基于IPv6的地震传感器网络及其应用前景展望

基于IPv6的地震传感器是将与地震相关的信息作为感知对象的综合传感器, 具备接入各种符合标准的物理量或信息量的功能. 地震传感器网络的功能结构可分为5个层面, 即基础层、网络层、数据管理与处理层、应用开发环境层及应用层. 具有端到端连接、服务质量（QoS）、安全性、多播、移动性、即插即用等IPv4环境不易实现的功能. 2005年, 我国IPv6将进入实质性的应用发展阶段, 我们需要抓住国家下一代互联网发展契机, 尽快开展基于IPv6的地震传感器网络技术的开发与试验, 以服务于我国地震监测预报、震害防御和紧急救援三大体系建设.     

基于前置机的空间数据共享交换技术研究及应用

为了解决空间数据共享交换的安全性问题,设计了"交换中心+交换前置机"的解决方案,开发了数据共享交换管理系统,并在顺德区政务地理信息平台中进行应用,实现了基础空间数据和专题空间数据在政务内网与公安专网、三防专网等不同网络环境和部门之间的共享和交换。     

高分辨率遥感影像建筑物提取多路径RSU网络法

针对卷积神经网络在提取建筑物的过程中,存在建筑物边界不准确和建筑物内部空洞等问题,提出以RSU模块（residual U-block）为核心的MPRSU-Net （multi-path residual U-block network）。该模块利用编码器-解码器结构和残差连接,实现了局部特征和多尺度特征的融合。由于一个RSU模块提取的信息有限,MPRSU-Net进一步通过多路径结构并行了不同尺度的RSU模块,并在这些模块之间进行信息交换,提高了特征聚集效率。在分辨率为0.3 m的WHU和Inria建筑物数据集上进行试验,精度分别达95.65%和88.63%,IoU分别达91.17%和79.31%,验证了本文方法的有效性。此外,本文方法相较于U²Net,计算量明显降低,模型参数量减少68.63%,表明本文方法具有一定的应用价值。     

美国国家生态观测站网络(NEON)--概念、设计和进展

美国国家生态观测站网络（NEON）是一个以研究区域至大陆尺度重要环境问题的国家网络,它是由美国国家科学基金会（NSF）于2000年提出建立的。NEON的目标是通过网络式的观测、试验、研究和综合分析,阐明环境变化的成因和后果,预测环境变化的趋势并提出相应的对策。它是一个由17个区域网络组成的国家生态学研究和环境教育平台。该网络的建成,将为促进生态学的发展、人才培养以及保障美国的生物和生态安全发挥重要作用。由美国国家科学院国家研究委员会的地球与生命研究部任命的专家委员会于2003—2004年对NEON的原设计和建设方案进行了评估,充分肯定了建立NEON的必要性,但同时对原设计和建设方案提出了一些重大修改意见。新的方案建议NEON应重点关注生物多样性、物种组成与生态系统功能、生物地球化学循环的生态学影响、气候变化的生态学内涵、传染病的生态学和演化、入侵种以及土地利用和栖息地的丧失这6个美国国家层次当前所面临的最严峻的环境挑战。未来的NEON将由研究这些问题所设置的6个国家网络组成。深入研究NEON的概念、设计方案和实施计划,对于我国国家生态观测和研究网络的建设具有重要的参考意义。     

Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error(RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia(POAMA)-1.5 general circulation model(GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.     

基于神经网络方法的不确定非线性微分-代数子系统的鲁棒反推镇定控制

对于指数1且关联可测的不确定非线性微分-代数子系统,将反推方法和神经网络相结合,研究了其鲁棒渐近镇定控制问题.基于反推方法来构造镇定控制器,利用3层的神经网络来逼近每一步控制器构造过程中的不确定项.提出一种新的自适应算法对神经网络权值进行在线调节,并适当选取每一步虚拟控制器的参数,最终得到的控制器使得闭环系统是渐近稳定的.     

A hybrid method for grade estimation using genetic algorithm and neural networks

In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP) network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore, the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted when the training data set is small.     

Identifying wildland fire ignition factors through sensitivity analysis of a neural network

Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network’s weights after the training procedure (i.e., the percentage of influence—PI, the weight product—WP, and the partial derivatives—PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables’ importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.