基于支持向量回归的海面高度统计预报方法研究

本文提出一种基于支持向量回归的统计预报方法,通过经验正交分解对原始数据矩阵进行时空分解,提取出空间模态和时间系数。由于海面高度变化具有非线性、大惯性的特点,对时间系数进行小波分析,能有效滤除其中的高频信号,得到表征海面高度变化的低频信号。利用支持向量回归方法对小波分解后的低频信号构建预报模型。最后,进行小波重构,还原时间序列长度,实现未来7天的海面高度预报。通过黑潮附近海域的海面高度预报结果验证,该预报方法的预报效果优于整合滑动平均自回归预报方法。本文通过机器学习的算法实现了海面高度的预报,为海洋预报方法提供了新的思路。     

基于SVM回归模型的SAR图像有效波高估测

A new method for estimating significant wave height(SWH) from advanced synthetic aperture radar(ASAR) wave mode data based on a support vector machine(SVM) regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts(ECMWF) is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization(PSO) algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.     

赤潮随机梯度回归分析

赤潮的危害日益严重,为了预测赤潮的发生,运用回归树的随机梯度Boosting算法分析渤海赤潮数据, 建立浮游植物总量与环境因子的定量关系,给出各种环境因子对浮游植物总量相对影响的大小以及浮游植物总量和各种环境因子偏相关的图形,有利于探索赤潮的发生机制,指导菌种的培养. 最后,相比其它算法,回归树的随机梯度Boosting对于＂局部剧增＂的赤潮数据是稳健的,而且具有较高的预测精度.     

基于最小二乘支持向量机的声速空间变化模型构建

建立声速空间变化模型是解决声速剖面代表性误差的有效方法。在对不同声速剖面进行标准化处理的基础上,通过声速训练样本及核函数的选取,提出并实现了一种基于最小二乘支持向量机算法的声速空间变化模型构建方法。为了检验该方法的有效性,选取实测的声速剖面数据进行验证,结果表明该方法能有效地构建声速空间变化模型,从而消除或最大限度地削弱声速剖面代表性误差。     

用显微注射方法分析牙鲆肌肉发育调节基因MyoD, Myf5的启动子序列

克隆了牙鲆(Paralichthys olivaceus)肌肉发育调节基因MyoD和Myf5的启动子序列,分别为608 bp和1 073 bp。对序列进行分析发现MyoD和Myf5的启动子含有参与肌肉发育调控的基因的结合位点。将MyoD和Myf5的启动子连接到GFP载体上,构建了重组质粒MyoDP-GFP和Myf5P-GFP,并注射到一细胞或两细胞的斑马鱼胚胎中,对这两个基因的启动子进行了瞬时表达研究。结果表明,牙鲆MyoD和Myf5的启动子可以驱动绿色荧光蛋白特异地在斑马鱼肌肉纤维中表达,因此牙鲆608 bpMyoD和1073 bpMyf5的启动子包含着可以驱动MyoD和Myf5正常表达的必需元件,它们是肌肉特异的,并且可以跨物种行使其功能。     

Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010–2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.     

Support vector regression methodology for storm surge predictions

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting storm surges is applied. The original data of Longdong station at Taiwan ‘invaded directly by the Aere typhoon’ are considered to verify the present model. Comparisons with the numerical methods and neural network indicate that storm surges and surge deviations can be efficiently predicted using SVR.     

LIC color texture enhancement algorithm for ocean vector field data based on HSV color mapping and cumulative distribution function

Texture-based visualization method is a common method in the visualization of vector field data. Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image, a new color texture enhancement algorithm based on the Line Integral Convolution (LIC) for the vector field data is proposed, which combines the HSV color mapping and cumulative distribution function calculation of vector field data. This algorithm can be summarized as follows: firstly, the vector field data is convoluted twice by line integration to get the gray texture image. Secondly, the method of mapping vector data to each component of the HSV color space is established. And then, the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image. Thirdly, the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values. Finally, both the gray texture image and the color image are fused to get the color texture. The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data. Furthermore, the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.     

Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification

System identification provides an effective way to predict the ship manoeuvrability. In this paper several measures are proposed to diminish the parameter drift in the parametric identification of ship manoeuvring models. The drift of linear hydrodynamic coefficients can be accounted for from the point of view of dynamic cancellation, while the drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the parameter drift, reconstruction of the samples and modification of the mathematical model of ship manoeuvring motion are carried out. Difference method and the method of additional excitation are proposed to reconstruct the samples. Using correlation analysis, the structure of a manoeuvring model is simplified. Combined with the measures proposed, support vector machines based identification is employed to determine the hydrodynamic coefficients in a modified Abkowitz model. Experimental data from the free-running model tests of a KVLCC2 ship are analyzed and the hydrodynamic coefficients are identified. Based on the regressive model, simulation of manoeuvres is conducted. Comparison between the simulation results and the experimental results demonstrates the validity of the proposed measures.     

基于网格搜索的支持向量机核函数参数的确定

为提高支持向量机的分类准确率,研究了支持向量机核函数的参数确定问题,得到了1种确定支持向量机核函数的参数的有效途径.利用网格搜索法可使各组核函数参数相互解耦,从而便于并行计算,提高了运行效率.将此方法用于测井岩性分类器的训练得到了较理想的仿真结果.     

