带噪声奇偶学习问题(learning parity with noise,LPN)是密码学中的一类重要困难问题,它可以视作随机线性码译码问题的一般形式,是抗量子假设中的有力候选.在求解LPN问题前,通常需要执行约简操作,将待求实例转化为秘密长度更短的实例....带噪声奇偶学习问题(learning parity with noise,LPN)是密码学中的一类重要困难问题,它可以视作随机线性码译码问题的一般形式,是抗量子假设中的有力候选.在求解LPN问题前,通常需要执行约简操作,将待求实例转化为秘密长度更短的实例.本文提出了一种新的混合约简算法Hybrid,它将经典的丢弃约简算法和覆盖码约简算法相结合,在约简过程中丢弃与码字距离超过给定界限的LPN样本,而非将所有样本直接近似到码字.这种新的约简方法可以到达权衡样本复杂度和时间复杂度的目的.从算法层面讲,丢弃约简与覆盖码约简可以视作混合约简的特例.最后,使用池化高斯算法求解经过混合约简后的LPN样本,给出了其完整的理论复杂度.数值估计的结果表明混合约简可以进一步缩减良好池化高斯算法(Well-Pooled Gauss)的样本复杂度,但需要以时间开销上升为代价.展开更多
In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel...In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.展开更多
文摘带噪声奇偶学习问题(learning parity with noise,LPN)是密码学中的一类重要困难问题,它可以视作随机线性码译码问题的一般形式,是抗量子假设中的有力候选.在求解LPN问题前,通常需要执行约简操作,将待求实例转化为秘密长度更短的实例.本文提出了一种新的混合约简算法Hybrid,它将经典的丢弃约简算法和覆盖码约简算法相结合,在约简过程中丢弃与码字距离超过给定界限的LPN样本,而非将所有样本直接近似到码字.这种新的约简方法可以到达权衡样本复杂度和时间复杂度的目的.从算法层面讲,丢弃约简与覆盖码约简可以视作混合约简的特例.最后,使用池化高斯算法求解经过混合约简后的LPN样本,给出了其完整的理论复杂度.数值估计的结果表明混合约简可以进一步缩减良好池化高斯算法(Well-Pooled Gauss)的样本复杂度,但需要以时间开销上升为代价.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.
