提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题,满足GPS定位信号的精度和信号数据处理时间的要求,提出1种群调度滤波算法,通过实验仿真,分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间,具有较大优势,可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

大规模突发事件中基于满意度的应急物资优化调度模型

建立了大规模突发事件下供给点应急物资供应数量小于应急点应急物资需求数量的应急物资管理调度模型。同时结合大规模突发事件的特点,将模型设为多时段动态变化的,能使有限资源达到最大救援效用的多物资、多种运输方式、多运输车辆的物资优化调度模型,并根据不同应急点对物资需求的重要程度不同引入权重系数,使应急救援初期有限资源的分配达到整体的最大满意度。结合模型特点,给出模型的求解算法,并通过一个算例验证模型和算法的有效性。最后得出,以整体应急点的满意度作为具有多货物多起止点多运输方式等特点的应急物资调度问题的目标函数,能使应急救援过程达到最大的救援效率。     

基于模拟植物生长算法的快线公共车辆优化调度研究

公共车辆的运营优化调度能够很好的减少城市交通拥堵,缓解交通压力,保证多方面的利益。对乘客高峰期快线公共车辆的调度进行了研究。首先运用了三元组α/β/γ方法对问题进行了描述,将乘客高峰期快线车辆的调度描述为一类以最小化制造期和平均等待时间为目标,具有先入先出原则和机器适用限制的流水车间动态调度问题。并在此基础上建立了相应的数学模型;接着,运用模拟植物生长算法进行了算法设计;最后,用实际数据进行了数值仿真,结果证明该研究具有较高的理论及现实价值。     

海上突发灾难事件应急物资调度优化方法研究

为满足海上突发灾难事件救援对应急物资的需求,最大限度降低其带来的损失,根据海上突发灾难事件的特点,提出救助目标在受复杂气象、海况等因素影响发生漂移/扩散时的海上突发灾难事件应急物资调度方法。综合考虑海洋环境影响,引入救助资源包的概念。构建考虑时间、运力、物资需求等约束条件,以最大限度地满足物资供应可靠性为目标的海上突发灾难事件多级应急物资调度优化模型。通过算例对模型的应用进行验证。研究结果表明,事件形态的演变会影响应急物资集结点的选择,需要在预测事件形态时空变化的基础上合理确定海上应急物资调度方案。     

Optimisation algorithms for spatially constrained forest planning

We compared genetic algorithms, simulated annealing and hill climbing algorithms on spatially constrained, integrated forest planning problems. There has been growing interest in algorithms that mimic natural processes, such as genetic algorithms and simulated annealing. These algorithms use random moves to generate new solutions, and employ a probabilistic acceptance/rejection criterion that allows inferior moves within the search space. Algorithms for a genetic algorithm, simulated annealing, and random hill climbing are formulated and tested on a same-sample forest-planning problem where the adjacency rule is strictly enforced. Each method was randomly started 20 times and allowed to run for 10,000 iterations. All three algorithms identified good solutions (within 3% of the highest found), however, simulated annealing consistently produced superior solutions. Simulated annealing and random hill climbing were approximately 10 times faster than the genetic algorithm because only one solution needs to be modified at each iteration. Performance of simulated annealing was essentially independent of the starting point, giving it an important advantage over random hill climbing. The genetic algorithm was not well suited to the strict adjacency problem because considerable computation time was necessary to repair the damage caused during crossover.     

