Optimal DoS attack scheduling for multi-sensor remote state estimation over interference channels

In this paper, we investigate the optimal denial-of-service attack scheduling problems in a multi-sensor case over interference channels. Multiple attackers aim to degrade the performance of remote state estimation under attackers’ energy constraints. The attack decision of one attacker may be affected by the others while all attackers find their own optimal strategies to degrade estimation performance. Consequently, the Markov decision process and Markov cooperative game in two different information scenarios are formulated to study the optimal attack strategies for multiple attackers. Because of the complex computations of the high-dimensional Markov decision process (Markov cooperative game) as well as the limited information for attackers, we propose a value iteration adaptive dynamic programming method to approximate the optimal solution. Moreover, the structural properties of the optimal solution are analyzed. In the Markov cooperative game, the optimal joint attack strategy which admits a Nash equilibrium is studied. Several numerical simulations are provided to illustrate the feasibility and effectiveness of the main results.     

Adaptive optimal tracking controls of unknown multi-input systems based on nonzero-sum game theory

This paper focuses on the optimal tracking control problem (OTCP) for the unknown multi-input system by using a reinforcement learning (RL) scheme and nonzero-sum (NZS) game theory. First, a generic method for the OTCP of multi-input systems is formulated with steady-state controls and optimal feedback controls based on the NZS game theory. Then a three-layer neural network (NN) identifier is introduced to approximate the unknown system, and the input dynamics are obtained by using the derivative of the identifier. To transform the OTCP into a regulation optimal problem, an augmentation of the multi-input system is constructed by using the tracking error and the commanded trajectory. Moreover, we use an NN-based RL method to online learn the optimal value functions of all the inputs, which are then directly used to calculate the optimal tracking controls. All the NN weights are tuned synchronously online with a newly introduced adaptation based on the estimation error. The convergence of the NN weights and the stability of the closed-loop system are analyzed. Finally, a two-motor driven servo system and another nonlinear system are presented to illustrate the feasibility of the algorithm for both linear and nonlinear multi-input systems.     

基于提前支付强度过程的抵押贷款定价与均衡

基于提前支付强度过程考察了固定利率抵押贷款合同的定价和市场均衡问题.将均衡问题描述成代表性抵押人与市场之间的博弈.均衡由市场决定的内生抵押贷款利率和抵押人的最优再融资策略描述.在时齐Markov链利率及正线性比例再融资成本假设下,抵押人的规划问题可以简化成一个仅包含三个离散状态变量的Markov决策链,且一定存在唯一解.从而,均衡可由一个抵押利率决定函数和抵押人的最优再融资策略组成.一个简单的数值例子说明了计算均衡的迭代算法.结果表明,抵押人选择再融资往往是不明智的短视行为.     

国际能源价格波动对中国边际减排成本的影响:基于CEEPA模型的分析

减排成本是影响温室气体减排活动的一个关键因素。考虑到能源价格是影响减排成本的最主要的因素之一,本研究将中国的能源定价机制引入到所构建的中国能源与环境政策分析模型(CEEPA);运用改进的CEE-PA模型模拟中国不同能源定价机制情景下的边际减排成本;对国际能源价格波动对中国边际减排成本的影响进行了分析。结果表明,我国的边际减排成本对电力和成品油的定价方式是比较敏感的,放开这两种能源的定价特别是电价机制的市场化改革能推动我国边际减排成本的有效降低。不同能源的国际价格对我国边际减排成本的影响呈现很大的不同,国际能源价格上涨和下降对我国边际减排成本的影响具有对称性。     

电价调整对宏观经济及产业结构的影响

近年来中国电价的普遍性、连续性下调可能诱导资源向高耗能产业聚集,不利于产业结构优化.本文使用可计算一般均衡(CGE)模型分别在电价管制和电价市场化两类情景下分析电价调整对高耗能产业及新动能产业的影响.研究发现:①对国民经济各行业的"普遍性降电价"会导致高耗能产业增加值占比出现较大幅度上升,而仅对新动能产业的"精准性降电...     

光伏发电增值税优惠政策效应实物期权分析

本文在上网电价和发电成本不确定条件下建立光伏发电增值税收优惠政策模型,并在上网电价波动率为零的情况下构建标杆上网电价（固定上网电价）时增值税优惠政策效应实物期权模型。不同于现有税收优惠政策模型仅考虑税收优惠率的影响,本文的实物期权模型新方法包含了税收优惠期因素影响。选取实例数据实证分析表明：增值税优惠政策相当于使得光伏发电投资者每千瓦时多获得0.7分收益;增值税优惠政策虽能促进光伏发电投资,但是现行的较短税收优惠期并不能给投资者带来更大收益,应增加税收优惠期。     

