基于自适应自编码器假人力学响应降维和重构方法

Dimensionality reduction and reconstruction method of dummy biomechanics response based on adaptive autoencode

为了解决碰撞假人力学响应曲线的降维和重构问题,基于标准自编码器原理和假人力学响应曲线特征,添加限制条件,构建了自适应自编码器方法;选取假人的头部重心合成加速度曲线数据,经过数据清洗和取样后,作为样本数据;计算标准自编码器和自适应自编码器的互相关系数和重构均方误差,对比验证该自编码器的线性和非线性降维和重构能力。结果表明:该自编码器对假人力学响应数据的线性的升维重构误差为2.6%;对非线性的升维重构误差为2.4%;低维数据的协方差值接近于0。因此,本文所提方法可对假人力学响应数据实施线性和非线性降维,并可实现升维重构,且降维得到的低维数据具有强独立性。

A adaptive autoencoder was proposed to solve the problem of reducing and reconstructing the biomechanics response curve of collision dummy.The adaptive autoencoder method was constructed based on the standard autoencoder principle and the dummy biomechanics-response-curve characteristics with adding some constraints.The synthetic acceleration curve data were selected as sample data after data cleaning and sampling for the dummy-head gravity-center.The correlation number and the reconstruction mean square error of the standard autoencoder and the adaptive autoencoder are calculated;while the linear and nonlinear reduction and reconstruction ability were compared and verified for the adaptive autoencoder.The results show that the adaptive autoencoder has a reconstruction error of 2.6%for linear dimensionality and 2.4%for nonlinear dimensionality,while the covariance value is close to 0 for the low-dimensional data.Therefore,the adaptive autoencoder proposed in this paper implements linear and nonlinear dimensionality reduction and dimensionality reconstruction with a highly independent for the low-dimensional data.