基于最坏情况条件鲁棒利润的发电机组最优组合

采用最坏情况条件风险价值方法刻画电价风险,并定义最坏情况条件鲁棒利润描述发电商的利润,考虑机组启停,建立了基于最坏情况条件鲁棒利润的发电机组最优组合模型.在盒式不确定集下,将所建模型转化为混合整数二阶锥规划进行求解.通过10机24时段系统的算例仿真,验证了所建模型和求解方法的有效性.     

基于聚类和动态规划的组合路径策略

为提高电商仓库的拣货作业效率,本文提出了基于聚类和动态规划的组合路径策略,实现了生成路径消耗时间和路径长度之间的平衡,并将这一策略成功地应用到多区型仓库。该策略分四步:首先,根据待拣储位分布特征,运用聚类分析法对其进行分类;然后,以各类的首末储位作为节点,运用动态规划法对已得分类进行排序,得到相应的类序;其次,得到各类内部路径;最后,依次拣取待拣商品,并返回出发点完成拣货作业。在提出新的路径策略后,通过仿真方法将新策略与三种传统路径策略(穿越策略、最大间隙策略和混合策略)和一种优质算法(蚁群算法)进行了对比分析,结果表明:该策略具备良好的适用性和实用性。     

基于不同条件的旅游路线规划问题研究

运用2015年全国研究生数学建模竞赛F题的数据资料,针对旅游路线合理规划问题的第一问展开研究.以F题的问题一为起点进行了分析研究,是因为第一问的完成是解决后续问题的关键.首先通过地图搜集并补全了缺失数据并对数据进行合理的处理,然后采用将旅游年数最少目标转化为该最小生成树的"最少圈覆盖"方法来进行对问题一的求解,也可以理解为一个广义的多旅行商问题,以旅行商的人数(即年数)最少为目标.采用Dijkstra算法、最少圈覆盖法、智能算法和图论聚类等方法,通过这些方法建立了单目标优化模型,并运用旅行商问题和模型之间的转换来对问题进行分析与求解.     

分配问题的多角度分析

从数理逻辑、整数规划和博弈论三方面对分配问题进行分析,介绍不同的数学分支在分配问题中的应用.     

带凹性生产成本运输问题的新随机规划模型及其求解

提出了一种新的能反映决策者满意度的随机变量序关系,并据此研究了随机不等式的确定性等价类,方法被称为满意度方法.最后将其应用于带凹性生产成本运输问题的求解中,并将方法与常用的机会约束方法进行比较,说明满意度法不仅合理可行,而且当决策者对约束条件的要求越高时,它所得最优值越优于机会约束法所得最优值.     

基于Grbner基方法的交通分配算法

交通规划中的第四阶段交通分配是交通规划中最重要的环节之一,合理的交通分配方法是未来规划期内交通运输系统状态良好的关键,对交通分配模型进行优化有利于交通规划正确高效.经典的交通规划分配模型算法计算复杂,比较次数多,计算量大,而Grbner基方法在计算机上容易实现,计算思路清晰简洁,适合在交通分配中采用.选取了交通分配中的典型算法增量分配法,对其中最短路算法用Grbner基方法改进,构造了基于Grbner基方法的交通分配模型.模型先将交通分配中的最短路问题转化为求多项式集的Grbner基,然后直接得出交通分配中的最短路径,使交通分配算法高效简洁.最后,为算法加以实例佐证,证实算法在工程应用中可行.     

两大部类持续扩大再生产的优化

基于经典的马克思两大部类社会再生产公式,建立了离散确定型的持续扩大再生产的优化问题的动态规划模型.在生产资料部类的不变资本产出率高于另一部类的条件下,动态规划的指标函数是作为决策变量的生产资料部类积累率的单调函数,因而可以使用逆序解法或者顺序解法,获得唯一的最优策略和最优指标函数.借助《资本论》中的一个举例,计算验证了最优解.     

基于状态指标的更新决策方法研究

针对设备在实际运行和执行任务时经常涉及到维修、更新报废等管理活动缺乏定量模型支撑的问题,提出了利用典型部件的预测寿命和非典型部件的历史寿命对设备系统级的健康状态进行综合评估的方法,建立了一种以状态指标为决策序列的动态规划更新决策方法。该方法能综合考虑维修费用和更新费用,确定最优更新决策,降低保障费用,有效的提高了设备的经济效益。最后经过案例分析证明了该方法的有效性,对于提高设备的保障能力和经济效益提供了理论支撑和方法指导。     

Ho-Lee利率模型下多种风险资产的动态投资组合

假设无风险利率可由Ho-Lee利率模型描述,且与股票动态存在一般线性相关系数,应用最优性原理和HJB方程研究了市场存在多种风险资产情形的动态资产分配问题,通过变量替换方法得到了幂效用和指数效用下最优投资策略的显示解,数值算例分析了利率参数和市场参数对最优投资策略的影响趋势。研究结果发现:两种效用下的最优策略均由两部分所构成,一部分由市场参数所确定,另一部分由利率参数所确定。而且,幂效用下的最优投资策略与瞬时利率无关,而指数效用下的最优投资策略与瞬时利率相关。     

基于上转换纳米粒子的低损伤神经界面技术

Optogenetics is a neuromodulation technology that combines light control technology with genetic technology, thus allowing the selective activation and inhibition of the electrical activity in specific types of neurons with millisecond time resolution. Over the past several years, optogenetics has become a powerful tool for understanding the organization and functions of neural circuits, and it holds great promise to treat neurological disorders. To date, the excitation wavelengths of commonly employed opsins in optogenetics are located in the visible spectrum. This poses a serious limitation for neural activity regulation because the intense absorption and scattering of visible light by tissues lead to the loss of excitation light energy and also cause tissue heating. To regulate the activity of neurons in deep brain regions, it is necessary to implant optical fibers or optoelectronic devices into target brain areas, which however can induce severe tissue damage. Non- or minimally-invasive remote control technologies that can manipulate neural activity have been highly desirable in neuroscience research. Upconversion nanoparticles (UCNPs) can emit light with a short wavelength and high frequency upon excitation by light with a long wavelength and low frequency. Therefore, UCNPs can convert low-frequency near-infrared (NIR) light into high-frequency visible light for the activation of light-sensitive proteins, thus indirectly realizing the NIR optogenetic system. Because NIR light has a large tissue penetration depth, UCNP-mediated optogenetics has attracted significant interest for deep-tissue neuromodulation. However, in UCNP-mediated in vivo optogenetic experiments, as the up-conversion efficiency of UCNPs is low, it is generally necessary to apply high-power NIR light to obtain up-converted fluorescence with energy high enough to activate a photosensitive protein. High-power NIR light can cause thermal damage to tissues, which seriously restricts the applications of UCNPs in optogenetic technology. Therefore, the exploration of strategies to increase the up-conversion efficiency, fluorescence intensity, and biocompatibility of UCNPs is of great significance to their wide applications in optogenetic systems. This review summarizes recent developments and challenges in UCNP-mediated optogenetics for deep-brain neuromodulation. We firstly discuss the correspondence between the parameters of UCNPs and employed opsins in optogenetic experiments, which mainly include excitation wavelengths, emission wavelengths, and luminescent lifetimes. Thereafter, we introduce the methods to enhance the conversion efficiency of UCNPs, including optimizing the structure of UCNPs and modifying the organic dyes in UCNPs. In addition, we also discuss the future opportunities in combining UCNP-mediated optogenetics with flexible microelectrode technology for the long-term detection and regulation of neural activity in the case of minimal injury.