The influence of seasonality on estimating return values of significant wave height

A time-dependent generalized extreme value (GEV) model for monthly significant wave heights maxima is developed. The model is applied to several 3-hour time series from the Spanish buoy network. Monthly maxima show a clear non-stationary behavior within a year, suggesting that the location, scale and shape parameters of the GEV distribution can be parameterized using harmonic functions. To avoid a possible over-parameterization, an automatic selection model, based on the Akaike Information Criterion, is carried out. Results show that the non-stationary behavior of monthly maxima significant wave height is adequately modeled, drastically increasing the significance of the parameters involved and reducing the uncertainty in the return level estimation. The model provides new information to analyze the seasonal behavior of wave height extremes affecting different natural coastal processes.     

双通道实时数据采集处理系统的设计与实现

本文提出了一种基于 TMS32 0 C31串行接口的双通道实时数据采集处理系统的设计与实现方案 ,该设计以 TMS32 0 C31和 TL C32 0 AD5 0 C为核心器件 ,具有两个独立的 A/D,D/A通道 ,能够实现 32位浮点计算和 16位数据采集与回放。应用该系统进行归一化最小均方误差 (Normal-ized L east Mean Square,NLMS)算法实时自适应噪声抵消实验 ,实验结果表明 ,该系统能够实现实时的自适应噪声抵消 ,可广泛应用于实时语音信号处理等领域。     

极值波高建模中不确定性的模糊量化方法

A non-traditional fuzzy quantification method is presented in the modeling of an extreme significant wave height. First, a set of parametric models are selected to fit time series data for the significant wave height and the extrapolation for extremes are obtained based on high quantile estimations. The quality of these results is compared and discussed. Then, the proposed fuzzy model, which combines Poisson process and generalized Pareto distribution(GPD) model, is applied to characterizing the wave extremes in the time series data. The estimations for a long-term return value are considered as time-varying as a threshold is regarded as non-stationary. The estimated intervals coupled with the fuzzy theory are then introduced to construct the probability bounds for the return values. This nontraditional model is analyzed in comparison with the traditional model in the degree of conservatism for the long-term estimate. The impact on the fuzzy bounds of extreme estimations from the non stationary effect in the proposed model is also investigated.     

Time Domain Simulation of Transient Responses of Very Large Floating Structures Under Unsteady External Loads

A time domain finite element method （FEM） for the analysis of transient elastic response of a very large floating structure （VLFS） subjected to arbitrary time-dependent external loads is presented. This method is developed directly in time domain and the hydrodynamic problem is formulated based on linear, inviscid and slightly compressible fluid theory and the structural response is analyzed on the thin plate assumption. The time domain finite element procedure herein is validated by comparing numerical results with available experimental data. Finally, the transient elastic response of a pontoon-type VLFS under the landing of an airplane is computed by the proposed time domain FEM. The time histories of the applied force and the position and velocity of an airplane during landing are modeled with data from a Boeing 747-400 jumbo jet.     

砂质海岸突堤式建筑物下游岸线变形数学模型

提出了一种改进的砂质海岸岸滩演变的“一线理论”数学模型.该模型中考虑了岸滩演变过程中岸滩坡度变化的影响,并采用波浪折射、绕射和反射联合计算数学模型模拟掩护区的波浪场,提高了波浪计算的精度.实例计算表明,在验证资料比较充分的条件下,该数学模型计算结果的可靠度较高,可供实际工程规划和设计时参考使用.     

Transient sonar signal classification using hidden Markov modelsand neural nets

In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension (1-D). The hidden Markov model (HMM) is well suited to classification of 1-D signals such as speech. In HMM methodology, the signal is divided into a sequence of frames, and each frame is represented by a feature vector. This sequence of feature vectors is then modeled by one HMM. Thus, the HMM methodology is highly suitable for classifying the patterns that are made of concatenated sequences of micro patterns. The sonar transient signals often display an evolutionary pattern over the time scale. Following this intuition, the application of HMM's to sonar transient classification is proposed and discussed in this paper. Toward this goal, three different feature vectors based on an autoregressive (AR) model, Fourier power spectra, and wavelet transforms are considered in our work. In our implementation, one HMM is developed for each class of signals. During testing, the signal to be recognized is matched against all models. The best matched model identifies the signal class. The neural net (NN) classifier has been successfully used previously for sonar transient classification. The same set of features as mentioned above is then used with a multilayer perceptron NN classifier. Some experimental results using “DARPA standard data set I” with HMM and MLP-NN classification schemes are presented. A combined NN/HMM classifier is proposed, and its performance is evaluated with respect to individual classifiers     

