随着信息技术的快速发展,数据安全问题日益受到重视。本文提出了一种可验证的秘密分享方案,该方案基于Shamir秘密分享方案,并结合NTRU数字签名算法,增强方案的安全性。NTRU数字签名算法作为一种能够抵抗量子攻击的数字签名算法,有效防御了伪造和篡改攻击,确保了秘密恢复过程的可信度。本文详细分析了方案的正确性和安全性。With the rapid development of information technology, data security issues are increasingly being taken seriously. This paper proposes a verifiable secret sharing scheme based on the Shamir secret sharing scheme and combined with the NTRU digital signature algorithm to enhance the security of the scheme. The NTRU digital signature algorithm, as a type of digital signature algorithm capable of resisting quantum attacks, effectively defends against forgery and tampering attacks, ensuring the credibility of the secret recovery process. This paper provides a detailed analysis of the correctness and security of the scheme.
为了实现实时、高效、精准的锂电池健康状态(state of health,SOH)估计,提出一种基于恒压充电片段和灰狼算法(grey wolf optimizer,GWO)优化卷积神经网络(convolutional neural network,CNN)的SOH估计方法。通过提取部分恒压充电过程中...
详细信息
为了实现实时、高效、精准的锂电池健康状态(state of health,SOH)估计,提出一种基于恒压充电片段和灰狼算法(grey wolf optimizer,GWO)优化卷积神经网络(convolutional neural network,CNN)的SOH估计方法。通过提取部分恒压充电过程中的电流和时间序列数据,利用CNN自动提取特征,简化传统方法中繁琐的特征工程步骤,克服对完整充电数据的依赖,能显著降低数据采集和储存的成本。引入GWO对CNN的超参数进行优化,提高模型的估计精度。NASA和CALCE电池数据集的实验结果表明,该方法在SOH估计方面具有较高的精准度。
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