DES算法的安全误区

在对DES算法原理的描述和分析的基础上，论证DEC算法在应用中会导致安全性被破坏且容易被忽视的误区，并给出避开DES算法应用误区的具体操作方法．     

Maximizing Profits of Routing in WDM Networks

Let G = (V, E) be a ring (or chain) network representing an optical wavelength division multiplexing (WDM) network with k channels, where each edge e_j has an integer capacity c_j. A request s_i,t_i is a pair of two nodes in G. Given m requests s_i,t_i, i = 1, 2, ..., m, each with a profit value p_i, we would like to design/route a k-colorable set of paths for some (may not be all) of the m requests such that each edge e_j in G is used at most c_j times and the total profit of the set of designed paths is maximized. Here two paths cannot have the same color (channel) if they share some common edge(s).This problem arises in optical communication networks. In this paper, we present a polynomial-time algorithm to solve the problem when G is a chain. When G is a ring, however, the optimization problem is NP-hard (Wan and Liu, 1998), we present a 2-approximation algorithm based on our solution to the chain network. Similarly, some results in a bidirected chain and a bidirected ring are obtained.     

Robotic-Cell Scheduling: Special Polynomially Solvable Cases of the Traveling Salesman Problem on Permuted Monge Matrices

In this paper, we introduce the 1 − K robotic-cell scheduling problem, whose solution can be reduced to solving a TSP on specially structured permuted Monge matrices, we call b-decomposable matrices. We also review a number of other scheduling problems which all reduce to solving TSP-s on permuted Monge matrices. We present the important insight that the TSP on b-decomposable matrices can be solved in polynomial time by a special adaptation of the well-known subtour-patching technique. We discuss efficient implementations of this algorithm on newly defined subclasses of permuted Monge matrices.     

Finding a Length-Constrained Maximum-Density Path in a Tree

Let T = (V,E,w) be an undirected and weighted tree with node set V and edge set E, where w(e) is an edge weight function for e E. The density of a path, say e₁, e₂,..., e_k, is defined as ^k_{i = 1} w(e_i)/k. The length of a path is the number of its edges. Given a tree with n edges and a lower bound L where 1 L n, this paper presents two efficient algorithms for finding a maximum-density path of length at least L in O(nL) time. One of them is further modified to solve some special cases such as full m-ary trees in O(n) time.     

从人工智能、基因技术到人性危机:对科技宗教的先验现象学反思

近年来人工智能、认知科学和基因技术的突破,导致一种科技宗教的产生.这种自然主义、物理主义的意识形态,把生命和意识归约为算法加数据流,从而导致人性的危机和人文主义的破产.从先验现象学的进路分析意识难题和物理主义,可以基于先验主体性思路,给这些科学以另一种解读,同时重新阐释人性和人文主义,以消除科学危机和人类精神的危机.     

具有电力存储设备的实时需求响应与能量调度

实时需求响应与能量调度是智能电网中调节电力供需平衡的理想手段,其实施必然对用户的用电行为和电网的运行与管理产生深远影响。本文考虑用户具有多个可充、放电的电力存储设备,兼顾供电商发电量平稳的需求,在社会福利最大化模型的基础上,建立一个实时需求响应与能量调度的优化模型。给出模型的对偶问题,在满足强对偶性的前提下,可以通过求解对偶问题得到原问题的最优解,并确定可供用户与供电商参考的实时电价。在对偶问题中,问题可以分解为用户侧和供电侧两类子问题。进而设计分布式实时需求响应算法,并证明了算法的收敛性,供电侧与用户侧通过信息互动求得最优解。仿真结果验证了模型的合理性和算法的可行性。     

金融结构优化方法与理论

金融结构不存在一个放之四海而皆准的最优标准,一国的最优金融结构由特定时期的国情所决定。金融结构优化是实现最优金融结构,进而促进经济健康发展的重要手段。系统梳理了金融结构理论的演变历程,分析了金融结构的优化目标,总结了金融结构的优化方法。在文献研究基础上,明确后续研究的突破口在于如何针对金融结构与经济发展多目标的复杂交互关系,实现金融结构的多目标优化。为此,提出基于进化算法的金融结构多目标优化研究新视角,明确其中的关键问题,并对后续研究做出展望。     

女权社会主义视域中的现代女性解放问题探论

在置换流水加工环境下,以最小化生产流程时间为目标制定的初始加工方案,由于新工件的到达变得不再最优或不可行,为了降低对原始加工方案的影响,在权衡生产成本和扰动成本的情况下,建立双目标重调度干扰管理模型,对初始最优方案进行调整。针对该模型的特点和问题复杂度,结合微粒群算法强大的全局搜索能力,以及非支配排序遗传算法（NSGA Ⅱ）获得的Pareto解优良的综合性能,提出了一种混合微粒群算法来对问题求解。通过求解经典文献中置换流水车间双目标问题和随机生成的置换流水车间新工件到达问题,结果表明混合算法要优于NSGA Ⅱ和多目标微粒群算法（MOPSO）,同时验证了求解置换流水车间干扰管理问题的有效性。     

人工智能定价对消费者价格公平感知的影响

在GARCH模型框架下,提出过新的双曲GARCH形式(记为HGARCH),不仅与HY-GARCH模型一样可以同时刻画波动的强烈振幅和长记忆衰减两个性质,并且较之HY-GARCH模型,有更简单的条件方差非负约束条件.然而,当时间序列较长时,用单一参数结构不能充分捕捉可能发生的结构变化.为此,提出新的动态混合HGARCH模型(DM-HGARCH),使之可以同时拥有协方差平稳、长记忆和结构变化3个特性.讨论了新模型的弱平稳解存在条件,利用EM算法进行参数估计,并且用蒙特卡罗模拟给出估计在有限样本下的表现.最后将该模型分别用于1995年~2014年中国上证指数和美国标普500指数的日波动率建模.结果表明,在给定样本期间内,动态混合HGARCH模型(DM-HGARCH)对标普500指数有更好的样本内拟合和样本外预测表现.     

Economic-statistical design of simple linear profiles with variable sampling interval

Control charts are statistical tools to monitor a process or a product. However, some processes cannot be controlled by monitoring a characteristic; instead, they need to be monitored using profiles. Economic-statistical design of profile monitoring means determining the parameters of a profile monitoring scheme such that total costs are minimized while statistical measures maintain proper values. While varying sampling interval usually increases the effectiveness of profile monitoring, economic-statistical design of variable sampling interval (VSI) profile monitoring is investigated in this paper. An extended Lorenzen–Vance function is used for modeling total costs in VSI model where the average time to signal is employed for depicting the statistical measure of the obtained profile monitoring scheme. Two sampling intervals; number of set points and the parameters of control charts that are used in profile monitoring are the variables that are obtained thorough the economic-statistical model. A genetic algorithm is employed to optimize the model and an experimental design approach is used for tuning its parameters. Sensitivity analysis and numerical results indicate satisfactory performance for the proposed model.