Automatic indexing of underwater survey video: algorithm and benchmarking method

It is often the case that only a few sparse sequences of long videos from scientific underwater surveys actually contain important information for the expert. Locating such sequences is time consuming and tedious. A system that automatically detects those critical parts, online or during post-mission tape analysis, would alleviate the expert workload and improve data exploitation. In this paper, a methodology for evaluating the performance of such a system on real data is presented. Interesting sequences are started by changes of visual context. An algorithm to detect significant context changes in benthic videos in real time has been presented by Lebart et al. in 2000. It is used as an illustration for this methodology - its performance is studied and benchmarked on real underwater data, ground truthed by an expert biologist. Various issues relating to the complexity of the problems of automatically analyzing underwater video are also discussed.     

Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals

Power spectral density estimation is often employed as a method for signal detection. For signals which occur randomly, a frequency domain kurtosis estimate supplements the power spectral density estimate and, in some cases, can be employed to detect their presence. This has been verified from experiments with real data of randomly occurring signals. In order to better understand the detection of randomly occurring signals, sinusoidal and narrow-band Gaussian signals are considered, which when modeled to represent a fading or multipath environment, are received as non-Ganssian in terms of a frequency domain kurtosis estimate. Several fading and muitipath propagation probability density distributions of practical interest are considered, including Rayleigh and log-normal. The model is generalized to handle transient and frequency modulated signals by taking into account the probability of the signal being in a specific frequency range over the total data interval. It is shown that this model produces kurtosis values consistent with real data measurements. The ability of the power spectral density estimate and the frequency domain kurtosis estimate to detect randomly occurring signals, generated from the model, is compared using the deflection criterion. It is shown, for the cases considered, that over a large range of conditions, the power spectral density estimate is a better statistic based on the deflection criterion. However, there is a small range of conditions over which it appears that the frequency domain kurtosis estimate has an advantage. The real data that initiated this analytical investigation are also presented.     

基于网络坐标的地理信息系统

确定网络节点位置是互联网中一种重要的应用服务。目前主流的确定网络节点位置的系统都不易获取且不具有实时性,给出的节点位置不够准确。提出1种基于网络坐标的节点地理位置预测系统,利用少数地标节点的位置信息,并结合相应的网络坐标,对网络当中任意节点的地理位置进行实时地预测。通过在Internet网络当中开展实验,进一步验证了该系统的可行性和准确性。结果表明,在实验开展的范围内,系统能够准确的预测节点的地理位置。     

经验模式分解对恒电量瞬态响应信号的预处理

应用经验模式分解（EMD）将恒电量瞬态响应信号分解为不同时间尺度的内在模函数（IMF）分量。去除其中的小时间尺度的干扰噪声分量。然后经过拉普拉斯变换获得恒电量频谱以研究电化学腐蚀过程。     

A time-dependent numerical model of the mild-slope equation

On the basis of the previous studies, the simplest hyperbolic mild-slope equation has been gained and the linear time-dependent numerical model for the water wave propagation has been established combined with different boundary conditions. Through computing the effective surface displacement and transforming into the real transient wave motion, related wave factors will be calculated. Compared with Lin’s model, analysis shows that calculation stability of the present model is enhanced efficiently, because the truncation errors of this model are only contributed by the dissipation terms, but those of Lin’s model are induced by the convection terms, dissipation terms and source terms. The tests show that the present model succeeds the merit in Lin’s model and the computational program is simpler, the computational time is shorter, and the computational stability is enhanced efficiently. The present model has the capability of simulating transient wave motion by correctly predicting at the speed of wave propagation, which is important for the real-time forecast of the arrival time of surface waves generated in the deep sea. The model is validated against analytical solution for wave diffraction and experimental data for combined wave refraction and diffraction over a submerged elliptic shoal on a slope. Good agreements are obtained. The model can be applied to the theory research an d engineering applications about the wave propagation in a biggish area.     

