采用芬顿高级氧化法对电石法PVC聚合釜洗釜废水进行了小试试验。结果表明:在pH值为3.5,反应时间为60 min,H 2 O 2∶COD(质量比)=1∶1,H 2 O 2∶Fe 2+(摩尔比)=3∶1的试验条件下,并兼顾COD去除率及运行成本的情况下,处理后的废水生物毒...采用芬顿高级氧化法对电石法PVC聚合釜洗釜废水进行了小试试验。结果表明:在pH值为3.5,反应时间为60 min,H 2 O 2∶COD(质量比)=1∶1,H 2 O 2∶Fe 2+(摩尔比)=3∶1的试验条件下,并兼顾COD去除率及运行成本的情况下,处理后的废水生物毒性得到大幅度降低,可生化性得到显著提升。后续采用以生化为主并引入其他保障处理工艺,最终出水COD可稳定在30 mg/L以下,各项指标满足厂内循环水冷却水回用要求。展开更多
在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不...在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不够理想。对此,本文先通过霍夫变换将法向空间与二维平面相对应,并将二维空间离散化获得所有潜在切平面,设计特征聚合将点特征转化为潜在切平面特征作为CNN输入来降低霍夫变换过程中信息的损失,从而提升卷积神经网络的输入,进而提升网络整体的法向估计质量。实验结果表明,由此产生的法向估计网络的整体性能有所提升,对于不同噪声尺度也更具鲁棒性。In the process of point cloud processing, normal estimation is a very important step. The existing normal estimation network of deep Hough transform obtains neighborhood features by performing Hough transform on the point cloud and then inputs them into a convolutional neural network to learn and estimate the normal. However, due to certain information loss in the Hough transform process, the finally obtained normal is inaccurate and the effect is not ideal. In response to this, this paper first corresponds the normal space to a two-dimensional plane through Hough transform, and discretizes the two-dimensional space to obtain all potential tangent planes. Feature aggregation is designed to transform point features into potential tangent plane features as the input of CNN to reduce the information loss in the Hough transform process, thereby enhancing the input of the convolutional neural network and further improving the overall normal estimation quality of the network. Experimental results show that the overall performance of the resulting normal estimation network is improved, and it is also more robust to different noise scales.展开更多
文摘采用芬顿高级氧化法对电石法PVC聚合釜洗釜废水进行了小试试验。结果表明:在pH值为3.5,反应时间为60 min,H 2 O 2∶COD(质量比)=1∶1,H 2 O 2∶Fe 2+(摩尔比)=3∶1的试验条件下,并兼顾COD去除率及运行成本的情况下,处理后的废水生物毒性得到大幅度降低,可生化性得到显著提升。后续采用以生化为主并引入其他保障处理工艺,最终出水COD可稳定在30 mg/L以下,各项指标满足厂内循环水冷却水回用要求。
文摘在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不够理想。对此,本文先通过霍夫变换将法向空间与二维平面相对应,并将二维空间离散化获得所有潜在切平面,设计特征聚合将点特征转化为潜在切平面特征作为CNN输入来降低霍夫变换过程中信息的损失,从而提升卷积神经网络的输入,进而提升网络整体的法向估计质量。实验结果表明,由此产生的法向估计网络的整体性能有所提升,对于不同噪声尺度也更具鲁棒性。In the process of point cloud processing, normal estimation is a very important step. The existing normal estimation network of deep Hough transform obtains neighborhood features by performing Hough transform on the point cloud and then inputs them into a convolutional neural network to learn and estimate the normal. However, due to certain information loss in the Hough transform process, the finally obtained normal is inaccurate and the effect is not ideal. In response to this, this paper first corresponds the normal space to a two-dimensional plane through Hough transform, and discretizes the two-dimensional space to obtain all potential tangent planes. Feature aggregation is designed to transform point features into potential tangent plane features as the input of CNN to reduce the information loss in the Hough transform process, thereby enhancing the input of the convolutional neural network and further improving the overall normal estimation quality of the network. Experimental results show that the overall performance of the resulting normal estimation network is improved, and it is also more robust to different noise scales.
