基于气象服务信息的应急物资配送路径选择

为构建科学的应急救援体系,基于气象服务信息制定更合理的应急物资配送路径方案。将突发灾害下典型的"单出救点、单受灾点"应急物资配送网络结构作为研究对象。运用衰减系数反映气象条件对物资配送速度的实时影响。构建以满足时间限制和需求量要求为前提的应急物资配送路径选择模型。设计算例进行数值仿真和参数分析,将求解结果与传统最短路径法所选路径方案进行对比分析。结果表明:气象服务提供的衰减系数和途经各区域灾害强度等关键信息对配送路径的优化决策有重要影响。     

Dijkstra算法在确定矿井通风最大阻力路线中的应用

矿井通风最大阻力路线的确定,是矿井通风系统设计及改造的主要任务之一。对Dijkstra算法进行研究、改造,使其适用于求解矿井通风最大阻力路线,并根据该算法编写了计算机程序,将其应用于典型风网,获得了正确的结果。Dijkstra算法的引入为确定最大阻力路线提出了一种新的方法,同时计算机的应用也提高了计算的效率及准确性。     

基于QPSO-RBF的瓦斯涌出量预测模型

为了提高径向基(RBF)网络预测瓦斯涌出量的泛化能力,提出QPSO-RBF模型。该模型采用量子粒子群(QPSO)算法优化RBF网络隐层基函数中心、扩展系数以及输出权等初始参数,将网络参数编码为QPSO学习算法中的粒子个体,在全局空间中搜索最优适应值参数。其中,RBF网络选取5-3-1的精简结构,采用5个变量作为影响因子预测瓦斯涌出量。结果表明,经QPSO优化后的RBF网络模型预测结果稳定且唯一,其泛化指标平均相对变动值(ARV)为0.012 2。与PSO-RBF、RBF模型预测结果比较,QPSO-RBF模型的泛化能力和网络训练速度优于前2种;预测精度约为PSO-RBF模型的1.5倍、RBF模型的4倍。     

Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China

In this study, we applied Bayesian networks to prioritize the factors that influence hazardous material (Hazmat) transportation accidents. The Bayesian network structure was built based on expert knowledge using Dempster-Shafer evidence theory, and the structure was modified based on a test for conditional independence. We collected and analyzed 94 cases of Chinese Hazmat transportation accidents to compute the posterior probability of each factor using the expectation-maximization learning algorithm. We found that the three most influential factors in Hazmat transportation accidents were human factors, the transport vehicle and facilities, and packing and loading of the Hazmat. These findings provide an empirically supported theoretical basis for Hazmat transportation corporations to take corrective and preventative measures to reduce the risk of accidents.     

基于FXLMS算法的矿用有源耳罩的研究

基于矿井中低频噪声突出,存在时间长、危害大,尤其周期性噪声比重较大,而传统降噪技术对其降噪效果并不明显,提出了基于fxlms 算法的自适应有源降噪耳罩降低矿井中的低频周期性信号,并采用离线建模方法建立了次级通道模型,利用MATLAB8.0拟合了64阶的滤波器,对 矿井中具有代表性的1kHz低频周期噪声和伴有随机噪声的1kHz低频周期性噪声进行了降噪仿真研究。结果表明:1kHz低频周期性噪声的降噪量 达到了30dB。而且在本模型下,随机噪声对低频周期性噪声的降噪效果几乎没有影响。     

基于铀尾矿库核素污染监测WSN分簇路由协议

用WSN对铀尾矿库核素污染监测优于传统有线布置,为解决无线传输路由能耗的问题,提出了一种基于蚁群算法的前向传输分簇路由协议。该协议以分簇的方式来减少数据发送量与寻优开销,在下一跳接收节点选择阶段,以前向传输区域为切入点,利用优化的蚁群算法进行多路径搜索最后选出满足条件的最优路径。仿真实验表明,CFTA协议的网络生存周期有效延长,并减小了能耗。     

基于LSSVM的石灰石浆液制取电耗优化

提出了基于最小二乘支持向量机(LSSVM)构造以球磨机制浆速度作为目标优化模型,并采用遗传算法实现了给料量、给水量、漩流器入口压力等运行参数的寻优,获得了优化制浆速度的调整方式。以此方式的相应参数作为当前负荷下的基准值,能很好地解决制浆工况下运行参数基准的确定问题。通过现场试验证明:按此基准值运行,可以提高制浆速度,降低电耗。     

Modeling Habitat Suitability for Complex Species Distributions by Environmental-Distance Geometric Mean

This paper presents a new habitat suitability modeling method whose main properties are as follows: (1) It is based on the density of observation points in the environmental space, which enables it to fit complex distributions (e.g. nongaussian, bimodal, asymmetrical, etc.). (2) This density is modeled by computing the geometric mean to all observation points, which we show to be a good trade-off between goodness of fit and prediction power. (3) It does not need any absence information, which is generally difficult to collect and of dubious reliability. (4) The environmental space is represented either by an expert-selection of standardized variables or the axes of a factor analysis [in this paper we used the Ecological Niche Factor Analysis (ENFA)].We first explain the details of the geometric mean algorithm and then we apply it to the bearded vulture (Gypaetus barbatus) habitat in the Swiss Alps. The results are compared to those obtained by the median algorithm and tested by jack-knife cross-validation. We also discuss other related algorithms (BIOCLIM, HABITAT, and DOMAIN). All these analyses were implemented into and performed with the ecology-oriented GIS software BIOMAPPER 2.0.The results show the geometric mean to perform better than the median algorithm, as it produces a tighter fit to the bimodal distribution of the bearded vulture in the environmental space. However, the median algorithm being quicker, it could be preferred when modeling more usual distribution.     

Automatic Calibration of Hydrologic Models With Multi‐Objective Evolutionary Algorithm and Pareto Optimization1

Abstract: In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade‐offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi‐objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process‐based, semi‐distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA‐II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3‐year period. The daily Nash‐Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi‐objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy‐making problems related to conflicting objectives of economics and environmental quality.     

MODELING AND SENSITIVITY ANALYSIS FOR PLANNING DECISIONS IN WATER RESOURCES EXPANSION1

ABSTRACT. Individuals and organizations concerned with the expansion of the facilities of a river basin (such as a river basin authority) need to determine optimal strategies of operation and capital investment. They also need to examine the sensitivity of whatever planning decisions are contemplated. This paper extends the applicability of an algorithm that had been previously applied to the deterministic river-basin expansion problem to include the feature of a sensitivity analysis. The algorithm, containing a partial enumeration search technique and a network analysis code, gave a construction sequence of reservoirs, canals, and treatment plants, and an operating policy that maximized the present value of net earnings consistent with certain underlying assumptions. A river basin was chosen that had an existing configuration of unregulated streams and rivers, reservoirs, canals and treatment plants, and sites for future additional facilities. A series of representative synthetic flow sequences, future demand profiles, interest rates and reservoir costs that served as inputs to or parameters in the system were each perturbed by various factors (for a total of 24 cases). The sensitivity studies showed that the immediate planning decision of what facility to construct next was insensitive to variations in future demands and costs and independent of later decisions. Thus, decision-making was adaptive in the sense that by always making the optimal proximate decision, the management of the river basin is optimized.