[1]张峰峰,孙立宁,杜志江,等.基于BP神经网络的虚拟手术实时仿真技术研究[J].郑州大学学报(工学版),2007,28(04):43-46.[doi:10.3969/j.issn.1671-6833.2007.04.011]
 ZHANG Fengfeng,SUN Lining,Du Zhijiang,et al.Research on real-time simulation technology of virtual surgery based on BP neural network[J].Journal of Zhengzhou University (Engineering Science),2007,28(04):43-46.[doi:10.3969/j.issn.1671-6833.2007.04.011]
点击复制

基于BP神经网络的虚拟手术实时仿真技术研究()
分享到:

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
28卷
期数:
2007年04期
页码:
43-46
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Research on real-time simulation technology of virtual surgery based on BP neural network
作者:
张峰峰孙立宁杜志江等.
哈尔滨工业大学,机器人技术与系统国家重点实验室,黑龙江,哈尔滨,150001, 哈尔滨工业大学,机器人技术与系统国家重点实验室,黑龙江,哈尔滨,150001, 哈尔滨工业大学,机器人技术与系统国家重点实验室,黑龙江,哈尔滨,150001, 哈尔滨工业大学,机器人技术与系统国家重点实验室,黑龙江,哈尔滨,150001
Author(s):
ZHANG Fengfeng; SUN Lining; Du Zhijiang; etc
关键词:
BP神经网络 虚拟手术仿真 有限元模型 医疗机器人
Keywords:
BP neural networks Virtual surgical simulation finite element model Medical robots
DOI:
10.3969/j.issn.1671-6833.2007.04.011
文献标志码:
A
摘要:
在人体腿部的虚拟仿真研究中,建立有限元模型并进行生物力学特性分析是一种有效的方法.但由于有限元模型数据量大,解算时间长,并且难以与既有系统融合,因此不适合在实时的手术培训和手术预演中应用.为了提高虚拟手术仿真系统进行实际作业的能力,笔者提出了以BP神经网络模型来代替有限元模型,实现实时的生物力学响应.并结合已有的医疗机器人辅助接骨虚拟现实仿真手术系统,构建了系统实验平台.实验结果证明,人体腿部的BP神经网络模型能够完全满足手术仿真所需的实时性要求.
Abstract:

In the virtual simulation study of human legs, it is an effective method to establish a finite element model and analyze the biomechanical characteristics. However, due to the large amount of data, long solution time, and difficulty in integrating with existing systems, the finite element model is not suitable for real-time surgical training and surgical rehearsal. In order to improve the ability of the virtual surgical simulation system to carry out actual operations, the author proposes to replace the finite element model with the BP neural network model to achieve real-time biomechanical response. Combined with the existing medical robot-assisted osteopathic virtual reality simulation surgery system, a system experimental platform was constructed. Experimental results show that the BP neural network model of human legs can fully meet the real-time requirements required for surgical simulation.

相似文献/References:

[1]刘广瑞,周文博,田欣,等.多传感器信息融合在焊接质量控制中的应用[J].郑州大学学报(工学版),2017,38(05):28.[doi:10.13705/j.issn.1671-6833.2017.02.025]
 Liu Guangrui,Zhou Wenbo,Tian Xin,et al.Application of Information Fusion in Welding based on Arc and Ultrasonic sensor[J].Journal of Zhengzhou University (Engineering Science),2017,38(04):28.[doi:10.13705/j.issn.1671-6833.2017.02.025]
[2]蔡婉贞,黄 翰.基于 BP-RBF神经网络的组合模型预测港口物流需求研究[J].郑州大学学报(工学版),2019,40(05):84.[doi:10.13705/j.issn.1671-6833.2019.02.025]
 Cai Wanzhen,Huang Han.Research on Port Logistics Demand Forecasting Based on Combination Model of BP-RBF Neural Network[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):84.[doi:10.13705/j.issn.1671-6833.2019.02.025]
[3]孙国栋,江亚杰,徐亮,等.BP网络预测阈值的仪表重影字符识别方法研究[J].郑州大学学报(工学版),2020,41(04):28.[doi:10.13705/j.issn.1671-6833.2020.04.011]
 Sun Guodong,Jiang Yajie,Xu Liang,et al.Study on Instrument ghosting character recognition method for predicting threshold by BP network[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):28.[doi:10.13705/j.issn.1671-6833.2020.04.011]
[4]冯冬青,郭艳..遗传算法改进BP神经网络在地下水水质评价中的应用[J].郑州大学学报(工学版),2009,30(03):126.[doi:10.3969/j.issn.1671-6833.2009.03.032]
 Feng Dongqing,Guo Yan.Genetic algorithm improves the application of BP neural network in groundwater quality assessment[J].Journal of Zhengzhou University (Engineering Science),2009,30(04):126.[doi:10.3969/j.issn.1671-6833.2009.03.032]
[5]李清富,邓宇,杜卫兵..碳纤维布加固钢纤维混凝土梁的抗弯性能预测[J].郑州大学学报(工学版),2004,25(04):1.[doi:10.3969/j.issn.1671-6833.2004.04.001]
 LI Qingfu,DENG Yu,Du Weibing.Prediction of bending performance of carbon fiber reinforced steel fiber concrete beams with carbon fiber cloth[J].Journal of Zhengzhou University (Engineering Science),2004,25(04):1.[doi:10.3969/j.issn.1671-6833.2004.04.001]

更新日期/Last Update: 1900-01-01