An optimized encoding method for secure key distribution by swapping quantum entanglement and its extension

Song [Song D 2004 Phys. Rev. A69034301] first proposed two key distribution schemes with the symmetry feature.We find that, in the schemes, the private channels which Alice and Bob publicly announce the initial Bell state or the measurement result through are not needed in discovering keys, and Song’s encoding methods do not arrive at the optimization.Here, an optimized encoding method is given so that the efficiencies of Song’s schemes are improved by 7/3 times. Interestingly, this optimized encoding method can be extended to the key distribution scheme composed of generalized Bell states.

Research on Optimizing the Distribution Path of Agricultural Cold Chain Logistics in Green Consumption Era

In recent years, with the rapid development of China's fresh market, cold chain logistics ushered in a new development opportunity. Because of the late start and slow development of cold chain logistics in China, it is difficult to achieve cross-regional and offseason sales activities, resulting in a situation of high output and small sales volume, which is also an important reason for the high price and poor quality of fresh agricultural products. With the passage of time, the characteristics of cold chain distribution of agricultural products have become an important direction for the future development of logistics industry. In this paper, aiming at the characteristics of agricultural cold chain distribution, the carbon emissions in the process of distribution were quantified as costs and added to the total cost, and an optimization model of agricultural cold chain logistics path with time window considering the minimum total cost as the objective function was constructed. The improved genetic algorithm was used to solve the problem. The simulation results show that the improved genetic algorithm is an effective method to solve the optimization problem of agricultural cold-chain logistics path considering carbon emissions, and has guiding significance for the selection of low-carbon and environmentally friendly distribution routes for agricultural cold-chain logistics distribution enterprises.

基于剩余碰撞时间的线控制动分层控制策略

针对不同制动工况需求的制动策略存在差异的情况,提出一种线控制动分层控制策略.在该分层策略的上层,利用二阶TTC安全碰撞时间模型计算出车辆与前车的剩余碰撞时间,以此作为依据进行制动策略的选取,并建立了汽车二自由度模型、车身法向受力模型和Burckhardt轮胎模型;在该分层策略的下层,进行了制动力在轮间的分配,使用序列二次规划(SQP)方法,分别在一般制动、紧急制动、失稳制动3种工况下,以轮胎滑移率为对象建立优化函数,对车辆制动力进行了优化分配.使用MATLAB/Simulink和Carsim进行了联合仿真,对所提出3种工况下的制动分配策略进行了有效性验证.结果表明:在一般制动工况下,采用该策略时相比对照工况制动距离减少18.08%,制动时间减少25.12%;在紧急制动工况下,采用该策略时相比对照工况制动距离减少19.17%,制动时间减少12.79%;在失稳制动工况下,该策略可通过轮胎差扭来提升车辆的横向稳定性.采用文中制动策略显著提升了车辆的制动效能.

Distributed Lagrange Multiplier/Fictitious Domain Finite Element Method for a Transient Stokes Interface Problem with Jump Coefficients

The distributed Lagrange multiplier/fictitious domain(DLM/FD)-mixed finite element method is developed and analyzed in this paper for a transient Stokes interface problem with jump coefficients.The semi-and fully discrete DLM/FD-mixed finite element scheme are developed for the first time for this problem with a moving interface,where the arbitrary Lagrangian-Eulerian(ALE)technique is employed to deal with the moving and immersed subdomain.Stability and optimal convergence properties are obtained for both schemes.Numerical experiments are carried out for different scenarios of jump coefficients,and all theoretical results are validated.

Distributed Alternating Direction Method of Multipliers for Multi-Objective Optimization

In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.

Optimal distribution of reliability for a large network based on connectivity

It is a non-polynomial complexity problem to calculate connectivity of the complex network. When the system reliability cannot be expressed as a function of element reliability, we have to apply some heuristic methods for optimization based on connectivity of the network. The calculation structure of connectivity of complex network is analyzed in the paper. The coefficient matrixes of Taylor second order expansion of the system connectivity is generated based on the calculation structure of connectivity of complex network. An optimal schedule is achieved based on genetic algorithms （GA）. Fitness of seeds is calculated using the Taylor expansion function of system connectivity. Precise connectivity of the optimal schedule and the Taylor expansion function of system connectivity can be achieved by the approved Minty method or the recursive decomposition algorithm. When error between approximate connectivity and the precise value exceeds the assigned value, the optimization process is continued using GA, and the Taylor function of system connectivity needs to be renewed. The optimization process is called iterative GA. Iterative GA can be used in the large network for optimal reliability attribution. One temporary optimal result will be generated every time in the iteration process. These temporary optimal results approach the real optimal results. They can be regarded as a group of approximate optimal results useful in the real project.