不完全信息下物联网信息服务定价策略研究

运用博弈理论,构建了不完全信息下中间商占主导的物联网信息服务定价模型。在供应商边际生产成本信息不完全的静态情况下,通过信息甄别合约,运用显示原理分析了实现均衡时的信息服务定价问题及双方的最优定价策略;运用Rubinstein的思想分析在不完全信息动态博弈下双方信息服务定价的均衡解以及各自最优定价策略。研究表明:在不完全信息静态下,中间商可通过制定合理的信息服务价格与对供应商的转移支付来降低供应商边际生产成本不确定所带来的风险,同时中间商与供应商应共同致力于提高信息服务质量以增加收益。在不完全信息动态下,中间商若想在第一阶段、供应商在第二阶段报价成功,应努力降低自己的边际成本,并希望对方对信息服务价格的期望值接近临界值。     

市场条件下的电动汽车充电服务费双赢价格分析

伴随着电动汽车产业的发展,尽快出台合理的充电服务费定价标准成为充换电设施网络建设运营的重要影响因素。文中运用动态评价指标,基于调研数据分别测算投资者和消费者可接受的充电服务费,认为不存在双赢的价格区间。针对充电服务费的影响因素,提出政府应健全保障措施,制定合理规划,创新运营模式并推行峰谷分时的用电政策。     

双责任闭环供应链生态设计策略及其差异化影响

考虑生态设计降低产品制造成本、提高产品销量和提高废旧产品回收率3种生态设计策略,采用斯坦伯格博弈方法,面向产品生态设计和废旧产品回收的双责任闭环供应链,探究不同生态设计策略对企业的双责任行为、利润和环境等的影响。研究结果表明,在不同生态设计策略下,制造商的生态设计水平对零售商决策的影响范围、影响方向和影响力度具差异性;消费者价格敏感度与产品回收市场收益是影响双责任闭环供应链决策和制造商生态设计策略选择的关键要素;生态设计要素和固定成本要素对企业双责任行为、产品批发价和零售价产生不同影响。根据研究结论,提出从促进闭环供应链履行双责任视角,制造商应根据闭环供应链层面市场特征的变化而选择不同的生态设计策略。     

生产商—零售商供应链博弈研究——基于全量折扣和营销费用视角

基于全量折扣和营销费用的供应链生产商—零售商博弈模型为基础,分析了合作和非合作博弈下生产商和零售商的定价、需求量、产量及收益情况。其中非合作博弈基于Stackelberg模型,分为生产商领导和零售商领导,合作博弈则基于Pareto改进。结果表明,在合作博弈情况下,销售价格和市场营销费用均比非合作博弈低,同时在合作博弈情况下,需求也较非合作博弈有所提高。     

Model-Free distributed optimal consensus control of nonlinear multi-agent systems: A graphical game approach

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm.     

考虑员工学习-遗忘效应行为的动态生产-库存优化

员工在重复性生产作业中表现出学习效应行为,在生产中断后表现出遗忘效应行为,研究员工学习-遗忘效应行为下的动态生产-库存优化问题。引入学习-遗忘理论,构建生产率具有学习-遗忘特征的集成生产-库存系统平均成本函数,证明了在一定条件下平均成本函数是关于单次供货量或供货次数的凸函数。数值分析结果表明,在生产的前期学习效应对成本的降低具有重要的作用;完全遗忘时间越长,最优的平均成本越低;当完全遗忘时间较小时,在生产的后期,供应商处于学习与遗忘的摇摆状态,学习效应对降低成本发挥作用有限;最优成本随学习率的增大而快速增大;最优成本随初始生产率的增大而缓慢减少。本文的研究结论可为管理者何时决定生产中断及中断的时间长度提供决策依据。     

基于灰色模型的可再生能源电价补贴收支平衡

庞大的电价累计补贴缺口对可再生能源行业和财政支出造成巨大压力,研究中国可再生能源电价补贴收支平衡问题对其可持续发展具有重要现实意义。在电价补贴退出背景下,本文以风电、光伏发电、生物质能发电3类可再生能源为研究对象,利用灰色模型GM(1, 1)预测第二、三产业用电量和城乡居民用电量,核算可再生能源电价理论补贴和可再生能源电价附加收入,分析电价补贴收支平衡情况。结果表明：①2025年可完成电价累计补贴缺口的偿还,可再生能源电价补贴达到收支平衡,并有253.47亿元的补贴盈余,之后电价补贴盈余不断增大;②从2026年开始逐步下调电价附加征收标准,2026—2030年可每年下调电价附加征收标准0.002元/kW·h,2031—2038年每年下调电价附加征收标准0.001元/kW·h,直至2039年取消电价附加征收;③中国可再生能源电价附加收入漏出严重,征收率低,应尽快提升征收率直至足额征收,促进电价补贴提前实现收支平衡。最后,从调整电价附加征收标准、提高电价附加收入征收率、募集补贴资金等方面提出促进可再生能源行业高质量发展的对策建议。     

C2B平台竞价回收博弈研究

C2B网络回收平台的竞价回收机制决定了众多回收商之间存在着激烈的竞争,每个网络回收商的回收量不仅取决于自己的回收价格,还取决于其他网络回收商的回收价格。期望利润最大化是每个网络回收商的竞争目标。运用最优化理论与非合作博弈理论证明C2B平台竞价回收博弈存在唯一的Nash均衡。在完全竞争与串谋合作的不同情况中发现,完全竞争情况下Nash均衡的网络回收价格高于串谋合作下的网络回收价格,并通过数值仿真进行验证。     

A study on forecasting electricity production and consumption in smart cities and factories

The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.     

