A simplified approach to design fuzzy logic controller for an underwater vehicle

Fuzzy logic controller (FLC) performance is greatly dependent on its inference rules. In most cases, the more rules being applied to an FLC, the accuracy of the control action is enhanced. Nevertheless, a large set of rules requires more computation time. As a result, an FLC implementation requires fast and high performance processors. This paper describes a simplified control scheme to design a fuzzy logic controller (FLC) for an underwater vehicle namely, deep submergence rescue vehicle (DSRV). The proposed method, known as the single input fuzzy logic controller (SIFLC), reduces the conventional two-input FLC (CFLC) to a single input FLC. The SIFLC offers significant reduction in rule inferences and simplifies the tuning process of control parameters. The performance of the proposed controller is validated via simulation by using the marine systems simulator (MSS) on the Matlab/Simulink^® platform. During simulation, the DSRV is subjected to ocean wave disturbances. The results indicate that the SIFLC, Mamdani and Sugeno type CFLC give identical response to the same input sets. However, an SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.     

Prediction of wave parameters by using fuzzy logic approach

The purpose of this study is to investigate the relationship between wind speed, previous and current wave characteristics. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy IF–THEN rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. In this paper, a sophisticated intelligent model, based on Takagi–Sugeno (TS) fuzzy modeling principles, was developed to predict the changes in wave characteristics such as significant wave height and zero up-crossing period due to the wind speed. Past measurements of significant wave height values and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals such as 1, 3, 6 and 12 h. The verification of the proposed model is achieved through the wave characteristics time series plots and various numerical error criterias. Also the model results were compared with classical Auto Regressive Moving Average with exogenous input (ARMAX) models. For the application of the proposed approach the offshore station located in the Pacific Ocean was used.     

A decision-making system to maritime risk assessment

In this study, we propose a fuzzy approach in order to evaluate the maritime risk assessment applied to safety at sea and more particularly, the pollution prevention on the open sea. The work is based on the decision-making system, named MARISA, presented in Balmat et al. (2009). This system allowed defining a risk factor for each ship according to ship’s characteristics and weather conditions. In this novel paper, the proposed system takes into account the ship speed evolution and the ship position with respect to maritime shipping lanes is developed. To validate the method, we present an example of results with real data.     

模糊逻辑仿真建模及其在青岛海雾分析中的应用

引入模糊推理仿真建模方法，在传统定性分析的基础上，基于观测事实和预报经验，建立起了青岛海雾定量化的模糊推理映射模型，在此基础上对青岛海雾发生发展的影响因子和变化特征进行了诊断和预报探讨。仿真试验结果表明，模糊推理方法在定量性、客观性和自动化程度方面优于传统的天气分析预报方法，在分析、预报诸如海雾等地域性特征明显的天气中有较好的实用意义和应用前景。     

An assessment of fuzzy logic vessel path control

A fuzzy logic controller for ship path control in restricted waters is developed and evaluated. The controller uses inputs of heading, yaw rate, and lateral offset from the nominal track to produce a commanded rudder angle. Input variable fuzzification, fuzzy associative memory rules, and output set defuzzification are described. Two maneuvering situations are evaluated: track keeping along a specified path where linearized regulator control is valid; and larger maneuvers onto a specified path where nonlinear modeling and control are required. For the track keeping assessment, the controller is benchmarked against a conventional linear quadratic Gaussian (LQG) optimal controller and Kalman filter control system. The Kalman filter is used to produce the input state variable estimates for the fuzzy controller as well. An initial startup transient and regulator control performance with an external hydrodynamic disturbance are evaluated using linear model simulations of a crude oil tanker. A fully nonlinear maneuvering model for a smaller product tanker is used to assess the larger maneuvers     

MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor

This paper presents a fuzzy approach for the MAritime RISk Assessment (MARISA) applied to safety at sea. The aim of this work is to define automatically an individual ship risk factor which could be used in a decision making system. To achieve this purpose, a modular and hierarchical structure using fuzzy logic has been developed. It allows us to obtain a fuzzy risk factor (FRF) composed of a static risk factor (SRF) and a dynamic risk factor (DRF). The static risk factor assessment takes into account several static data relative to the ship (age, flag, gross tonnage, number of companies, duration of detention and type). The dynamic risk factor is evaluated by considering the meteorological conditions (sea state, wind speed and visibility) and the moment of the day. Moreover, the MARISA graphic interface developed with the Labview software is presented. This interface allows several simulations to be carried out to validate the fuzzy method proposed. Simulation results are presented.     