三峡水库蓄水对消落带土壤Cu、Zn、Cr、Cd含量的影响

为揭示水位涨落形成系列淹水梯度对消落带土壤重金属含量的影响,论文采集三峡水库忠县消落带土壤,分析了不同淹水深度土壤Cu、Zn、Cr、Cd含量,并探讨了这些重金属元素物质来源及其与三峡水库水位涨落关系。结果表明：1）不同淹水深度下消落带土壤重金属含量差异较大,长期淹水土壤Cu、Zn、Cd含量远远高于短期淹水和未淹水土壤,Cr则在不同淹水深度略有变化;2）除Cr相对独立外,不同淹水深度下土壤SOM、pH与土壤Cu、Zn、Cd含量密切相关,但pH、SOM间仅有微弱的相关关系;3）因子分析中因子1和因子2对Cu、Zn、Cr、Cd的累计贡献率为93.47%,Cu、Zn、Cd在第一轴载荷值为0.962、0.967、0.925,Cr在第二轴载荷值为0.998。研究发现：1）重金属输入来源和流向异质性是决定不同淹水深度Cu、Zn、Cr、Cd含量差异的重要原因,长期淹水显著增加了土壤Cu、Zn、Cd含量,但对Cr效应并不明显;2）区域土壤内Cr主要来源为自然源的土壤侵蚀和母岩风化,Cu、Zn、Cd来源于人为输入的库岸输入和长江上游来水悬浮颗粒沉积,应引起足够重视。     

基于低碳效益的微电网能量优化调度

根据微电网中各分布式电源(DG)的特点,综合考虑各DG的安装、运行、维护成本、碳排放治理成本以及风电、光伏补贴等,分别构建各微源的低碳综合成本模型,在满足系统约束的条件下,提出一种计及微电网低碳效益的能量优化调度模型。深入挖掘微电网的低碳效益,确定微电网优化调度运行策略,依据构建的调度模型,应用混沌粒子群算法(CPSO)解决各个实用的运行策略中的优化调度问题。通过仿真验证了模型和算法的有效性,并对各运行策略及大电网供电对比分析,证实微电网的低碳效益明显。     

Coupling hydrologic and economic modeling for wetland management multi-optimization in Tram Chim National Park,Vietnam

We developed a comprehensive coupling framework with a multi-objective optimization that bridges a water balance model (WBM) and a wetland service model (WSM) to supporting wetland management. The framework was tested for management in Tram Chim National Park (with four wetland zones) where hydro-economic optimization was needed. The framework used (1) a model coupling process bridging WBM and WSM to create a modular hydro-economic model (MHEM), (2) a multi-objective optimization, and (3) an anneal scheduling for scenario optimization. The framework demonstrated its competency in identifying cause–effect/interaction flows (bridges) between WBM and WSM to design MHEM to simulate optimized scenarios; for the case study, the multi-objective optimization was met for all wetland zones. Results suggested a flexible consideration of management scales for optimization, i.e. hydrologic optimization at a zone level and net benefit optimization at a Park level. Our framework is applicable to supporting complex wetland decisions considering multiple objectives.     

A Routing and Scheduling System for Infectious Waste Collection

Most hospitals and clinics in Taiwan do not have on-site treatment facilities for their infectious waste and must rely on outside agencies for their collection and treatment. The problem of optimally routing and scheduling the collection of medical waste from a disperse group of facilities is recognized as a periodic vehicle routing problem. This study develops a computer system to solve the resulting optimization problem based on a two-phased approach proposed earlier. The first phase solves a standard vehicle routing problem to determine a set of individual routes for the collection vehicles. The second phase uses a mixed integer programming method to assign routes to particular days of the week. The computer system is user-friendly and consists of several Visual Basic programs while the geographical information system is incorporated to facilitate input and output interface and database management. An illustrative example for the infectious waste of 348 hospitals in the Tainan City area demonstrates the effectiveness of the system.     

大型环境地理信息系统的改进设计与实现研究

构建大型环境地理信息系统在提高环境保护的智能性方面具有重要意义,提出基于嵌入式ARM的大型环境地理信息系统设计方法.系统分为环境地理信息采集层、信息管理控制层和信息输出层,采用多线程的套接字技术进行环境信息过滤,采用TCP/IP以太网技术构建大型环境地理信息管理的信息资源共享模块,在云计算环境中进行地理信息的在线查询和资源调度.以嵌入式ARM9TDMI为内核微处理器,实现大型环境地理信息系统的软件开发,用ARM片上控制器来完成地理信息系统的读写和数据访问操作.系统测试结果表明,设计的大型环境地理信息系统对环境地理信息存储和调度的吞吐性能较好,数据输出的误码较低,信息系统具有很好的人工智能性.