BP神经网络在焉耆盆地农田排水量

利用BP神经网络技术对焉耆盆地农田排水量进行预测。利用灰色关联度分析确定了排水量与各影响因素的关系，选取了对排水量影响最大的5个因素作为BP网络的输入，利用均匀设计方法，确定了最优的神经网络结构。估算结果表明利用BP神经网络可以准确的估算农田排水量，最大相对误差仅为-2.45％。     

计算机网络安全分析及对策

随着计算机技术网络的快速发展,网络安全成为人们研究的焦点。本文分析了网络攻击的主要方式和网络安全的关键,举例让我们对病毒的攻击有深入了解,再采用相应的技术防范。     

自动化综合实验网络系统的构建与应用

介绍了实验室现有的多个实验工艺流程装置以及控制系统的特点,描述了在实验室环境中应用常规控制系统、集散控制系统、现场总线控制系统与工业以太网控制系统实现对多个工艺流程装置的自动控制,以及研究自动化综合实验网络的构建、智能控制理论与方法的运用和教学实践的应用.     

论农业机械化在新农村建设中的重要作用

以新农村建设带给农机化新的发展机遇为主线,结合武汉市以实施农村"家园建设行动计划"为抓手,建设社会主义新农村的实际,论述农业机械化在新农村建设中发挥的重要作用。     

无线局域网在解放军医学图书馆的应用

 

结合解放军医学图书馆无线局域网络的实际应用情况，从无线局域网的概况、应用特点及安全防护等几方面介绍了无线局域网络的相关知识。

     

绥化市秦家镇农田防护林网络架构及生态农业效益分析

对松嫩平原上一典型土地开发整理项目区深入调查研究基础上,在人均土地面积较少的农村,构建出由农田防护林、水利排灌、道路、农田4项网络构成的高效农田防护林复合经营与多样型林带乔、灌立体混交经营模式,实现了林带由单一结构向多层次的立体结构转移,架构了生态立体农业体系,旨为重新调整土地利用结构,实现山、水、田、林、路的全面治理提供依据。     

一种改进深度卷积神经网络的海岛识别方法

受不规律潮汐的影响,现有的海岛地物类别自动识别方法存在精度低和时效性差等问题,通过改进深度卷积神经网络提出了一种基于遥感影像的海岛快速识别方法:(1)在深度卷积神经网络的卷积层中增设1×1的卷积核作为瓶颈单元,对多波段的遥感影像进行降维;(2)在池化层引入了重采样方法,基于灰度值对海量的遥感影像进行特征压缩。以300景Landsat-8遥感影像为源数据,分别采用CNN、RCNN和本文改进的深度卷积神经网络对遥感影像中的海岛进行识别,实验结果表明:(1)改进的深度卷积神经网络降低了海岛识别的计算耗时,其计算耗时仅为CNN的4.56%和RCNN的5.60%;(2)改进的深度卷积神经网络较CNN和RCNN提高了海岛识别的精度,识别精度分别为96.0%、93.3%和95.0%。结果说明,改进的深度卷积神经网络适用于面向遥感影像的海岛自动识别。     

基于粒子群算法优化BP神经网络的土壤含水量短期预测模型

为提高土壤含水量预测精度,基于物联网监测数据,提出了粒子群算法(PSO)优化BP神经网络的土壤含水量预测方法。首先应用主成分分析法筛选出影响土壤含水量的关键影响因子,然后构建8-5-1的BP神经网络拓扑结构,应用粒子群算法优化BP神经网络的初始权值和阈值。结果表明:与传统BP神经网络相比,新模型优化了网络结构,避免了陷入局部最优解,具有良好的预测效果;模型的评价指标平均绝对误差、平均绝对百分误差、误差均方根分别为0.259 2、0.010 5和0.135 6,与单一BP神经网络相比,预测精度更高,可满足实际的土壤含水量预测的需要。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.