[1]胡雷,邱运军,王熙照,等.面向调线调坡的点云大数据分析及深度模型研究[J].智能系统学报,2020,15(4):795-803.[doi:10.11992/tis.201911027]
 HU Lei,QIU Yunjun,WANG Xizhao,et al.Point cloud big data analysis and deep model research for line and slope fine-tuning[J].CAAI Transactions on Intelligent Systems,2020,15(4):795-803.[doi:10.11992/tis.201911027]
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面向调线调坡的点云大数据分析及深度模型研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年4期
页码:
795-803
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2020-10-30

文章信息/Info

Title:
Point cloud big data analysis and deep model research for line and slope fine-tuning
作者:
胡雷1 邱运军2 王熙照1 张志轶3
1. 深圳大学 计算机与软件学院,广东 深圳 518061;
2. 中建南方投资有限公司,广东 深圳 518022;
3. 中建轨道电气化工程有限公司,北京 100089
Author(s):
HU Lei1 QIU Yunjun2 WANG Xizhao1 ZHANG Zhiyi3
1. Department of Computer Science & Software Engineering, Shenzhen University, Shenzhen 518061, China;
2. China Construction South Investment Co., Ltd., Shenzhen 518022, China;
3. China Construction Railway Electrification Engineering Co., Ltd., Beijing 100089, China
关键词:
实际隧道理论隧道偏差点云大数据侵限值设计线路深度学习梯度下降极值
Keywords:
actual tunneltheoretical tunneldeviationpoint cloud big datainvasion limit valuedesign linedeep learninggradient descentextreme value
分类号:
TP391
DOI:
10.11992/tis.201911027
摘要:
已建成的隧道与原始的设计隧道之间的偏差信息对于地铁线路的安全调整非常重要。然而,目前还没有明确的数学公式能够准确地描述和度量这个偏差。目前主流的做法是通过人工测量具有相同间隔的截面的侵限值,并对这些侵限值进行累加求和,最终得到该偏差,这种方式存在误差大、耗时、成本高等缺点。为了解决这些问题,提出了一种新的基于深度神经网络的偏差表示方法,其能够基于点云大数据学习到设计线路的参数与侵限值之间的内在联系,进而预测出能够使得侵限值的和最小的参数,这些参数可以被用来辅助地铁线路的安全调整。在一个采集于实际地铁工程中的数据集上的实验结果表明,该方法能快速地计算出合适的调线调坡方案,并且只需要很少的计算机内存资源。
Abstract:
The deviation information between the completed tunnel and the originally designed tunnel is very important for the safety adjustment of metro lines. However, there is no clear mathematical formula that can be used to accurately describe and measure the deviation. At present, the mainstream approach is to measure the invasion value of each section with the same interval manually and then sum up these values to get the deviation. This method has the disadvantages of large error, time-consuming and high cost. To solve these problems, a novel deviation representation method based on deep neural network is proposed, which can learn the internal relationship between the parameters of the designed tunnel and the invasion values based on the point cloud data, and then predict the parameters that can make the sum of the invasion values minimum. These parameters can be used to assist the safety adjustment of metro lines. The experimental results on a data set collected from a real subway project show that the proposed method can quickly obtain the appropriate adjustment scheme of the lines and slopes with only a small amount of computer memory resources.

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备注/Memo

备注/Memo:
收稿日期:2019-11-19。
基金项目:国家自然科学基金项目(61976141,61732011)
作者简介:胡雷,硕士研究生,主要研究方向为机器学习、深度学习。申请专利软件著作权2项;邱运军,高级工程师,主要研究方向为轨道交通接触网、供电与轨道系统。申请专利2项,参与城市轨道交通工程设备安装指南、施工作业操作手册及施工安全预控等多本著作的编著;王熙照,教授,博士生导师,Machine Learning and Cybernetics主编,中国人工智能学会常务理事、知识工程专委会主任、机器学习专委会副主任,IEEE-SMC计算智能专委会主席,主要研究方向为不确定性建模和面向大数据的机器学习。主持完成国家自然科学基金等项目30余项,担任多个国际/国内学术会议的大会或程序主席,创办的机器学习与控制国际会议 (ICMLC)已持续18年。深圳市海外高层次人才,曾获省级自然科学一等奖和吴文俊人工智能自然科学一等奖,曾获全国模范教师称号。发表学术论文200余篇,出版学术专著3部、教材2部
通讯作者:王熙照.E-mail:xzwang@szu.edu.cn
更新日期/Last Update: 2020-07-25