Adaptive algorithm for cloud cover estimation from all-sky images over the sea

A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.     

雷达高度计海况偏差估计神经网络模型研究

本文基于Jason-2高度计数据,在12个不同季节的cycle数据中组合1~6个cycle的有效波高、风速和海况偏差为训练集,选取Jason-2的另外3个不同季节的cycle数据集为测试集。经检验分析,确定3个cycle对应的BP神经网络模型。将该模型应用于HY-2高度计海况偏差的估计,通过海况偏差与有效波高及风速的拟合优度、解释方差和残差对比分析,结果表明:神经网络BP模型可以有效应用于HY-2的海况偏差估计并明显优于传统海况偏差参数模型。     

Artificial neural network ability in evaluation of random wave-induced inline force on a vertical cylinder

An approach based on artificial neural network (ANN) is used to develop predictive relations between hydrodynamic inline force on a vertical cylinder and some effective parameters. The data used to calibrate and validate the ANN models are obtained from an experiment. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed by use of three design parameters (i.e. wave surface height, horizontal and vertical velocities) as network inputs and the ultimate inline force as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared to those obtained from Morison equation which is usually used to determine inline force as a computational method. With the existing data, it is found that least square method (LSM) gives less error in determining drag and inertia coefficients of Morison equation. With regard to the predicted results agreeing with calculations achieved from Morison equation that used LSM method, neural network has high efficiency considering its convenience, simplicity and promptitude. The outcome of this study can contribute to reducing the errors in predicting hydrodynamic inline force by use of ANN and to improve the reliability of that in comparison with the more practical state of Morison equation. Therefore, this method can be applied to relevant engineering projects with satisfactory results.     

Integrated assessment on sea water quality based on the fuzzy neural network

根据俚岛镇海湾22个海洋观测点的水质监测数据,依据人工神经网络与模糊理论,建立模糊神经网络模型,对研究区海水水质进行了综合评价.结果表明,区域内40.91％的采样点属于Ⅰ类海水水质标准,其余为Ⅱ类海水水质.其中,水质Ⅱ类区位于临络湾与俚岛湾的半封闭海域,因该区造船业对水体造成污染且海水对流强度较弱,不利于污染物的扩散;而海水水质Ⅰ类区域靠近黄海,工业活动较少,水体对流较强,利于污染物扩散.     

基于模糊神经网络的海水水质综合评价

根据俚岛镇海湾22个海洋观测点的水质监测数据,依据人工神经网络与模糊理论,建立模糊神经网络模型,对研究区海水水质进行了综合评价。结果表明,区域内40,91%的采样点属于Ⅰ类海水水质标准,其余为Ⅱ类海水水质。其中,水质Ⅱ类区位于临络湾与俚岛湾的半封闭海域,因该区造船业对水体造成污染且海水对流强度较弱,不利于污染物的扩散;而海水水质Ⅰ类区域靠近黄海,工业活动较少,水体对流较强,利于污染物扩散。     

基于人工神经网络的混凝土疲劳寿命估算

把决定混凝土疲劳寿命的应力水平，应力比，加载频率和描述随机性的破坏概率作为神经网络的输入，以疲劳寿命作为网络的输出，用网络结构描述它们之间的非线性关系，利用实验数据样本完成了网络的训练，并对模拟样本进行了估算，实例计算了结果表明，利用已有疲劳试验数据训练好的人工神经网络进行混凝土疲劳寿命估算，可部分代替冗长，昂贵的疲劳实验。     

GA-BP神经网络在罗源湾口波浪模拟研究中的应用

由于BP神经网络存在收敛速度慢和易于陷入极小值等缺点,引入遗传算法(GA)对网络的权值和阈值加以优化,并采用不同组合的输入因子和样本数,对福建省罗源湾口的波浪进行模拟研究.对输入因子的分析结果表明,研究区域的波浪主要受台湾海峡波浪传播影响,与局地气象因子(风速、风向、海气温差)的相关性较弱.训练样本试验表明,30 d以上的波浪历史数据可使GA-BP神经网络充分学习研究区域的波浪特征,从而实现对波浪要素的高精度模拟.模拟结果显示,对春、夏季实测波浪数据的模拟效果均很好,其中相关性分别为0.967和0.938,均方根误差分别为0.112 m和0.107 m,表明GA-BP神经网络在近岸波浪模拟预报中有较广阔的应用前景.     

Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods

The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area.     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

基于BP人工神经网络的海水水质综合评价

为了能够客观地对海水水质进行综合评价,在分析人工神经网络概念和原理的基础上,从阈值角度出发,通过对各类海水水质污染指标浓度生成样本的方法,生成了适用于BP人工神经网络模型训练的样本,并应用基于误差反向传播原理的前向多层神经网络,建立了用于海水水质评价的BP人工神经网络模型。将该模型用于渤海湾近岸海域水环境评价,通过模型的计算,得到该海域的水质类别。结果表明,2004-2007年,渤海湾近岸海域污染指标总体上在河流丰水期时比枯水期时高,2005年和2006年污染较为严重,2007年有所好转。经训练的评价模型应用于实例的评价结果表明,该模型设计合理、泛化能力强,对海水水质评价具有较好的客观性、通用性和实用性。     

基于BP人工神经网络的海水水质综合评价

为了能够客观地对海水水质进行综合评价,在分析人工神经网络概念和原理的基础上,从阈值角度出发,通过对各类海水水质污染指标浓度生成样本的方法,生成了适用于BP人工神经网络模型训练的样本,并应用基于误差反向传播原理的前向多层神经网络,建立了用于海水水质评价的BP人工神经网络模型。将该模型用于渤海湾近岸海域水环境评价,通过模型...     

