9307785PWE_pononic

说明：  书名：《PythonCookbook》 3rd Edition 作者：David Beazley, Brian K. Jones 译者：熊能(PythonCookbook, 3rd Edition)

武汉大学测绘校区的《GPS原理与应用》PPT课件，经典-Campus of Wuhan University of Surveying and Mapping " GPS Theory and Application of" PPT courseware, classic! ! !

说明：  QT界面开发，产品研发界面自定义设计、包含自定义设置项，(QT interface development, product development interface custom design, including custom settings.)

说明：  应用分发系统最新版，支持安卓苹果自动识别(The latest version of App Distribution System supports Android Apple automatic recognition)

超声波测距（数码管显示），精确度差不多。。。。(Ultrasound ranging (digital tube display), almost the same accuracy.)

基于MATLAB的细观混凝土骨料模型，可以导入COMSOL中进行数值仿真(The mesoscopic concrete aggregate model based on MATLAB can be imported into COMSOL for numerical simulation.)

说明：  smote dataset for imbalanced data

说明：  Fisher判别，用于模式识别的Fisher线性判别(Fisher discrimination, Fisher linear discrimination for pattern recognition)

在混合高斯背景模型中，认为像素之间的颜色信息互不相关，对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点，其值在序列图像中的变化可看作是不断产生像素值的随机过程，即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰)，多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak))

说明：  裂纹是典型的线结构，在许多计算机视觉应用中都很有趣。在实际应用中，路面裂缝等许多裂缝连续性差、对比度低，给利用低层特征进行基于图像的裂缝检测带来了很大的挑战。在本文中，我们提出了深度裂纹——一个端到端可训练的深度卷积神经网络，通过学习用于裂纹表示的高级特征来自动检测裂纹。该方法将在层次卷积阶段学习到的多尺度深度卷积特征融合在一起，以获取线路结构。更详细的表示在大比例尺的feature maps中进行，更全面的表示在小比例尺的feature maps中进行。我们在SegNet的编码器解码器架构上构建深度裂纹网，并对在相同尺度下在编码器网络和解码器网络中生成的卷积特征进行配对融合。(Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack---an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale.)