传统的概率转移矩阵(Probabilistic Transfer Matrix,PTM)方法是一种能够比较精确地估计软差错对门级电路可靠度影响的方法,但现有的方法只适用于组合逻辑电路的可靠度估计.本文提出基于PTM的时序电路可靠度估计方法(reliability estima...传统的概率转移矩阵(Probabilistic Transfer Matrix,PTM)方法是一种能够比较精确地估计软差错对门级电路可靠度影响的方法,但现有的方法只适用于组合逻辑电路的可靠度估计.本文提出基于PTM的时序电路可靠度估计方法(reliability estimation of Sequential circuits based on PTM,S-PTM),先把待评估时序电路划分为输出逻辑模块和次态逻辑模块,然后用本文提出的时序电路PTM计算模型得到电路的PTM,最后根据输入信号的概率分布计算出时序电路的可靠度.用ISCAS 89基准电路为对象进行实验和验证,实验表明所提方法是准确和合理的.展开更多
Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need t...Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term prediction.CKNet adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.展开更多
For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of in...For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of input combinations of the simulator that generates a pre-determined simulator output. Ranjan et al. [1] proposed an expected improvement criterion under a sequential design framework for the inverse problem with a scalar valued simulator. In this paper, we focus on the inverse problem for a time-series valued simulator. We have used a few simulated and two real examples for performance comparison.展开更多
文摘传统的概率转移矩阵(Probabilistic Transfer Matrix,PTM)方法是一种能够比较精确地估计软差错对门级电路可靠度影响的方法,但现有的方法只适用于组合逻辑电路的可靠度估计.本文提出基于PTM的时序电路可靠度估计方法(reliability estimation of Sequential circuits based on PTM,S-PTM),先把待评估时序电路划分为输出逻辑模块和次态逻辑模块,然后用本文提出的时序电路PTM计算模型得到电路的PTM,最后根据输入信号的概率分布计算出时序电路的可靠度.用ISCAS 89基准电路为对象进行实验和验证,实验表明所提方法是准确和合理的.
基金National Natural Science Foundation of China,Grant/Award Numbers:61825305,62003361,U21A20518China Postdoctoral Science Foundation,Grant/Award Number:47680。
文摘Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term prediction.CKNet adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.
文摘For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of input combinations of the simulator that generates a pre-determined simulator output. Ranjan et al. [1] proposed an expected improvement criterion under a sequential design framework for the inverse problem with a scalar valued simulator. In this paper, we focus on the inverse problem for a time-series valued simulator. We have used a few simulated and two real examples for performance comparison.