畸形波数值模拟和定点生成

采用David L等人提出的数值模拟产生畸形波的方法,以Longuet-Hinggins模型为基础,利用一个基本波列和一个瞬态波列线性叠加模拟波面,在很短的波列中得到畸形波,从而使模拟畸形波的效率提高。得到的畸形波畸形程度较高,并且可以控制畸形波出现的时间和地点。用该方法模拟实测畸形波并与其进行对比验证,结果表明模拟产生的畸形波和实测畸形波吻合良好。以此方法为基础,在实验室内物理产生畸形波将更加具有可操作性。     

不同时频分析方法的精度比较及应用

在传统的地震资料分析、处理和属性分析提取等研究中,基于平稳信号分析处理理论的傅里叶变换是核心技术之一。地震信号往往是非线性、非平稳信号,传统傅里叶变换是一种整体变换,难于反映非平稳信号的局部特征,而时频分析技术能同时展示信号在时间域和频率域的局部化特征。在介绍几种地震常用的时频分析方法基础上,进一步深入研究自适应时频分析方法。理论分析和试验表明,自适应最优核时频分布在抑制交叉项的同时,时频聚集性也较好,因此,更有利于地震勘探信号的分析和地震属性的提取、频谱分解等。     

High-Resolution Signal and Noise Field Estimation Using theL1 (Least Absolute Values) Norm

In this paper a new method for obtaining a quantitative estimate of an acoustic field consisting of a set of discrete sources and background noise is described. The method is based on theL1 (least absolute values) norm solution to an underdetermined system of linear equations defining the Fourier transform of the signal series. Implementations of the method with either equality or inequality constraints are presented and discussed. The much faster and more compact equality constraint version with a provision for modeling the noise field is recommended in practice. Experience with real data has shown the necessity of correcting for an observed Gaussian decay on the covariances. A simple means of estimating this effect and taking it into account in the signal estimation procedure is discussed, and the implications of this effect in high-resolution beamforming are considered. The effectiveness and versatility of theL1 method indicate that it has a useful role in high-resolution signal estimation.     

Autonomous bathymetry survey system

This paper describes the Autonomous Bathymetry Survey System (AutoSurvey), a system that provides automation of swath sonar bathymetric surveys. This system enables faster surveying of an area through environmentally adaptive techniques while ensuring adequate coverage and data quality. AutoSurvey assesses data quality and coverage in real time and generates next-trackline waypoints based on actual system performance. The need for real-time performance assessment is discussed. A primary factor considered is the effect of the environment on swath bathymetry system performance, which is difficult to predict a priori. The system's features, design, and implementation are discussed in this paper. Simulation and sea trial results are presented, as well as an analysis of the system's ability to reduce survey time     

Efficient computation method for two-dimensional nonlinear waves

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Simulation of small-amplitude frequency-dispersive transient waves by means of the mild-slope equation

A numerical model based on the mild-slope equation is applied to reproduce the propagation of small-amplitude transient waves. The model makes use of the Fourier Transform to convert the time-dependent hyperbolic equation into a set of elliptic equations in the frequency domain. The results of two available experimental studies on tsunamis generated by landslides are used to validate the model, which appears to be able of carefully reproducing the effects of the frequency dispersion. An example application of tsunamis propagating around the Stromboli island is also presented to show the applicability of the present approach to real life scenarios. It is finally discussed how this model could be applied as support to a tsunami early warning system.     

Marine sediment acoustic measurement system

This paper describes a system that has been developed to measure compressional wave speed in cored marine sediments onboard ship. The structure enables one to secure an extruded core sample to its base and to move acoustic probes to a desired location, implant them to a specified depth into the sample and perform the measurement. The acoustic measurement system is a pulse-time delay system measuring time difference over a fixed path length and the temperature of the sediment. The time difference and temperature measurement systems are comprised of task oriented components and are housed in a single portable box. The system is adaptable to the various sample sizes obtained with the coring apparatus presently in use. Initial field tests indicate that ship motion has no effect on the system. Data collected from cores has been classified according to sediment type and displays good agreement with data presented by Hamilton (1970). The difference in compressional wave velocity, based on sediment type, for the two studies is 5 m per sec.