Real-time wave forecasting using genetic programming

The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24 h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.     

Study on the uncertainty of the available time under ship fire based on Monte Carlo sampling method

Available safety egress time under ship fire(SFAT) is critical to ship fire safety assessment,design and emergency rescue.Although it is available to determine SFAT by using fire models such as the two-zone fire model CFAST and the field model FDS,none of these models can address the uncertainties involved in the input parameters.To solve this problem,current study presents a framework of uncertainty analysis for SFAT.Firstly,a deterministic model estimating SFAT is built.The uncertainties of the input parameters are regarded as random variables with the given probability distribution functions.Subsequently,the deterministic SFAT model is employed to couple with a Monte Carlo sampling method to investigate the uncertainties of the SFAT.The Spearman’s rank-order correlation coefficient(SRCC) is used to examine the sensitivity of each input uncertainty parameter on SFAT.To illustrate the proposed approach in detail,a case study is performed.Based on the proposed approach,probability density function and cumulative density function of SFAT are obtained.Furthermore,sensitivity analysis with regard to SFAT is also conducted.The results give a high-negative correlation of SFAT and the fire growth coefficient whereas the effect of other parameters is so weak that they can be neglected.     

水团分析中的模糊数学方法

使用多年平均温盐资料,应用模糊数学的一系列方法,对渤、黄、东海整个海域逐月进行了水团划分与分析。用移动格域法和模糊积分法确定水团的聚类中心,依Fuzzy-F统计判定海区内水团的个数;用模糊ISODATA聚类法调整软划分矩阵,依模糊贴近度衡量水团之间的相近程度,用模糊熵讨论水团的不均匀程度,从而给出了在水团分析中系统地应用模糊数学方法的实例和计算流程。     

基于模糊影响图的海洋结构风险分析方法研究

基于模糊影响图(FPID)和失效模式与效果分析(FMEA)建立了一种海洋结构风险分析方法。鉴于风险分析中某些事件发生概率和关系概率两个重要参数确定时需借助专家主观判断,本文将模糊概率的概念引入海洋结构影响图评估方法中。该方法成功地应用到海洋平台人员伤害分析中。虽然这里仅给出了该方法在海洋平台风险评估中的应用,但其具有更广泛应用价值。     

基于RMF技术的远洋船舶定量风险评估方法研究

在远洋船舶运输中，设备失效是导致各类事故的一个重要因素。文中引人RMF技术，将其应用到远洋船队的定量风险评估中，以及时查找并消除设备方面的事故隐患。首先找出潜在的风险事件及相关风险因素，然后运用模糊综合评判方法和事故损失综合评估理论对设备风险事件的模糊概率和损失程度后果进行量化，最后依据风险评价结果对需要控制的风险源给出控制措施。实例分析亦表明，运用RMF技术能对远洋船舶的各类设备及属具实施科学的风险管理。     

Fuzzy Risk Analysis of Harbour Engineering Investment by Hierarchy System Approach

The present risk analysis model of engineering investment is built by fuzzy hierarchy approach under the assumption of maximizing the revenues of the project during its whole life cycle of operation. It can reasonably be expressed by a system evaluation analysis. As a matter of fact, the system, aimed by its system goal can be modelled by a set of factors, constitutively structured by certain links between them, to form a factorial network chart, which represents the essentials of the system behaviours, the nodes of which represent the factors concerned. The weight distribution between factors located at the same level can be determined by the eigen-value problem of a "pair comparison" relation matrix. The weight distribution of factors at each level is successively manipulated until the fuzzy synthetic risk assessment. As an example of risk analysis of engineering investment, a harbour construction project is presented for illustration.     

Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study’s authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10?m) by evaluation with the used reference vector data.     

