GNSS转发式欺骗干扰方法的改进     

何婷 《测绘通报》2019,(4):71-74,83

针对GNSS多天线转发式欺骗干扰在实际应用中，当干扰机与目标机距离大于一定范围时，将引起目标机钟差突跳，从而易被目标机检测和识别的缺陷，提出了基于干扰机阵列的转发式欺骗干扰新方法。干扰机阵列按照正六边形网型布设，可实现目标区域的无缝覆盖，并且灵活易拓展。不论目标机位于区域的任何位置，均有一个最优干扰机能够对其实施有效干扰。为了确定相邻干扰机的最优间距，本文在干扰机阵列不同间距下对具有钟差突跳自适应检测能力的目标机的干扰有效性进行了仿真研究。结论表明，综合考虑各种约束因素，相邻干扰机最优间距为17 km，满足该条件的干扰机阵列在实施欺骗干扰过程中，不会造成目标机钟差突跳，有效解决了该干扰方式易被目标机识别的问题。  相似文献   

Supply and Demand Levels for Livestock and Poultry Products in the Chinese Mainland and the Potential Demand for Feed Grains     

HUANG Shaolin  LIU Aimin  LU Chunxia  MA Beibei 《资源与生态学报(英文版)》2020,11(5):475-482

The widening gap between the supply and demand levels for livestock and poultry products in the Chinese mainland poses a significant challenge to the secure supply of feed grains. Therefore, the accurate prediction of the demand potential for feed grains represents a key scientific issue for ensuring food security in the Chinese mainland. This study is based on an analysis of several factors, such as the Chinese mainland’s output, trade volume, apparent consumption of livestock and poultry products, and two different scenarios for predicting the future demand for feed grains are assessed. The results indicate that output and consumption of livestock and poultry products, as well as the country’s trade deficit and the pressure of the supply and demand balance with respect to these products, have been increasing in recent years. The analysis predicts that the demand for feed grains in the Chinese mainland will reach 425.5 or 389.6 million tons in 2030 based on the two scenarios. This finding indicates that with the increasing demand for livestock and poultry products in the Chinese mainland, the demand for feed grains will continue to increase, and the shortfall in feed grains and raw materials will expand further, especially dependence on external sources of protein-rich feed grains will remain high.  相似文献   

Obtaining the distribution of quiescent periods directly from the power spectral densities of Sea waves     

《Applied Ocean Research》2019

There is a growing practical interest in the ability to increase the sea states at which marine operations can be safely undertaken by exploiting the quiescent periods that are well known to exist under a wide range of sea conditions. While the actual prediction of quiescent periods at sea for the control of operations is a deterministic process, the long term planning of future maritime tasks that rely on these quiescent periods is a statistical process involving the anticipated quiescence properties of the forecasted sea conditions in the geographical region of interest. It is in principle possible to obtain such data in tabular form either large scale simulation or from field data. However, such simulations are computationally intensive and libraries of appropriate field data are not common. Thus, it is clearly attractive to develop techniques that exploit standard wave spectral models for describing the quiescence statistics directly from such spectra. The present study focuses upon such techniques and is a first step towards the production of a computationally low-cost quiescence prediction tool and compares its efficacy against simulations. Two significant properties emerge for a large class of wave spectral models that encompasses the ubiquitous Neumann and Pierson Moskowitz or Bretschneider forms. Firstly, the auto-correlation function of the wave profile that are required to produce the quiescence property can be obtained analytically in terms of standard special functions. This considerably reduces the computational cost making desktop computer-based planning tools a reality. Secondly, for each class of these parametric spectra, the probability of a given number of consecutive wave heights (normalised to the significant wave heights) less than some critical value is in fact independent of absolute wave height. Thus, for a broad class of practically interesting wave spectra all that is required to obtain the statistical distribution of the quiescent periods is simple rescaling.  相似文献   

基于回归模型的热带气旋条件下的海表降温参数化方案设计     

韦骏  刘欣  蒋国庆 《海洋学报(英文版)》2018,37(1):1-10

Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature(SST) cooling induced by tropical cyclones(TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling(SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables:sea surface height(SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.  相似文献   

Stokes漂流对全球海表面温度模拟的影响     

肖林  史剑  蒋国荣  刘子龙  张成成 《海洋通报》2018,(1):55-87

Stokes漂流对海洋上混合层中的流场和温度场结构具有不可忽视的作用。本文基于WAVEWATCHⅢ海浪模式模拟的海浪要素计算得到Stokes漂流,将其引入SBPOM模式的动量方程中,从体积输运的角度研究Stokes漂流对全球海表面温度的影响。分析发现Stokes漂流与Stokes输运在全球呈现高纬度强于中低纬度的带状分布特征,且这种流动与输运对全球海表面温度具有降温作用,该降温作用的分布与全球Stokes输运强度相对应,高纬降温作用大于中低纬度,特别是南极绕极流海域平均降温明显大于其余海域,最大降温可达1.5℃,且全球月平均降温超过0.1℃。  相似文献   

