基于BP神经网络算法的密云水库水质参数反演研究

密云水库是北京市重要的地表饮用水源地,在保障首都水源安全方面起着重要作用,而密云水库水质参数的区域监测已成为当前亟待解决的问题。为了实现密云水库水质参数大范围、实时获取,该文基于遥感和GIS技术,采用BP神经网络算法,结合地面监测数据和Landsat 8遥感影像,分别建立了反演总磷、总氮、氨氮和COD(化学需氧量)4个水质参数的BP神经网络模型,并反演了密云水库2013-2018年非结冰期主要水质参数,分析了密云水库主要水质参数的年际变化特征、季节变化特征和空间分异特征。结果表明,(1)水质参数的Landsat 8敏感波段分别为:总氮为1、4波段,氨氮为1-7波段,总磷为1、3-7波段,COD为2-5波段。(2)密云水库主要水质参数在2013-2018年总体呈下降趋势,氨氮和COD为Ⅰ类水质,总磷为Ⅱ类水质,总氮为Ⅲ类水质。(3)4个水质参数指标春季最高、秋季次之、夏季最低,总氮、总磷、氨氮和COD的春季值分别是夏季值的1.08、1.36、1.6、1.45倍。(4)密云水库不同水质参数的空间差异性较大,总体来看,水库北部和东部的4个水质参数含量相对较高,这种分布与北部和东部村庄密集以及密云水库两大入库河流有关。综上所述,基于BP神经网络算法的密云水库水质反演研究是可行的,且得到了较为可信的研究结果,该研究可为密云水库水质管理与政策制定提供重要的科学依据。     

Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites

Summary. Individuals in an insect colony need to identify one another according to caste. Nothing is known about the sensory process allowing nestmates to discriminate minute variations in the cuticular hydrocarbon mixture. The purpose of this study was to attempt to model caste odors discrimination in four species of Reticulitermes termites for the first time by a non-linear mathematical approach using an "artificial neural network" (ANN). Several rounds of testing were carried out using 1 – the whole hydrocarbon mixtures 2 – mixtures containing the hydrocarbons selected by principal component analysis (PCA) as the most implicated in caste discrimination. Discrimination between worker and soldier castes was tested in all four species. For two species we tested discrimination of four castes (workers, soldiers, nymphs, neotenics). To test cuticular pattern similarity in two sibling species (R. santonensis and R. flavipes), we performed two experiments using one species for training and the other for query. Using whole hydrocarbons mixtures, worker/soldier discrimination was always successful in all species. Network performance decreased with the number of hydrocarbons used as inputs. Four-caste discrimination was less successful. In the experiment with the sibling species, the ANN was able to distinguish soldiers but not workers. The results of this study suggest that non-linear mathematical analysis is a good tool for classification of castes based on cuticular hydrocarbon mixture. In addition this study confirms that hydrocarbon mixtures observed are real chemical entities and constitute a true chemical signature or odor. Whole mixtures are not always necessary for discrimination. Received 23 July 1998; accepted 9 October 1998.     

MCAI资源管理的信息化

要实现培养创造力人才的目标,教育信息化是最有效途径之一,教育信息化要从资源(包括多媒体辅助教学资源)库整合、资源管理信息化(ERM)等多方面着手,并使它们协调运行才能有效推进教育信息化进程.整合MCAI资源库,建立资源管理信息化系统的实现流程,共建共享资源,快速推进教育信息化进程.     

江苏规划高速公路网生态环境影响研究

以RS和GIS为基础，运用遥感信息提取、编制自然保护专题系列图、系列图与规划高速公路网叠加、缓冲分析、统计分析等方法，对江苏省规划高速公路网生态环境影响进行分析。规划的5纵9横6联线路中的9条，对127个不同类型保护区中的9个有影响。11个过江通道中的5个对沿江饮用水源保护地有影响。规划高速公路占用的土地中76．74％是基本农田。跨越太湖的沪宁复线将加重梅梁湾、五里湖的水质富营养化。针对上述问题，从保护生态环境的角度提出6大对策：避绕生态保护对象，修改跨太湖线路方案，合理规划穿越山地丘陵区的线路，减少平原区生态破坏，调整过江通道与饮用水源地的交叉关系，保护白鳍豚生存环境安全。     

基于人工神经网络模型预测ABR反应器处理效率

ABR反应器的处理效率受多方面的因素影响，本文通过BP人工神经网络，利用ABR反应器进水CODcr浓度、容积负荷、温度、稳定运行时间四个参数对其反应器处理效率进行预测。结果表明，BP人工神经网络可较好的用于ABR反应器处理效率的预测，具有较高的精度，在实际生产中，可以运用人工神经网络，对ABR反应器的运行参数进行调整，使之达到最优化的运行状态。     

人工神经网络方法用于城市环境空气质量综合评价

采用人工神经网络技术,建立了城市环境空气质量综合评价的ANN模型,并通过实例对建立的评价模型进行验证。结果证明,ANN模型用于区域环境空气质量评价,其评价结果比较直观。同时,对在建立和应用ANN模型时应注意的一些问题进行了讨论。     

三维铝型材挤压模多参数优化

以一个导流组合模为例,利用神经网络建立模具参数的数学模型,用遗传算法对参数优化。其中由正交法设计得到实验样本,由软试模提取模具的SDV值,将其作为优化目标。最终实验结果与优化值吻合,为建立和控制挤压模参数提供了一种行之有效的手段。     

合肥台CTS-1分析系统网络组建

本文阐述了利用UNIX和WIN98/2000互联的局域网技术,对CTS-1系统进行优化配置,组建CTS-1网络分析系统,实现CTS-1系统数据共享,提高地震分析的效率,确保数据备份。利用代理软件用一个MODEM让各个子机同时上网,同时充分利用台站现有的计算机设备,最大限度的实现地震监控、速报、分析以及地震研究的同时开展。     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

模糊神经网络控制在污水处理中的应用研究

对模糊神经网络(FNN)技术应用于污水处理进行了研究,提出了一个具有五层的模糊神经网络控制器,仿真实验表明,该控制系统具有很强的鲁棒性与容错性.该控制器能够自动调整隶属度函数、动态优化控制规则,将其应用于溶解氧控制和出水水质预测,结果表明可以快速、有效地使溶解氧浓度达到期望值,实际出水水质预测结果也具有很好的收敛性和预测精度.