Assessing seawater quality with a variable fuzzy recognition model

With the rapid development of the marine economy industry, human exploitation of marine resources is increasing, which is contributing to the growing trend of eutrophication and frequent occurrence of red tide. Accordingly, investigations of seawater quality have attracted a great deal of attention. This study was conducted to construct a seawater environmental quality assessment model based on the variable fuzzy recognition model. The uncertainty and ambiguity of the seawater quality assessment were then considered, combining the monitoring values of evaluation indicators with the standard values of seawater quality. Laizhou Bay was subsequently selected for a case study. In this study, the correct variable model for different parameters was obtained according to the linear and nonlinear features of evaluation objects. Application of the variable fuzzy recognition model for Laizhou Bay, water quality evaluation and comparison with performance obtained using other approaches revealed that the generated model is more reliable than traditional methods, can more reasonably determine the water quality of various samples, and is more suitable for evaluation of a multi-index, multi-level, nonlinear marine environment system; accordingly, the generated model will be an effective tool for seawater quality evaluation.     

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基于模糊小波网络的伺服系统参数辨识研究

伺服系统大多是非线性系统,难以对其建立准确的控制模型,其位置、和速度检测信号易受干扰,而小波网络具有多分辨率特性和任意逼近能力。利用其非线性映射能力对系统的输入输出关系进行模拟,将小波网络和模糊规则结合对系统的位置和速度进行辨识,动态调整网络的权值W和模糊规则,将非线性映射的问题转化为求解系统最优解,从而产生一种新的系统辨识方法,并以永磁伺服系统为例,设计了辨识的结构模型和策略,实验表明该算法可以达到较高的系统控制要求。     

青海省土地整治项目效益评价研究

本文选取投入产出比、人均收入增量、静态投资回报期、生产经营改善系数、万元新增耕地面积、路网占地比、土地利用率变化率、人均耕地面积增加量、农户支持率、土地垦殖率变化率、粮食单产增量、绿色植被覆盖率、灌溉水利用系数等13项定量化指标及土地权属调整状况、土壤质量改良程度2项定性指标来构建指标体系,采用层次分析法确定指标权重,模糊综合评价法构建评价模型,分别以青海省四个土地利用功能区中已完工的12个项目作为评价实例,将评价的指标体系与评价模型应用于评价实例中,并分析评价结果。评价结果与实际情况基本吻合,从侧面验证了本次评价体系的合理性与可靠性,可以为以后的土地整治效益评价工作提供一个科学的借鉴。     

Fully coupled time-domain simulation of dynamic positioning semi-submersible platform using dynamic surface control

A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning （DP） system. In the control force design, a dynamic model of reference linear drift frequency in the horizontal plane is introduced. The dynamic surface control （DSC） is used to design a control strategy for the DP. Compared with the traditional back-stepping methods, the dynamic surface control combined with radial basis function （RBF） neural networks （NNs） can avoid differentiating intermediate variables repeatedly in every design step due to the introduction of a first order filter. Low frequency motions obtained from total motions by a low pass filter are chosen to be the inputs for the RBF NNs which are used to approximate the low frequency wave force. Considering the propellers’ wear and tear, the effect of filtering frequencies for the control force is discussed. Based on power consumptions and positioning requirements, the NN cen-ters are determined. Moreover, the RBF NNs used to approximate the total wave force are built to monitor the disturbances. With the DP assistance, the results of fully coupled dynamic response simulations are given to illustrate the effectiveness of the proposed con-trol strategy.     

Verifying Fossil-Fuel Carbon Dioxide Emissions Forecasted by an Artificial Neural Network with the GEOS-Chem Model

In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.     

广西寒露风开始期短期气候预测方法研究

利用1951-2013年广西90个气象观测站气温资料、国家气候中心74项指数和美国National Oceanic and Atmospheric Administration（NOAA）的Climate Prediction Center（CPC）60项指数以及海温和陆地雪盖资料、美国国家冰雪研究中心（NSIDC）的两极海冰资料,使用相关分析方法得到广西寒露风开始期气候影响因子,利用逐步回归和神经网络方法进行寒露风开始期的预测。结果表明：寒露风开始期与前一年9-10月北极海冰面积、当年3月南极海冰面积、前一年6月欧亚雪盖、当年5月北美雪盖、北半球雪盖的相关显著。与前一年9月北半球极涡面积指数、前一年10月亚洲区极涡面积指数、前一年3月热带印度洋海温偶极子等指数相关显著。粒子群-神经网络方法预测误差低于逐步回归方法,预报能力有明显提高。     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。     

模糊支持向量机和变化矢量分析相结合的矿区土地覆盖变化检测

针对目前土地覆盖变化检测常用的方法存在不同程度的误差累积,夸大了变化区域,提出模糊支持向量机(FSVM)和变化矢量分析(CVA)相结合的土地覆盖检测方法。以某矿区2004年和2008年两期的CBERS遥感影像进行了试验。结果表明,植被大幅减少,其他地类都有不同程度的增加,主要是由于开采规模和产量提升所致。通过与常规的其他两类方法比较发现,本文方法的总体精度、Kappa系数、漏检误差和虚检误差分别为92.67%、0.892 7%、5.79%、7.31%,比其他两种方法有较大提高,能够提供较全面的变化类别和准确信息,可以有效地应用于矿区土地覆盖动态监测。     

高阶BOC调制信号无模糊跟踪技术性能比较

BOC调制方式提升了导航信号的性能和频谱资源利用率,但是自相关函数的多峰特性使信号接收面临新的问题。本文研究高阶BOC调制方式及其无模糊跟踪技术,主要分析了Bump-Jump算法、BPSK-like算法、SCPC算法和双环路算法,比较不同跟踪技术的硬件复杂度、计算复杂度、跟踪精度和跟踪稳定性,结果可为接收机设计提供参考。