基于再制造闭环供应链视角的低碳减排研究

以一个销售商驱动的低碳再制造闭环供应链为研究对象,用均值-方差刻画风险特性,分析研究低碳产品市场和废旧低碳产品市场均为随机市场需求情境下、零和博弈和集中决策下闭环供应链中,低碳产品销售价、交易价格、低碳废旧产品回收价格等变量和节点企业期望效用及整个链条期望效用的变化规律。研究表明:零和博弈下低碳闭环供应链未能实现优化和协调。最后,设计正向渠道收益共享、逆向渠道风险共担契约,实现低碳再制造闭环供应链的协调,并通过数值仿真和灵敏度分析验证该模型的有效性及实用性。     

A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs

In this paper, we address the issue of sparse signal recovery in wireless sensor networks (WSNs) based on Bayesian learning. We first formulate a compressed sensing (CS)-based signal recovery problem for the detection of sparse event in WSNs. Then, from the perspective of energy saving and communication overhead reduction of the WSNs, we develop an optimal sensor selection algorithm by employing a lower-bound of the mean square error (MSE) for the MMSE estimator. To tackle the nonconvex difficulty of the optimum sensor selection problem, a convex relaxation is introduced to achieve a suboptimal solution. Both uncorrelated and correlated noises are considered and a low-complexity realization of the sensor selection algorithm is also suggested. Based on the selected subset of sensors, the sparse Bayesian learning (SBL) is utilized to reconstruct the sparse signal. Simulation results illustrate that our proposed approaches lead to a superior performance over the reference methods in comparison.     

中国能源价格变动与居民消费水平的动态效应——基于VAR模型和SVAR模型的检验

能源价格的变动对居民消费水平的变化有重要影响。本文综合运用VAR模型和SVAR模型,通过对1989年-2009年我国能源价格变动与居民消费水平的动态波动效应的实证检验表明:①居民消费水平在短期对能源价格水平有正向冲击作用,在长期有负向冲击作用,居民消费水平的提高短期内对能源价格水平的上涨有推动作用;能源价格水平在短期和长期对居民消费水平有明显的正向冲击作用,能源价格水平的上涨导致居民消费水平的上涨;②能源价格水平在短期主要受前期自身水平的影响,居民消费水平在中长期对能源价格水平也有较强贡献;居民消费水平短期内主要受前期居民消费水平的影响,长期受居民消费水平和能源价格水平的共同影响。基于以上结论,国家在调控能源价格时需要考虑到能源价格对居民消费水平的影响,避免由于能源价格过快上涨导致居民消费水平的剧烈波动;此外,在能源价格长期上涨预期背景下,我国政府应逐步提高居民工资收入占GDP比重,通过收入分配改革提高居民收入水平,为维持高能源价格背景下居民消费水平的健康发展提供支持。     

基于马尔可夫模型的图书馆用户聚类分群方法研究

【目的/意义】针对图书馆用户群体聚类分群不稳定且错误率较高的问题,提出基于马尔可夫模型的图书馆 用户聚类分群方法,提升图书馆用户聚类分群精准度。【方法/过程】采用一阶马尔可夫混合模型构建用户动作序列 模型,通过模型产生用户行为聚类,体现用户动作的动态性,采用自适应自然梯度算法,依据用户行为分离状态自 适应调整自身步长,优化模型参数学习中模型自动选择问题,实现最佳图书馆用户聚类分群。【结果/结论】通过实 验结果能够证明,实际聚类数量小于L值时,提出方法能够实现参数学习过程中模型的自动选择。提出方法的分群 数量最多,能够划分出最大的取值区间,聚类错误率最低为0.22%,聚类性能比较稳定,分群结果更加精准,达到了 设计的预期。【创新/局限】采用一阶马尔可夫混合模型实现了图书馆用户聚类分群。后续将进一步研究可考虑用 户序列间关联的高阶马尔可夫分量模型,以提高分群算法的准确性和稳定性。     

Trilayer Stackelberg game for nonlinear systems using adaptive dynamic programming

This paper considers a trilayer Stackelberg game problem for nonlinear system with three players. A novel performance function is defined for each player, which depends on the coupling relationships with the other two players. The coupled Hamilton–Jacobi–Bellman (HJB) equations are built from the performance functions, and the optimal control polices of three players are obtained based on the Bellman’s principle of optimality. Because of the nonlinearity and coupling characteristics, a policy iteration (PI) algorithm with a three-layer decision-making framework is developed to online learn the coupled HJB equations. In order to implement the algorithm, we construct a critic-action neural network (NN) structure and design a NN approximation-based iteration algorithm. Finally, a simulation example is presented to verify the effectiveness of the proposed method.