波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风...波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风格式分部求和-一致逼近项(summation by parts-simultaneous approximation terms,SBP-SAT)有限差分方法离散波动方程,并通过能量法进行稳定性评估。通过数值仿真,表明所提出的离散框架具有整合度高,稳定性好和拓展性强等特点。此外,该方法可以稳定模拟曲线域中的波传播并降低其实现成本,表明了波动方程系数矩阵对称化方法及其离散框架在波传播模拟领域具有广泛的应用前景。展开更多
The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field represen...The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)solvers.Our research makes several significant contri-butions to the field.Firstly,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known implementations.Secondly,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local solutions.This approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box implementation.Furthermore,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior performance.This implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy consumption.Our approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy consumption.These optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.展开更多
当前基于神经网络的端到端SAT求解模型在各类SAT问题求解上展现了巨大潜力。然而SAT问题难以容忍误差存在,神经网络模型无法保证不产生预测误差。为利用SAT问题实例特性来减少模型预测误差,提出了错误偏好变量嵌入架构(architecture of ...当前基于神经网络的端到端SAT求解模型在各类SAT问题求解上展现了巨大潜力。然而SAT问题难以容忍误差存在,神经网络模型无法保证不产生预测误差。为利用SAT问题实例特性来减少模型预测误差,提出了错误偏好变量嵌入架构(architecture of embedding error-preference variables, AEEV)。该架构包含错误偏好变量嵌入调整算法和动态部分标签训练模式。首先,为利用参与越多未满足子句的变量越可能被错误分类这一特性,提出了错误偏好变量嵌入调整算法,在消息传递过程中根据变量参与的未满足子句个数来调整其嵌入。此外,提出了动态部分标签监督训练模式,该模式利用了SAT问题实例的变量赋值之间存在复杂依赖关系这一特性,避免为全部变量提供标签,仅为错误偏好变量提供一组来自真实解的标签,保持其他变量标签为预测值不变,以在训练过程管理一个更小的搜索空间。最后,在3-SAT、k-SAT、k-Coloring、3-Clique、SHA-1原像攻击以及收集的SAT竞赛数据集上进行了实验验证。结果表明,相较于目前较先进的基于神经网络的端到端求解模型QuerySAT,AEEV在包含600个变量的k-SAT数据集上准确率提升了45.81%。展开更多
文摘波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风格式分部求和-一致逼近项(summation by parts-simultaneous approximation terms,SBP-SAT)有限差分方法离散波动方程,并通过能量法进行稳定性评估。通过数值仿真,表明所提出的离散框架具有整合度高,稳定性好和拓展性强等特点。此外,该方法可以稳定模拟曲线域中的波传播并降低其实现成本,表明了波动方程系数矩阵对称化方法及其离散框架在波传播模拟领域具有广泛的应用前景。
基金supported in part by the National Natural Science Foundation of China(No.62162016)in part by the Innovation Project of Guangxi Graduate Education(Nos.YCBZ2023132 and YCSW2023304).
文摘The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)solvers.Our research makes several significant contri-butions to the field.Firstly,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known implementations.Secondly,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local solutions.This approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box implementation.Furthermore,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior performance.This implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy consumption.Our approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy consumption.These optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.