基于模糊C均值聚类-支持向量机的海底沉积物分类识别

在总结了目前海底底质分类研究的基础之上,率先提出利用计算机数值模拟技术对海底底质进行分类识别研究。相较于目前海底底质分类研究中所使用的水槽实验法,提出采用计算机数值正演技术模拟实际地震勘探中数据采集过程。在分类识别算法上,分别采用支持向量机(SVM)和模糊C均值聚类(FCM)算法对采集的数据进行分类,为使支持向量机分类识别率达到最大,引入差分进化算法对支持向量机中关键参数进行最优化搜索,并研究了向原始地震记录中加入10%,30%,50%的高斯白噪音时算法的稳定性。在分析了这两种算法分类识别的正确率及其各自的优缺点后,提出了海底底质分类识别的两步法,即(1)先利用模糊C均值聚类进行一粗糙的预测分类,在每一类中挑选聚类性较好的数据作为支持向量机的训练样本;(2)将上一步中筛选的样本作为支持向量机的训练样本,并用差分进化算法优化支持向量机分类参数,再利用训练好的支持向量机对其余数据做预测分类。鉴于计算机数值模拟的可重复性、高效快速性及本文提出的模糊C均值聚类-支持向量机方法的鲁棒性,为便于开展进一步研究,归纳总结了一套行之有效的采用计算机数值模拟技术开展海底底质分类识别研究的一般化流程。     

基于GM(1,1)模型的贫信息渔业数据CPUE标准化研究

单位捕捞努力量渔获量(catch per unit of effort,CPUE)标准化是渔业资源评估和管理中基础性工作.为了对信息量少、没有时空和环境变量的渔业监测数据进行CPUE标准化,本研究采用灰色系统方法,结合2010—2014年江苏省4种张网类调查生产数据,构建灰色GM(1,1) CPUE标准化模型,采用残差、关联度和后验差等3种检验方法评价CPUE标准化优良度,为渔业监测调查数据CPUE标准化提供一种新的途径.结果表明,所有模型的残差都可以接受;在关联度检验下,所有灰色GM(1,1)模型的关联度都大于0. 6,建模结果为满意;后验差结果显示所有灰色GM(1,1)的预测精度为合格.上述结果表明采用灰色GM(1,1)模型对信息量少、没有空间和环境信息的渔获数据进行CPUE标准化是可行的,可以为渔业管理部门提供决策支持.     

基于SVM粗差剔除的有色噪声建模

针对有色噪声建模存在的粗差影响系统辨识的问题,提出了一种基于支持向量机(SVM)的粗差剔除方法,在粗差剔除的基础上对有色噪声进行建模,并给出了相应的理论模型。为了验证该方法的正确性,进行了仿真算例的研究,结果表明该方法能有效地剔除有色噪声中的粗差,并在此基础上对噪声模型进行有效的系统辨识。     

Parameter identification of nonlinear roll motion equation for floating structures in irregular waves

In order to predict the roll motion of a floating structure in irregular waves accurately, it is crucial to estimate the unknown damping coefficients and restoring moment coefficients in the nonlinear roll motion equation. In this paper, a parameter identification method based on a combination of random decrement technique and support vector regression (SVR) is proposed to identify the coefficients in the roll motion equation of a floating structure by using the measured roll response in irregular waves. Case studies based on the simulation data and model test data respectively are designed to validate the applicability and validity of the identification method. Firstly, the roll motion of a vessel is simulated by using the known coefficients from literature, and the simulated data are used to identify the coefficients in the roll motion equation. The identified coefficients are compared with the known values to validate the applicability of the identification method. Then the roll motion is predicted by using the identified coefficients. The prediction results are compared with the simulated data, and good agreement is achieved. Secondly, the model test data of a FPSO are used to identify the coefficients in the roll motion equation. Then the random decrement signature of the roll motion is predicted by using the identified coefficients and compared with that obtained from the model test data, and satisfactory agreement is achieved. From this study, it is shown that the identification method can be effectively applied to identify the coefficients in the nonlinear roll motion equation in irregular waves.     

基于SVR和LSTM算法反演散射计下有效波高研究

有效波高反演对于海洋工程及海洋环境安全具有重要意义.我国海洋二号(HY-2A)卫星载有散射计和高度计等获取海洋要素的仪器.散射计可获取海洋风场数据但无法直接获取有效波高数据,高度计可获取海洋有效波高数据但覆盖区域狭小.本文将散射计与高度计各自优势结合,利用支持向量回归(SVR)和长短期记忆(LSTM)智能算法反演散射计...     

一种融合纹理特征与NDVI的随机森林海冰精细分类方法

海冰的精准分类对于掌握海冰生长发育状况,保障航海安全等具有重要意义。由于受数据源和分类方法等影响,使得海冰分类精度提高受限。本文面向高空间分辨率的光学遥感影像,提出了一种融合纹理特征和归一化差分植被指数（NDVI）的海冰精准分类方法,运用随机森林分类器构建海冰分类方法。以青岛胶州湾为实验区,高分二号（GF-2）为实验数据,进行了海冰类型提取,并与其他分类方法进行对比。结果显示:针对GF-2高分辨率光学遥感数据,融合纹理特征和NDVI的随机森林方法,相比于传统的随机森林、支持向量机、自动决策树和融合纹理特征的最大似然分类方法,总体分类精度分别提高13.70%、11.60%、19.22%、29.37%。Kappa系数分别提高0.16、0.13、0.22、0.44。相比于融合纹理特征和归一化水指数（NDWI）的随机森林方法,总体分类精度提高了9.67%,Kappa系数提高了0.09。这表明本文构建的海冰分类方法可有效提高海冰分类精度,为海冰的精确分类提供了一种有效的技术手段。     

一种建立局域无缝海图深度基准面的方法

根据海洋测绘中垂直基准构建的需求,提出了一种基于主成分-逐步回归分析的建立局域无缝海图深度基准面的方法。在此基础上,利用中国某海区多个验潮站深度基准面值及分潮调和常数振幅,建立了该海区局域无缝深度基准面值回归方程。实例计算表明,该方法所建立的局域海区无缝海图深度基准面值能够达到厘米级精度,可为海图深度基准面体系的重新构建提供技术支持。