对话状态追踪(DST)是任务型对话系统中一个重要的模块,但现有的基于开放词表的DST模型没有充分利用槽位的相关信息以及数据集本身的结构信息。针对上述问题,提出基于槽位相关信息提取的DST模型SCELDST(SCE and LOW for Dialogue State T...对话状态追踪(DST)是任务型对话系统中一个重要的模块,但现有的基于开放词表的DST模型没有充分利用槽位的相关信息以及数据集本身的结构信息。针对上述问题,提出基于槽位相关信息提取的DST模型SCELDST(SCE and LOW for Dialogue State Tracking)。首先,构建槽位相关信息提取器(SCE),利用注意力机制学习槽位之间的相关信息;然后,在训练过程中应用学习最优样本权重(LOW)策略,在未大幅增加训练时间的前提下,加强模型对数据集信息的利用;最后,优化模型细节,搭建完整的SCEL-DST模型。实验结果表明,SCE和LOW对SCEL-DST模型性能的提升至关重要,该模型在两个实验数据集上均取得了更高的联合目标准确率,其中在MultiWOZ 2.3(Wizard-of-OZ 2.3)数据集上与相同条件下的TripPy(Triple coPy)相比提升了1.6个百分点,在WOZ 2.0(Wizard-of-OZ 2.0)数据集上与AG-DST(Amendable Generation for Dialogue State Tracking)相比提升了2.0个百分点。展开更多
This paper designs a joint controller/observer framework using a state dependent Riccati equation(SDRE)approach for an active transfemoral prosthesis system.An integral state control technique is utilized to design a ...This paper designs a joint controller/observer framework using a state dependent Riccati equation(SDRE)approach for an active transfemoral prosthesis system.An integral state control technique is utilized to design a tracking controller for a robot/prosthesis system.This framework promises a systematic flexible design using which multiple design specifications such as robustness,state estimation,and control optimality are achieved without the need for model linearization.Performance of the proposed approach is demonstrated through simulation studies,which show improvements versus a robust adaptive impedance controller and an extended Kalman filter-based state estimation method.Numerical results confirm the benefits of our method over the above-mentioned approaches with regard to control optimality and state estimation.展开更多
A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with u...A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.展开更多
文摘对话状态追踪(DST)是任务型对话系统中一个重要的模块,但现有的基于开放词表的DST模型没有充分利用槽位的相关信息以及数据集本身的结构信息。针对上述问题,提出基于槽位相关信息提取的DST模型SCELDST(SCE and LOW for Dialogue State Tracking)。首先,构建槽位相关信息提取器(SCE),利用注意力机制学习槽位之间的相关信息;然后,在训练过程中应用学习最优样本权重(LOW)策略,在未大幅增加训练时间的前提下,加强模型对数据集信息的利用;最后,优化模型细节,搭建完整的SCEL-DST模型。实验结果表明,SCE和LOW对SCEL-DST模型性能的提升至关重要,该模型在两个实验数据集上均取得了更高的联合目标准确率,其中在MultiWOZ 2.3(Wizard-of-OZ 2.3)数据集上与相同条件下的TripPy(Triple coPy)相比提升了1.6个百分点,在WOZ 2.0(Wizard-of-OZ 2.0)数据集上与AG-DST(Amendable Generation for Dialogue State Tracking)相比提升了2.0个百分点。
文摘This paper designs a joint controller/observer framework using a state dependent Riccati equation(SDRE)approach for an active transfemoral prosthesis system.An integral state control technique is utilized to design a tracking controller for a robot/prosthesis system.This framework promises a systematic flexible design using which multiple design specifications such as robustness,state estimation,and control optimality are achieved without the need for model linearization.Performance of the proposed approach is demonstrated through simulation studies,which show improvements versus a robust adaptive impedance controller and an extended Kalman filter-based state estimation method.Numerical results confirm the benefits of our method over the above-mentioned approaches with regard to control optimality and state estimation.
基金supported by the National Natural Science Foundation of China(No.20155896025)
文摘A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.
