在长期时间序列预测(long-term time series forecasting,LTSF)领域,采用编码器-解码器架构的深度学习模型展现出了卓越的性能。目前,虽然编码器从输入的历史序列中能够提取深层次的时间变化特征,捕获时间序列内部的周期性、趋势性以及...在长期时间序列预测(long-term time series forecasting,LTSF)领域,采用编码器-解码器架构的深度学习模型展现出了卓越的性能。目前,虽然编码器从输入的历史序列中能够提取深层次的时间变化特征,捕获时间序列内部的周期性、趋势性以及局部信息相关性,但是解码器多依赖于编码器输出的全局特征,对编码器提取的局部特征利用不充分,限制了模型的预测性能。为充分挖掘和利用局部特征,提出了一种多局部增强线性解码器(multiple local augmented linear decoders,MLAD),通过引入局部特征增强机制(local feature augmented mechanism,LFAM),在编码器生成的特征序列上进行滑动,并将提取的局部特征与原始历史序列融合,从而生成多个局部增强序列,然后通过计算所有的局部增强序列对应预测序列的平均值来确定预测结果。在7个公开数据集上进行实验,结果表明MLAD达到或超过了现有主流模型性能,证明了其在LTSF任务中的有效性。展开更多
A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorize...A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorized seat belt system provides functions to protect passengers and improve passenger's convenience. Each MSB function has its own required belt tension which is determined by the function's purpose. To realize the MSB functions, state information, such as seat belt winding velocity and seat belt tension are required. Using a linear state observer, the state information for MSB operations can be estimated without sensors. To design the linear state observer, the motorized seat belt system is analyzed and represented as a state space model which contains load torque as an augmented state. Based on the state space model, a linear state observer was designed and verified by experiments. Also, the retraction control of the MSB algorithm using linear state observer was designed and verified on the test bench. With the designed retraction control algorithm using the linear state observer, it is possible to realize various types of MSB functions.展开更多
文摘在长期时间序列预测(long-term time series forecasting,LTSF)领域,采用编码器-解码器架构的深度学习模型展现出了卓越的性能。目前,虽然编码器从输入的历史序列中能够提取深层次的时间变化特征,捕获时间序列内部的周期性、趋势性以及局部信息相关性,但是解码器多依赖于编码器输出的全局特征,对编码器提取的局部特征利用不充分,限制了模型的预测性能。为充分挖掘和利用局部特征,提出了一种多局部增强线性解码器(multiple local augmented linear decoders,MLAD),通过引入局部特征增强机制(local feature augmented mechanism,LFAM),在编码器生成的特征序列上进行滑动,并将提取的局部特征与原始历史序列融合,从而生成多个局部增强序列,然后通过计算所有的局部增强序列对应预测序列的平均值来确定预测结果。在7个公开数据集上进行实验,结果表明MLAD达到或超过了现有主流模型性能,证明了其在LTSF任务中的有效性。
基金Project supported by the Second Stage of Brain Korea 21 Projects and Changwon National University in 2011-2012
文摘A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorized seat belt system provides functions to protect passengers and improve passenger's convenience. Each MSB function has its own required belt tension which is determined by the function's purpose. To realize the MSB functions, state information, such as seat belt winding velocity and seat belt tension are required. Using a linear state observer, the state information for MSB operations can be estimated without sensors. To design the linear state observer, the motorized seat belt system is analyzed and represented as a state space model which contains load torque as an augmented state. Based on the state space model, a linear state observer was designed and verified by experiments. Also, the retraction control of the MSB algorithm using linear state observer was designed and verified on the test bench. With the designed retraction control algorithm using the linear state observer, it is possible to realize various types of MSB functions.
