Wave spectrum estimation conditioned on machine learning-based output using the wave buoy analogy

In this work, a hybrid approach for wave spectrum estimation is proposed. Fundamentally, the approach is based on the wave buoy analogy, processing ship response measurements, via a framework combining machine learning and a physics-based method dependent on available transfer functions. Specifically, a non-parametric (Bayesian) estimate is obtained of the directional wave spectrum conditioned on integral wave parameters established by a convolutional neural network. The developed method is assessed in a case study considering about two years of data obtained from an in-service container ship. The method produces good results, significantly improved when compared to the initial estimate made without constraints.     

多线索融合的结构化道路检测算法研究

为实现结构化道路的检测，提出了一种用于道路检测的激光雷达和视觉融合法，通过提取激光雷达在道路边缘的三维点云信息，将其投影到视觉图像上，形成激光点云与图像的映射关系，生成激光雷达似然图。通过改进提取道路的颜色、纹理、水平线等特征的方法，生成相对应的视觉似然图。在贝叶斯框架下将激光雷达和视觉生成的似然图进行融合。在KITTI数据集上测试可知，精度达到94%，准确率达到86%，表明该道路检测法具有较好的道路检测果。