果表明,改进的GA算法预测回归系数可达到0.95271,适合地表下沉系数预测分析,预测准确率最高.     

纸质简谱图像的分割、倾斜校正及音符歌词提取

乐谱图像的自动分割、倾斜校正是乐谱识别过程中的关键技术,各种计算机光学乐谱识别技术在乐谱图像的数字化中有着广泛的应用,但对于乐谱中简谱的识别一直鲜有研究.本文针对人工拍摄条件下光照不理想的简谱图像,提出一种基于PCNN(脉冲耦合神经网络)和DNN(深度神经网络)相结合的分块简谱图像自动分割算法,该方法根据简谱图像灰度分布特征对图像进行自适应分块处理,依据每个分块的灰度特征与PCNN最佳迭代次数之间的关系构造合适的DNN神经网络,从而实现了最优分割图的自适应选取;进一步利用最优分割图像中音符小节线的水平投影,提出一种双尺度下降法实现了简谱图像的倾斜校正;提出去边垂直投影法和连通域距离判断法实现了简谱图像中音符及歌词的提取.实验仿真结果表明:本文算法对复杂光照条件下的简谱图像处理都具有较好的鲁棒性,同时表现出更高的效率.