A model of the effects of heavy ion radiation on human tissue

In heavy ion radiotherapy and space travel humans are exposed to energetic heavy ions (C, Si, Fe and others). This type of irradiation often produces more severe biological effects per unit dose than more common X-rays. A new Monte Carlo model generates a physical space with the complex geometry of human tissue or a cell culture based model of tissue, which is affected by the passage of ionizing radiation. For irradiation, the model relies on a physical code for the ion track structure; for tissues, cellular maps are derived from two- or three-dimensional confocal microscopy images using image segmentation algorithm, which defines cells as pixilated volumes. The model is used to study tissue-specific statistics of direct ion hits and the remote ion action on cells. As an application of the technique, we considered the spatial pattern of apoptotic cells after heavy ion irradiation. The pattern of apoptosis is modeled as a stochastic process, which is defined by the action cross section taken from available experimental data. To characterize the degree of apoptosis, an autocorrelation function that describes the spatial correlation of apoptotic cells is introduced. The values of the autocorrelation function demonstrate the effect of the directionality of the radiation track on the spatial arrangements of inactivated cells in tissue. This effect is intrinsic only to high linear-energy-transfer radiation.     

风环境下行驶于大跨度桥梁上的车辆安全评价及影响因素研究

建立了风环境下行驶于振荡桥梁上的车辆受到侧向突发阵风时的车辆安全性分析方法.定义了包括侧翻、侧滑和偏转在内的三种风致车辆事故.在综合考虑路面粗糙度、车辆悬挂系统以及车轮相对于桥面侧向相对滑动的基础上,提出了能够考虑桥梁的静风响应、抖振响应、汽车一桥梁耦合振动、系统的时变特性以及结构几何非线性和气动荷载非线性影响的风一汽车一桥梁系统空间耦合分析模型,编制了相应的分析程序.以杭州湾跨海大桥为工程实例,运用所编制的程序研究了路面粗糙度、风速以及干、湿、雪、冰路面状况对行驶于桥梁上的车辆安全性的影响,给出了典型车辆在桥梁上发生事故的临界风速,并与车辆行驶于路面上的相应值进行了对比;对比表明:振荡的桥梁会降低车辆发生事故的临界风速.     

基于小波包分解和FCM聚类的纹理图像分割方法

提出了一种新的图像特征提取中选取最优小波分解树的方法.塔式小波分解对信号解不够全面,而小波包全分解又引入庞大的计算量,因此小波分解最优树的选取尤为重要.结合模糊c均值(FCM,Fuzzy C-Mean)聚类,提出了一种能同时进行小波自适应分解和纹理特征分类的纹理图像分割方法,该方法将无监督聚类中的聚类有效性参数引入到自适应小波分解的判决中,能根据无监督聚类分割的需要,自适应地选取小波包分解的树形结构和分解层数.相对于小波包全分解,节省了大量的运算,并能取得良好的分割效果.      

彩色图像分割算法研究与费歇算法应用

图像分割是图像分析和模式识别需要解决的首要问题和经典难题。彩色图像比灰度图像具有更多的视觉信息,因而受到了越来越多的重视。文中对各种彩色图像分割方法进行了系统论述,分析了各种方法的优缺点,重点介绍了费歇算法。该算法能有效地对彩色图像进行分割,在工业实践中取得了比较好的效果。     

基于人工神经网络的道路表面降雨水深预测

建立了基于人工神经网络的道路表面水膜厚度预测模型,通过试验数据的训练确定权重和阈值,经过检验样本的检验,能够很好地预测道路表面的水膜厚度。结果表明,本文建立的人工神经网络模型用于道路表面水膜厚度预估可行。     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

基于纹理特征多分辨双Markov-GAR模型的SAR图像分割

为了提高SAR图像分割精度,提出在以灰度共生矩阵产生的纹理统计量为特征所生成的图像上,同时考虑SAR图像像素间空间分布特征和局部灰度均值和方差等统计量给出多分辨双Markov框架下的GAR模型,采用多分辨MPM的参数估计方法及对应的无监督分割算法,对SAR图像进行纹理分割。实验结果表明该方法用于一些高分辨SAR图像,与基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,在分割精度上能降低分割时的错分率。     

噪声图象的快速边缘提取与分割

噪声图象的快速边缘提取和分割是实时图象处理中的重要问题。本文提出了一种噪声图象快速边缘提取和分割算法。实验表明,这种方法具有很强的噪声抑制能力,且快速准确。     

基于卷积神经网络的航天复合材料缺陷智能检测

针对传统的缺陷图像识别处理方式存在着准确度与辨识度不足,且处理缺陷种类单一的问题,提出了一种基于Cascade R-CNN和Mask R-CNN的神经网络模型。首先,为了提高缺陷检测的可视化效果和检测准确度,在实例分割卷积网络Mask R-CNN的基础上,结合级联神经网络Cascade R-CNN结构,组合成了新的级联实例分割Cascade Mask R-CNN网络;其次,对组合而成的级联卷积神经网络进行了训练,将训练好的模型对复合材料缺陷图像进行了检测。实验结果表明:检测的平均准确度达到了91.5%,平均置信度达到了97.3%,达到了检测精度的要求。该研究成果可运用于航天复合材料缺陷识别。     

飞机蒙皮机器人化制造曲面分片算法研究

为实现飞机曲面机器人加工的自动轨迹规划,研究了基于模型几何特征的飞机曲面分片算法。通过分析飞机构件加工的工艺参数信息,以表面加工允许的最大曲率或最大偏角为阈值,提出基于近似曲率的三角网格模型分片方法。以三角网格中任意面片为起点,沿网格邻接关系向外拓展联接,形成若干近似平面化的模型分片结构,当超过阈值时则停止拓展,从而得到一整片曲面。通过对飞机CAD模型进行分片,发现采用本文提出方法可以保证分片结果的均匀性,为后期加工轨迹自动规划规划提供保障。