Wave propagation over a deformable sea floor

The propagation of water waves over a deformable bottom is examined theoretically for the case where the soil beneath the water is regarded as an elastic solid. Equations are given for calculating wave speeds, soil displacements and stresses, and water motion and pressure. Detailed wave-speed solutions are worked out for the case of an incompressible soil. The effects of soil inertia are included in the analysis, and it is shown that such effects cannot generally be ignored without committing serious error.     

Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network

With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering.     

An on-line adaptation method in a neural network based controlsystem for AUVs

A neural network based control system “Self-Organizing Neural-Net-Controller System: SONCS” has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs). In this paper, an on-line adaptation method “Imaginary Training” is proposed to improve the time-consuming adaptation process of the original SONCS. The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object. The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out. In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data. A neural network “Identification Network” which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network. The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV “Twin-Burger”. It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation     

基于神经网络的长江口深水航道海域悬浮泥沙浓度研究

航道治理和维护的核心问题为泥沙问题。在充分分析遥感和遥测数据的基础上,利用BP神经网络方法对长江口深水航道海域悬浮泥沙浓度进行了研究。当隐含层神经元个数为8时,预测效果最好,网络的RMSE仅为0.102,网络的拟合度达到0.899。预测的最大相对误差为28%,最小相对误差为6%,总的相对误差为21.5%。     

基于小波变换和LSTM神经网络的格陵兰岛近海海域海平面变化预测

海平面变化包含多种不同时间尺度信息，传统的预测方法仅对海平面变化趋势项、周期项进行拟合，难以利用海平面变化的不同时间尺度信号，使得预测精度不高。本文基于深度学习的预测模型，提出一种融合小波变换（wavelet transform，WT）与LSTM （long short-term memory，LSTM）神经网络的海平面异常组合预测模型。首先利用小波分解得到反映海平面变化总体趋势的低频分量和刻画主要细节信息的高频分量；然后通过LSTM神经网络对代表不同时间尺度的各个分量预测和重构，实现海平面变化的非线性预测。基于该模型的海平面变化预测的均方根误差、平均绝对误差和相关系数分别为12.76 mm、9.94 mm和0.937，预测精度均优于LSTM和EEMD-LSTM预测模型，WT-LSTM组合模型对区域海平面变化预测具有较好的应用价值。     

Sea-clutter modeling using a radial-basis-function neural network

Recently, neural networks have been proposed for radar clutter modeling because of the inherent nonlinearity of clutter signals. This paper performs an analysis of the practicality of using a radial basis function (RBF) neural network to model sea clutter and to detect small target embedded in sea clutter. An experiment using an instrumental quality radar was carried out on the eastcoast of Canada to create a rich sea clutter and small surface target database. This database contains both staring and scanning data under various environmental conditions. Using data-sets with different characteristics, we investigate the effects of quantization error, measurement noise, generalization of the neural net over ranges and sampling rate on the RBF clutter model. Despite these physical limitations, the RBF model was shown to approach an optimal predictive performance. The RBF predictor was also applied to detect various small targets in this database based on the constant false alarm rate (CFAR) principle. This RBF-CFAR detector was demonstrated to be able to detect small floating targets even in rough sea conditions     

海上中距离跨海高程传递方法比较

远离岸线的中距离(30~50 km)海域缺少测绘基础资料,高程传递尚未形成相应的作业体系。为满足海上工程建设的精度要求,采用同步验潮平均海面传递法、GNSS/水准传递法以及DTU18全球平均海面模型传递法,在山东省半封闭海域与开阔海域进行了中距离海上高程传递实验。实验结果表明,15天同步验潮平均海面传递法与GNSS/水准法的精度相当(厘米级),与DTU18全球平均海面模型传递法进行直接传递的差异在15 cm内,可为不同测绘基础条件下海上风电高程基准传递提供作业参考。     

BP神经网络精度估计及其在海洋油气资源预测中的应用

在对BP算法进行深入分析的基础上,将测量数据处理与误差理论中的精度评定方法应用到BP神经网络的精度估计中,通过分别计算BP神经网络学习训练过程及预测过程的输出层中误差,实现对神经网络模型的精度评定。最后以海洋油气资源预测为例,结合实测资料建立了BP神经网络预测模型并分别进行了学习训练过程及预测过程的精度评定,以期为神经网络模型结构的优化设计提供有效参考,为提高神经网络模型的适用性提供科学依据。