垃圾焚烧炉渣在三轴试验中的力学特性和破碎特性

以上海某生活垃圾焚烧厂产生的炉渣为研究对象,对垃圾焚烧炉渣进行X射线荧光光谱分析,得到了炉渣的化学成分组成。然后对干燥状态下的炉渣进行三轴试验,并在试验后进行颗粒筛分,得到垃圾焚烧炉渣在三轴试验中的力学特性和破碎特性。最后,将三轴试验数据和直剪试验数据进行对比,分析炉渣在不同试验中的强度指标和颗粒破碎程度,定量描述炉渣颗粒的破碎率。研究发现,当三轴试验的围压达到一定大小后,由剪切作用产生的破碎率将不再增长,此时剪切面处的颗粒已经全部破碎,即如果用更大的围压力进行三轴试验,实际增加的只是压缩破碎率。     

结合匈牙利指派和改进粒子滤波的多目标跟踪算法

使用汽车雷达进行多目标跟踪时，为了提高航迹关联效率并改善非线性场景跟踪效果，提出了结合匈牙利指派和卡尔曼重要性采样的粒子滤波(Particle Filter with Kalman Importance Sampling，PF-KIS)算法。首先，将航迹关联分解为聚类和指派，通过密度聚类筛选并整合有效目标，经过匈牙利指派得到目标和航迹的最佳匹配关系，避免产生多余联合事件，提高关联效率；其次，以卡尔曼滤波的结果作为粒子滤波的先验，使采样粒子分布更合理，提高估计精度，进而改善非线性跟踪能力。实验表明，算法平均航迹关联正确率约为95%；非线性场景误差约为卡尔曼滤波的1/2，有效地改善了非线性场景跟踪能力。     

Particle swarm optimization approach to portfolio construction

Particle swarm optimization (PSO) is an artificial intelligence technique that can be used to find approximate solutions to extremely difficult or impossible numeric optimization problems. Recently, PSO algorithms have been widely used in solving complex financial optimization problems. This paper presents a PSO approach to solve a portfolio construction problem, since this methodology is a population-based heuristic algorithm that is suitable for solving high-dimensional constrained optimization problems. The computational results show that PSO algorithms have advantages in optimizing the Sortino ratio, especially in speed, when the size of the portfolio is large.     

一种铁磁性磨粒监测器

根据高梯度磁力分离技术原理设计了一种铁磁性磨粒监测器。通过实验确定了磁滤器的合适参数,并验证了其重复性;设计确定了合适的霍尔传感器及后续处理电路。     

水土资源约束下区域农业供给侧结构优化——以河北省为例

在水土资源约束背景下,如何通过农业供给侧结构优化协调区域水土资源分布与农业生产的关系是当前农业问题的重中之重。借助比较优势理论和农业系统理论,构建水土资源约束下区域农业供给侧结构优化模型,并以河北省为研究对象进行实证分析,选用多目标优化粒子群算法对模型进行求解,以实现区域农业经济高效、可持续发展。     

基于PSO-PP模型的江苏城市资源环境承载力评价

在新型城镇化背景下,对城市资源环境承载力提出了新要求。通过建立江苏省13个城市资源与环境承载力的指标体系,应用投影寻踪模型和粒子群算法和Matlab仿真模拟编程,对江苏省资源环境承载力进行评价,形成江苏城市的等级划分,评价结果与发展现状基本吻合。模型评价避免了主观赋权的干扰,评价结果客观。     

一种联邦云系统中的资源分配算法

联邦云系统是一种整合多个云数据中心的资源,为用户提供优质云计算服务的新型云计算体系架构。高效的资源管理算法是实现联邦云系统的关键。为此,本文研究了以利润最大化为目标的联邦云资源分配问题,提出了一种资源分配算法。试验结果表明,相比NTFI等现有算法,本文所提算法能充分发挥联邦云系统的资源优势,极大提高云计算系统的盈利能力。     

Static and semistatic hedging as contrarian or conformist bets

In this paper, we argue that, once the costs of maintaining the hedging portfolio are properly taken into account, semistatic portfolios should more properly be thought of as separate classes of derivatives, with nontrivial, model‐dependent payoff structures. We derive new integral representations for payoffs of exotic European options in terms of payoffs of vanillas, different from the Carr–Madan representation, and suggest approximations of the idealized static hedging/replicating portfolio using vanillas available in the market. We study the dependence of the hedging error on a model used for pricing and show that the variance of the hedging errors of static hedging portfolios can be sizably larger than the errors of variance‐minimizing portfolios. We explain why the exact semistatic hedging of barrier options is impossible for processes with jumps, and derive general formulas for variance‐minimizing semistatic portfolios. We show that hedging using vanillas only leads to larger errors than hedging using vanillas and first touch digitals. In all cases, efficient calculations of the weights of the hedging portfolios are in the dual space using new efficient numerical methods for calculation of the Wiener–Hopf factors and Laplace–Fourier inversion.     

Particle Swarm Optimization in Agent-Based Economic Simulations of the Cournot Market Model

The numerous variations of the particle swarm optimization (PSO) algorithm originally proposed by Kennedy and Eberhart ( 1995 . Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks IV. IEEE: Piscataway, NJ; 1942–1948) have proven to be powerful optimization methods that rely on exploiting simple analogues of social interaction. In this study, PSO is adopted in lieu of the social or individual evolutionary learning algorithms as a model of individual adaptation in an agent-based computational model. In this examination of the simple Cournot market framework, each agent's individual strategy evolves according to the PSO algorithm. The model is one in which agents’ strategies must adapt interdependently. That is, a change in one particle may not only affect its performance but also other particles within the same swarm simultaneously. The dynamics and convergence properties associated with this model are compared with those where evolutionary learning algorithms are employed. Similar to evolutionary learning, convergence to equilibrium is dependent on the scope of learning, social or individual. While convergence is dependent on some of the algorithm parameters, prices resulting from the individual PSO are nearest the Cournot equilibrium and those from social PSO are nearest the Walrasian equilibrium in all cases. For particular parameterizations, certain advantages over evolutionary algorithms exist: in the main, decreasing volatility in market prices does not require an election operator or the addition of free parameters through two-level learning. Copyright © 2015 John Wiley & Sons, Ltd.     

提高碎屑岩粒度分析工效的研究和实践

通过对碎屑岩粒度分析过程中影响分析效率的因素分析,找到了其中的5项主控因素,并针对这5项因素提出了相应的改进对策和办法,自行设计研制了岩心颗粒解散头组合装置,通过方法、流程、设备的优化和改进使得碎屑岩的粒度分析工作效率有了大幅提高,节约了成本,创造了可观的经济效益。