Estimation and prediction of plastic waste annual input into the sea from China   总被引：5，自引：0，他引：5  

BAI Mengyu  ZHU Lixin  AN Lihui  PENG Guyu  LI Daoji 《海洋学报(英文版)》2018,37(11):26-39

Marine plastic debris has been a pervasive issue since the last century, and research on its sources and fates plays a vital role in the establishment of mitigation measures. However, data on the quantity of plastic waste that enters the sea on a certain timescale remain largely unavailable in China. Here, we established a model using material flow analysis method based on life cycle assessment to follow plastic product from primary plastic to plastic waste with statistical data and monitoring data from accurate sources. This model can be used to estimate and forecast the annual input of plastic waste into the sea from China until 2020. In 2011, 0.547 3–0.751 5 million tons of plastic waste entered the seas in China, with a growth rate of 4.55% per year until 2017. And the amount will decrease to0.257 1 to 0.353 1 million tons in 2020 under the influence of governmental management. The amount of plastic waste discharged from coastal areas calculated in this study was much larger than that from river, thus it is suggested to strengthen the governance and control of plastic waste in coastal fishery activities in China in order to reduce the amount of marine plastic waste input.  相似文献   

灰色稳健总体最小二乘估计及高铁路基变形预测     

陈洋  文鸿雁  覃辉  王清涛  周吕 《大地测量与地球动力学》2018,38(2):140-146

最小二乘估计和部分变量误差模型的总体最小二乘估计不具备抵御粗差的能力。鉴于粗差可能同时出现在灰色白化微分方程的观测值和系数矩阵中，本文提出基于IGGⅢ抗差方案的部分变量总体最小二乘稳健估计。结合仿真数据和高铁路基观测数据，系统地比较稳健最小二乘、部分变量总体最小二乘、本文算法参数估计结果和算法稳定性。结果表明，本文算法预测精度高，可以应用到高铁路基沉降预测中。  相似文献   

近30 a甘肃省河东地区极端气温指数时空变化特征及趋势预测          下载免费PDF全文

黄浩  张勃  黄涛  王怀军  马尚谦  马彬  王晓丹  崔艳强 《干旱区地理》2020,43(2):319-328

基于甘肃省河东地区61个气象站点1988—2017年逐日气温数据，利用Mann-Kendall检验，Sen’s斜率估计方法分析甘肃省河东地区极端气温指数的时空变化趋势，并探讨极端气温指数与其影响因素之间的关系，最后利用NAR神经网络结合Hurst指数对甘肃省河东地区极端气温指数变化进行预测分析。结果表明：(1)从时间上看，冷极值相对指数呈下降趋势，冷极值绝对指数、暖极值以及气温日较差、作物生长期呈上升趋势。(2)从空间上看，对冷极值变化反应最为敏感的是高寒湿润区，对暖极值变化反应最为敏感的是温带半湿润区和北亚热带湿润区，除北亚热带湿润区外各区域作物生长期的变化都达到了显著水平，而气温日较差仅在温带半湿润区达到了显著水平。(3)多数极端气温指数与经纬度、海拔之间有显著相关性，但受区域自然特点影响，经度与海拔对其影响实为一类。(4)亚洲区极涡强度、北半球极涡强度以及青藏高原指数B与极端气温指数变化有密切关系，而太阳黑子等只与个别指数之间存在显著的相关性。(5)预测出的极端气温指数冷极值相对指数仍呈现下降趋势，冷极值的绝对指数、暖极值以及气温日较差、作物生长期仍然呈现增加趋势，但大多数指数与1988—2017年相比变化幅度有所降低。（6）与其他区域相比甘肃省河东地区大多数气温指数变化幅度处于中间水平，表现出其为多种不同气候区、自然区交界地带的特色。  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

基于多变量LSTM神经网络的地下水水位预测     

闫佰忠  孙剑  王昕洲  韩娜  刘博 《吉林大学学报(地球科学版)》2020,50(1):208-216

为解决以往模型未考虑地下水位相关影响因素的问题，探讨长短期记忆（LSTM）神经网络在地下水位预测中的应用，利用长短期记忆神经网络，采用多变量输入的方式，构建了基于多变量LSTM神经网络的地下水水位预测模型。以泰安市岱岳区J1号监测井为例，采用2001-2014年地下水水位动态监测资料与相关影响因素数据，利用多变量LSTM神经网络对2015-2016年地下水位进行预测，并与单变量LSTM神经网络和反向传播（BP）神经网络进行对比。研究结果表明：以相关影响变量为输入的BP神经网络无法考虑时序变化规律，预测均方根误差最大，为2.399 3；以地下水位为变量输入的单变量LSTM神经网络仅能根据时序变化作出相应预测，无法考虑相关变量影响，预测均方根误差为2.102 2；基于多变量输入的LSTM神经网络的预测精度显著高于单变量LSTM神经网络和BP神经网络，预测均方根误差最小，仅为1.919 1。总体上，多变量LSTM神经网络地下水位预测模型仅在某些峰值处误差较大，但总体预测效果较为理想。  相似文献