基于格的高效范围证明方案

早在1985年,Goldwasser、Michali和Rackoff就提出了零知识证明。近年来,区块链这一新技术越来越为人们所熟悉。由于区块链的应用和发展,在实现零知识证明的相关结构方面取得了很大进展。同时,随着量子计算机的研究,许多传统的密码体制受到了严重的威胁。因此,如何构造一个既高效又能抵抗量子攻击的密码方案是密码学领域的一个新的难题。
范围证明是一种特殊的零知识证明协议。范围证明可应用于各种实际应用中,如电子投票系统或匿名凭证场景,以确保匿名性和隐私性。在这种协议中,证明者可以使验证者确信他知道一个属于开放连续整数区间的秘密整数。并且证明者不会泄漏有关秘密值的任何信息,除了它位于特定区间的事实。通常,这个秘密整数会被加密方案或承诺方案隐藏。但是现有的范围证明方案要么是不抗量子的,要么在实际应用中效率很低。最糟糕的是,在目前的范围证明方案中,可以证明范围集合是有限的。换言之,如果我们需要证明一个属于任意范围的秘密整数,现有的方案无法做到这一点。针对上述问题,本文提出了两种更有效的基于格的范围证明方案,它们都是后量子方案。首先,我们针对Regev经典加密方案给出了一个高效的范围证明。该范围证明协议可以证明任意范围内的被加密值,例如:证明秘密整数a在范围[α,β],其中α,β是Z_q中整数。同时,我们针对KTX08承诺方案也给出了一个高效的范围证明方案。该范围证明协议能够证明在[0,2^d]中的被承诺值。与目前已有的基于格假设的范围证明方案相比,我们的方案都有着更小的合理性错误和更低的通信成本。     

约束多目标进化算法研究进展

约束多目标进化算法（CMOEAs）能够同时处理多个相互冲突的目标函数和约束条件,引导种群逼向可行域的最优解,受到了研究者的广泛重视。首先介绍了约束多目标优化问题（CMOPs）的相关定义和多目标进化算法（MOEAs）的三种分类;其次,系统地分析了当前CMOEAs中约束处理机制,凝练出当前主要的四种约束处理方法;然后,从基于支配、基于指标、基于分解三个方面对CMOEAs的研究进展进行了详细综述;最后,指明了CMOEAs存在的挑战和未来研究方向。     

改进分解进化算法求解动态火力分配多目标优化模型

战前制定合理的火力分配（WTA）方案,可以优化资源配置,用最小的代价获取最大的战场收益。其一,建立了面向多型武器协同进攻作战的动态火力分配（DWTA）多目标优化模型,由多个阶段静态模型构成,各阶段静态模型参数需根据战场态势实时获取;其二,重点研究阶段静态模型求解算法。针对模型特点,设计了一种满足资源约束的编码方式,融合禁忌搜索和拥挤距离策略,提出了一种改进分解进化算法。对比实验验证了算法的可行性、快速性和有效性。     

求解大规模车间调度问题的一种分解优化算法

针对以最小化加权拖期和为目标的大规模作业车间调度问题,提出了一种基于操作的分解优化算法.该算法采用迭代优化的框架,从原问题对应的操作集合中依次分解出部分操作,并确定其加工顺序.在每一次迭代过程中,首先用模拟退火算法搜索较优的操作分解方案,并形成子问题.然后用遗传算法求解该子问题.针对随机生成的测试问题,以及某减速器厂实际生产数据的计算实验表明,该算法能够在合理的计算时间内获得满意的优化质量.     

Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design

Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization-based MLAC-CAMD framework is established for solvent design, where a novel machine learning-based atom contribution method is developed to predict molecular surface charge density profiles (σ-profiles). In this method, weighted atom-centered symmetry functions are associated with atomic σ-profiles using a high-dimensional neural network model, successfully leading to a higher prediction accuracy in molecular σ-profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer-aided molecular design technique by formulating and solving a mixed-integer nonlinear programming model, where model complexities are managed with a decomposition-based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC-CAMD framework.     

A sensitivity-based coordination method for optimization of product families

This article provides an introduction to a decomposition-based method for the optimization of product families with predefined platforms. To improve the efficiency of the system coordinator, a new sensitivity-based coordination method (SCM) is proposed. The key idea in SCM is that the system level coordinates share variables by using sensitivity information to make trade-offs between the product subsystems. The coordinated shared variables are determined by minimizing the performance deviation with respect to the optimal design of subproblems and constraint violation incurred by sharing. Each subproblem has a significant degree of independence and can be solved in a simultaneous way. The numerical performance of SCM is investigated, and the results suggest that the new approach is robust and leads to a substantial reduction in computational effort compared with the analytical target cascading method. Then, the proposed methodology is applied to the structural optimization of a family of automotive body side-frames.     

基于分解的摩擦补偿研究

文中提出了一个基于分解的控制设计框架并合成了摩擦补偿方案,以最合适的控制方法来补偿每个类型的摩擦.具有未知但恒定的参数的未建模摩擦由自适应来进行补偿,而模型参数变化的摩擦及非参数未建模摩擦由鲁棒补偿器来进行补偿.两种补偿器综合起来,构成了总的补偿方案,仿真结果证明了该摩擦补偿方案的有效性.     

A generalized reaching-law-based discrete-time integral sliding-mode controller with matched/mismatched disturbance attenuation

A generalized reaching-law-based (RL-based) discrete-time integral sliding-mode controller, which is versatile for either matched or mismatched disturbances, is designed in this paper to obtain high output tracking accuracy and avoid tremendous control efforts. Specifically, a disturbance decomposition-based discrete-time integral sliding surface is designed, and a generalized discrete-time reaching law is established. Different from the existing integral sliding surfaces, the proposed sliding surface synthesizes a disturbance-related integral term that is defined based on disturbance decomposition; this is crucial to the disturbance attenuation. Moreover, different from the available discrete-time reaching laws, the proposed reaching law introduces an adaptive exponential term into the control gains, and hence, the conventional RL-based discrete-time integral sliding-mode control (DISMC) and the equivalent-control-based (EC-based) DISMC can be integrated. Rigorous analysis shows that the closed-loop system is stable, the control effort can be satisfactory, and the steady-state output tracking accuracy is of order $O(T^2)$ for both matched and mismatched disturbances. The proposed method is proven effective through numerical simulations.