An Application of Generalized Entropy Optimization Methods in Survival Data Analysis

In this paper, survival data analysis is realized by applying Generalized Entropy Optimization Methods (GEOM). It is known that all statistical distributions can be obtained as distribution by choosing corresponding moment functions. However, Generalized Entropy Optimization Distributions (GEOD) in the form of distributions which are obtained on basis of Shannon measure and supplementary optimization with respect to characterizing moment functions, more exactly represent the given statistical data. For this reason, survival data analysis by GEOD acquires a new significance. In this research, the data of the life table for engine failure data (1980) is examined. The performances of GEOD are established by Chi-Square criteria, Root Mean Square Error (RMSE) criteria and Shannon entropy measure, Kullback-Leibler measure. Comparison of GEOD with each other in the different senses shows that along of these distributions (MinMaxEnt)4 is better in the senses of Shannon measure and of Kullback-Leibler measure. It is showed that, (MinMaxEnt)3 ((MaxMaxEnt)4) is more suitable for statistical data among (MinMaxEnt)m,m=1,2,3,4(MaxMaxEnt)m,m=1,2,3,4. Moreover, (MinMaxEnt)3 is better for statistical data than (MaxMaxEnt)4 in the sense of RMSE criteria. According to obtained distribution (MinMaxEnt)3 (MaxMaxEnt)4 estimator of Probability Density Function?f^?(t), Cumulative Distribution Functio?F^ (t) , Survival Function Ŝ(t) and Hazard Rate ĥ(t) are evaluated and graphically illustrated. The results are acquired by using statistical software MATLAB.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

基于多元储能的分布式能源系统优化调度方法研究

为进一步构建清洁低碳、经济节能的供能系统,研究建立了含风、光以及多元储能的分布式能源系统,针对北京某办公园区,采用DeST预测用户负荷以及当地风、光条件。以经济性、节能性和环保性三方面的综合效益最大为目标,提出一种自适应优化运行策略,分别采用穷举搜索法和遗传算法对系统优化调度方案进行优化。同时,采用以电定热运行策略作为对照,对比分析几种不同运行策略下系统的综合效益。结果表明:传统以电定热运行模式下的综合效益平均值为0.41;而在自适应优化运行策略下,使用遗传算法得到的调度方案,其综合效益平均值可达0.5,穷举搜索法得到的运行方案,其综合效益平均值可达0.51。

基于随机响应面法的配电网故障恢复全时段不确定性优化方法

配电网故障恢复是电网安全经济运行的重要保障,为消除故障恢复过程中分布式电源不确定性对电网运行的风险,该文计及全时段不确定性影响,基于随机响应面法构建配电网故障恢复的混合整数二阶锥规划随机优化模型;为准确评估储能系统的运行风险,对各时段储能荷电状态的随机性进行概率等价处理,并基于Hermite混沌多项式的特征系数构建配电网安全运行的机会约束凸函数,确保解的全局唯一性;为降低优化模型的复杂度,在保证随机过程拟合精度的前提下,提出简化的随机潮流约束,以减少二阶锥松弛约束的使用。最后,通过蒙特卡洛仿真发现,所提随机优化模型能准确描述随机响应的概率特征,在满足安全运行的机会约束下,可最大化减少停电损失。

A Heuristic on Risk Management System in Goods Transportation Model Using Multi-Optimality by MODI Method

Transport risk management is one of the predominant issues to any industry for supplying their goods safely and in time to their beneficiaries. Damaging goods or delaying the shipping both make penalty to the company and also reduce the goodwill of the company. Every way of transportation routes has to be comfy which can make sure the supplies will attain without damaging goods and in time and additionally cost efficiently. In this paper, we find a few not unusual risks which might be concerned about all types of way of routes which include Highway, Waterway, Airway, Railway and so forth. Additionally, we proposed a technique to attain multiple optimal solutions by using Modified Distribution Method (MODI) of a transportation problem. Finally, we reduce the risks by minimizing the possible number of transportation routes using multi-optimality technique of the transportation problem.

