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SCO2 cycle
sCO2 cycle simulation.
- 2020-12-16 11:29:12下载
- 积分：1
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Desktop
周期性边界条件的自动施加，啊啊啊啊啊啊啊啊(Automatic imposition of periodic boundary conditions)
- 2020-06-23 07:20:02下载
- 积分：1
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Django测试
Django测试，Django测试Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，Django测试，
- 2022-02-25 03:55:40下载
- 积分：1
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pygis-bukun
说明： Python与开源GIS——数据处理、空间分析与地图制图(Python and open source GIS: data processing, spatial analysis and Cartography)
- 2020-06-12 15:50:31下载
- 积分：1
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用python实现外星人大战游戏
利用python的pygame模块编写了一个外星人大战的游戏
- 2023-04-11 20:00:06下载
- 积分：1
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BN-CNN
说明： 对轴承的时频图进行特征提取与融合，构建轴承健康指示量(Feature extraction and fusion of bearing time-frequency map are carried out to construct bearing health indicator)
- 2020-07-03 20:19:28下载
- 积分：1
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smokeRecognize-master
说明： 利用SVM对烟雾进行分类检测，很方便使用，内有SVM模型(Classification and Detection of Smoke Using SVM)
- 2019-07-08 16:46:03下载
- 积分：1
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CNN手写体识别报告
说明： 人工智能课程作业，TensorFlow中使用CNN实现手写体数字识别，基于CNN实现手写体数字识别并对比MLP分析。文章对代码原理结构有较为详尽的分析和解释，结尾处附有程序完整代码，可在python中直接运行。(Artificial intelligence course assignments, TensorFlow uses CNN to realize handwritten numeral recognition, and CNN to realize handwritten numeral recognition and MLP analysis. This article has a more detailed analysis and explanation of the code principle and structure. At the end of the article, there is a complete program code, which can run directly in python.)
- 2019-06-27 13:54:02下载
- 积分：1
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MachineLearning
说明： 机器学习的鸢尾花数据集的应用逻辑回归算法分类(Classification of iris data set by logical regression algorithm)
- 2021-03-22 13:59:32下载
- 积分：1
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DeepCrack
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.)
- 2021-04-09 16:58:59下载
- 积分：1
