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.     

基于数字孪生技术的平面渔网破损检测方法研究

为防止渔网破损造成养殖鱼类逃逸,有必要对网衣进行破损检测。为了克服人工检测劳动强度大且效率低下的缺点,实现渔网的精准实时监测,本研究提出了一种基于数字孪生的网衣破损检测方法,可利用传感器代替人工监测。该方法首先从渔网的数值仿真模型获取大量的仿真传感数据,然后将数据用于人工神经网络的训练与测试,最后生成可进行网衣破损识别的数字孪生体。数字孪生体可根据传感器监测到的数据来判断网衣是否发生破损。在数值模拟中,考虑各种波浪条件以及网衣的破损情况。在训练人工神经网络中,将有效波高Hs、谱峰周期Tp以及横纲竖纲的拉力值作为输入变量,将网衣完整状态以及破损状态作为输出。经过测试分析,该识别模型根据传感器数据识别网衣是否破损的平均准确率为94.32%,由此可见,数字孪生技术能准确检测到渔网的损坏,可以作为网衣破损检测的一种新方法。     

运用神经网络评价水淹和未水淹条件下灰化苔草光合特性的变化

在天鹅洲湿地,原位测定了在水淹及未水淹条件下灰化苔草(Carex cinerascens Kükenth.)的光合特征,利用神经网络对2种条件下灰化苔草的光合特征进行了研究,全面评价了不同条件下灰化苔草光合速率对环境因子响应程度。结果表明:水淹导致灰化苔草光合速率下降。通过神经网络,可以很好地预测灰化苔草光合速率的响应,水淹和未水淹2种条件下,气孔导度(gs),蒸腾速率(E),气温(Ta),叶温(Tl)和外界水分含量(Ha)均表现出极大的差异。     

河南省农用地集约利用与农民收入增长关系动态计量经济分析

农用地集约利用水平关系到区域粮食安全,与促进经济发展方式转变息息相关,对农用地集约利用水平进行测度并对农用地集约利用与农民收入水平进行动态研究,可以为区域土地可持续发展提供重要参考。本研究利用河南省1978—2012年的相关数据,通过构建适合河南省农用地集约利用评价的指标体系,借助BP神经网络对河南省农用地集约利用水平进行综合测度,并在此基础上采用计量经济分析考察农用地集约利用与农民收入之间的动态联系。研究表明:1978—2012年间,河南省农用地集约利用水平整体呈现上升趋势。农民收入水平与农用地集约利用指数互为Granger因果关系,农用地集约利用指数每提高1个单位,会引起农民收入提高0.628 7个单位。脉冲响应函数和方差分解结果显示,农用地集约利用水平的提高将促进农民收入的增长,且对农民收入变化的解释能力随着时间推移逐渐增强。     

基于ANN的轴流脱粒与分离装置性能指标预测

影响水稻轴流脱粒与分离装置性能指标的因素很多,并且它们之间存在着复杂的非线性关系,用传统方法很难对其进行准确预测。神经网络算法简单、学习收敛速度快、具有线性、非线性逼近精度高等特性。本文以正交旋转组合实验获得的数据作为样本,对人工神经网络(ANN)模型进行训练学习,利用训练后所得到的模型,对性能指标进行了预测。结果表明,网络预测值与实测值之间具有很高的相关性和精确度,为机械性能指标研究提供了一定的理论辅助手段。     

引入因素分析法的神经网络在洪水

因素分析法是一种多元统计法，可以通过线性变换，将原有数据转换成彼此不相关且维数较少的数据，本文将因素分析法引入洪水预报神经网络的输入单元精简过程，详细介绍了建模和神经网络训练过程中包括STA训练控制在内的一些关键技术问题，并通过实例评价了此应用方法的效果，通过分析，表明此方法可以在不损失或较小损失的前提下方便有效的对洪水预报神经网络结构进行精简，大大缩减了神经网络的规模，提高了效率，具有很高的实用价值。     

Signal Processing of Electroencephalogram and It''s Application

Electroencephalogram (EEG) signals are the electrical activities in the cortex or on the surface of scalp caused by the physiological activities of the brain which play a key role in the diagnosis of brain and the functional determination of brain. The authors discuss some methods for processing EEG signals from the view of the frequency domain and time domain, especially introduce some time-frequency analysis methods such as Wigner distribution, wavelet transform and matching pursuit etc. , ANN and non-linear analysis for EEG signals processing.     

人工神经网络与近红外光谱分析

介绍了人工神经网络的原理、结构、算法和研究进展,以及该方法在近红外光谱分析中的重要地位和应用。     

基于人工神经网络的组合预测及应用

综合组合预测和人工神经网络的特点，提出一种将人工神经网络应用于组合预测的新方法，并以浙江省猪肉产量预测为实例进行了论证。     

电力系统中谐波诊断的神经网络方法

本文提出了一种应用人工神经网络模型诊断电力系统中谐波含量的新方法。用误差反向传播模型（ＢＰ模型）取代传统的快速傅立叶变换方法，对ＢＰ模型进行训练，得到由电流（或电压）采样值映射出基波和谐波含量的ＢＰ模型。试验结果表明，该方法具有速度快、精度高的优点，硬件实现简单，同时具有在线应用的特点。