集成差异化建筑集群的微网系统分层分布式优化调度

针对差异化绝热性能建筑集群与微网系统间的协同互动,提出一种基于交替方向乘子法(ADMM)的分层分布式优化调度策略,在实现微网高效、经济运行的同时,充分考虑不同建筑集群的差异化利益诉求和隐私保护需求。首先,考虑多主体利益诉求,构建了集成差异化绝热性能建筑集群的微网系统分层调度模型。在上层,微网系统运营商(MSO)通过优化购电功率、售电功率和售电价格,最大化其收益;在下层,采用热阻-热容网络对建筑的热动态过程进行差异化建模,建筑集群用户通过优化暖通空调系统送风温度和购电价格,最小化其用能成本。然后,考虑数据隐私性需求,采用ADMM进行分层分布式求解。通过引入虚拟变量拆分上层MSO和下层建筑集群用户子问题,并耦合上下层优化变量,根据ADMM进行局部更新和交替迭代,得到全局优化解。为确保算法的收敛性,利用McCormick包络松弛方法将子问题转化为凸优化。最后,通过算例分析验证了分层分布式优化调度策略的合理性和有效性。

考虑异步通信的配电-气网分布式有功-无功协同优化

配电-气网(EGDN)中新能源大量并网和燃气运输过程中管网压力变化均可能导致配电网的电压波动增大。文中提出一种考虑异步通信的EGDN有功-无功协同优化方法。首先,研究EGDN中各元件的有功-无功特性;其次,根据EGDN能量运输过程中无功需求特性,建立EGDN无功供需模型;然后,考虑EGDN无功平衡和网络运行约束,以最小化电网电压波动、网损和EGDN购电/气成本为目标,建立EGDN无功电压优化控制模型;最后,为保障EGDN中各能源主体信息的安全性与私密性,考虑配电网与配气网信息采集传输情况存在差异,可能存在通信延迟和信息丢包情况,采用异步交替方向乘子法对模型进行求解。通过对改进的IEEE 33节点系统和7节点配气网构成的EGDN系统进行仿真,结果表明,所提方法可有效缓解EGDN电压波动问题,减小网损并降低系统总运行成本。

一种解决带有凸集约束的分布式伪凸优化的连续时间算法

本文提出了一种可以解决带有凸集约束且全局目标函数是非光滑伪凸的分布式优化问题的连续时间算法.本文所提出的分布式优化算法采用罚函数方法建模,通过构造一个合适的惩罚项迫使所有智能体的状态变量进入可行域.通过使用李雅普诺夫(有限时间)稳定性理论等工具,证明了在一定假设下,本文所提出的算法将在有限时间内达成一致状态且进入可行域并永驻其中,并在此之后收敛到原分布式优化问题的最优解集.此外,该算法在运行时各个智能体仅仅需要接收邻居节点的相对状态的符号信息.最后,仿真结果验证了本文所提出的算法的有效性.

考虑不确定性的配电网-多综合能源系统分布式协同规划

综合能源系统的多点并网加剧了配电网规划的“维数灾害”,提出兼顾多主体利益均衡性和分析计算高效性的配电网-多综合能源系统的分布式协同规划方法。以区间数刻画系统内的源-荷不确定性,构建计及多利益主体的配电网-多综合能源系统分布式协同规划框架;考虑多源联供的低碳清洁性、综合能源系统对配电网的支撑性和多能互补性,建立考虑奖惩碳交易机制的配电网-多综合能源系统双层规划模型;采用基于最大变差分析的目标级联分析法求解含有区间不确定性和多主体耦合性的规划模型。仿真结果表明,所提方法有效降低了配电网耦合综合能源系统的复杂度,提升了系统内各利益主体的均衡性。

含电动汽车的主动配电网多目标分层优化调度

为了协调电动汽车车主和主动配电网2个不同主体之间的利益关系,针对电动汽车接入后主动配电网的优化调度问题,文中提出一种考虑电动汽车充电综合满意度和主动配电网运行效益的多目标分层优化方法。上层模型注重最大化电动汽车车主的充电利益,采用归一化法向约束法求解电动汽车的最优充放电计划,并将其输入下层优化模型。下层模型旨在最大化主动配电网的运行效益,根据电动汽车的充放电计划调整可控分布式电源的输出功率,采用二阶锥松弛转换法和带权极小模理想点法求解该非线性多目标问题。仿真结果表明,所提含电动汽车的主动配电网多目标分层优化方法能够在促使电动汽车充电综合满意度超过0.9的同时,减少有功网损约94.12%、运行成本约30.90%,实现电动汽车车主和主动配电网的双赢。

基于净能力及二阶锥规划的分布式光储多场景协同优化策略

针对现有配电网中分布式光储调度模型存在资源协同不足、求解复杂等问题,提出了一种基于净能力及二阶锥规划的分布式光储多场景协同优化调度策略。通过引入储能接入配电网后的功率转移分布因子,提出一种基于系统净能力的储能最优选址计算方法;综合考虑储能的运行特性和分布式光伏的出力不确定性,建立以系统日综合成本和削峰填谷为目标的分布式光储多场景协同优化调度模型;利用二阶锥松弛和Big-M法对潮流约束、储能运行约束进行处理,将原规划模型转化为混合整数二阶锥规划问题。以IEEE 33节点系统和西北某实际系统为算例进行仿真分析,结果表明所提方法能在降低负荷峰谷差和日综合成本、平抑负荷波动的同时,显著提高对分布式光伏的消纳能力,验证了所提方法的有效性和可行性。

一种连续型不确定性复杂系统博弈理论及算法研究

当前对于不确定性复杂系统博弈的研究,通常情况下有关策略集是离散的,而非连续和随机的。而在复杂经济社会系统中,常常会遇到连续性随机博弈问题,以及系统中数据的确权问题。在此背景下,提出了一种随机博弈的概念,给出连续策略集下N人非合作随机博弈模型均衡解存在性定理,以及复杂信息系统随机博弈模型的构建及其纳什均衡解算法。给出连续策略下不确定性N人非合作随机博弈概念,建立以局中人的最大收益为目标函数的N人非合作随机博弈模型,提出了均衡解的存在性定理;构建了Wasserstein模糊集,之后融合分布鲁棒优化方法以及投资组合优化方法将该模型转化为有限凸规划,并运用遗传算法求解局中人的近似混合策略,最后构建了基于回归分析的纳什均衡求解算法并将纳什均衡解归一化进行确权。实证分析表明,所构建的理论与算法是有效可行的。

基于混合威布尔分布的水稻插秧机的可靠性分析及剩余寿命预测

为了更准确描述水稻插秧机的失效规律,提高可靠性分析的准确性,对水稻插秧机的故障数据进行分析,采用两参数混合威布尔分布对水稻插秧机进行建模。以残差平方和最小为优化目标,建立参数估计优化模型,利用改进粒子群算法对其进行求解,然后采用K-S检验法对模型进行检验,对比单一威布尔模型、混合威布尔模型与水稻插秧机失效数据之间的拟合程度,得出使用两参数混合威布尔模型评估水稻插秧机可靠性的合理性,在此模型的基础上计算得到水稻插秧机的平均无故障工作时间为161.75 h,中位寿命为147.14 h,特征寿命为191.31 h,且在可靠度为0.6时,预防性维修周期为115.19 h,最后在混合威布尔分布模型的基础上计算出剩余寿命-可靠度的关系,可定量分析插秧机在一定使用时间下的剩余寿命,从而进行预测性维护。

基于改进SCHO算法的局部遮荫光伏MPPT研究

针对局部遮荫工况下光伏阵列最大功率点跟踪(MPPT)存在的易陷入局部功率峰值点、跟踪时间长、跟踪精度低等问题,提出一种基于改进双曲正余弦优化(SCHO)的控制算法.其采用立方混沌映射初始化,提高初始候选解集的遍历性,并利用贝塔分布的概率特性修正SCHO算法的切换标准,提高算法与候选解集寻优进程的适配性.同时,对SCHO算法中的全局最优解、个体当前解引入平衡权重更新策略,且采用透镜成像反向学习策略对寻优后期的候选解进行扰动,采用比例收缩法对寻优空间进行动态钳位,提高算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他控制算法,本文提出的改进SCHO算法能缩短MPPT时间、提高MPPT精度,故具有更优的MPPT性能,可为进一步提升光伏发电效率提供参考.