Wash Waves Generated by High Speed Displacement Ships

It is difficult to compute far-field waves in a relative large area by using one wave generation model when a large calculation domain is needed because of large dimensions of the waterway and long distance of the required computing points. Variation of waterway bathymetry and nonlinearity in the far field cannot be included in a ship fixed process either. A coupled method combining a wave generation model and wave propagation model is then used in this paper to simulate the wash waves generated by the passing ship. A NURBS-based higher order panel method is adopted as the stationary wave generation model; a wave spectrum method and Boussinesq-type equation wave model are used as the wave propagation model for the constant water depth condition and variable water depth condition, respectively. The waves calculated by the NURBS-based higher order panel method in the near field are used as the input for the wave spectrum method and the Boussinesq-type equation wave model to obtain the far-field waves. With this approach it is possible to simulate the ship wash waves including the effects of water depth and waterway bathymetry. Parts of the calculated results are validated experimentally, and the agreement is demonstrated. The effects of ship wash waves on the moored ship are discussed by using a diffraction theory method. The results indicate that the prediction of the ship induced waves by coupling models is feasible.     

船舶随浪运动稳性仿真计算

本文利用Ｌｉａｐｕｎｏｖ理论，研究了船舶在规则波浪运动的稳性；利用摄动理论，求解出船舶运动响应；并讨论了船舶横摇与垂荡运动频率、最大横摇角和波浪要素对稳性曲线ＧＺ的影响，以及流体动压力对稳性曲线的修正，从而给计算船舶在随浪中的稳性提供了一种方法。     

自适应模糊神经推理系统在混凝土强度评定中的应用

混凝土强度评定的准确性对结构物的安全性评价具有很大的影响。为了充分利用钻芯法和回弹法这2种常用混凝土测强方法的特点,建立自适应模糊神经推理系统模型来综合评定结构的混凝土强度。将回弹值的常用对数和碳化深度值作为模型的输入,钻芯值的常用对数作为模型的输出。模型参数采用混合算法确定。其中,条件参数采用梯度下降法来调整;结论参数采用最小二乘法来调整。该模型可以有效地映射出训练数据之间复杂的非线性关系。通过对已有的钻芯、回弹试验数据的对比计算,自适应模糊神经推理系统方法的强度预测精度高于常规的回归方法。     

Modeling of Multi-Freedom Ship Motions in Irregular Waves with Fuzzy Neural Networks

1 .IntroductionAshiptravelingatseaundergoesundesirablewave inducedmotions ,namely ,surge ,sway ,heav ing ,rolling ,pitchingandyaw .Thesemotionsoftencauseproblemstothecrew ,theonboardequip mentand ,intheworstcase ,thesafetyofthevessel.Tominimizethewave inducedshipmotions ,controlsystemsmaybeapplied .Theaccuratemodelingofshipmotionsisthereforeveryimportantforshipdesignanddesignofmotioncontrolsystems .Manyresearchershavedevelopedshipmotionpredictionmethodsbasedonthepotentialflowtheo ries (Dong ,…     

跨海桥梁船撞风险综合评估

近年来全世界范围内船撞桥事故时常发生,尤其是大型跨海桥梁,对其进行合理的船撞桥风险评估逐渐成为桥梁安全运营的重要保障之一。通过对近年国内外船撞桥案例调研分析,确定了影响事故的多重因素,建立包括4个二级风险评价指标和18个三级指标的层次化评价指标体系,并运用层次分析（AHP）法和熵权法,对各评价指标进行主客观综合赋权,明确各个风险因素对船舶撞击的重要性程度,基于模糊数学理论对船撞桥风险进行多层次综合评判。以浙江省舟山朱家尖跨海大桥为工程背景,结合该桥某年船舶通航统计数据,运用上述方法进行船撞桥风险评估,计算表明朱家尖跨海大桥平均风险评价值为4.22,属于可接受中风险水平,并提出了相应的风险控制对策及措施。     

Stochastic modeling of seafloor morphology; Resolution oftopographic parameters by sea beam data

The authors explore the resolving power of an inversion algorithm which estimates five parameters of the seafloor covariance function from a single swath of multibeam echosounding data. The resolving power is evaluated as a function of the swath length, the orientation of ship track with respect to topographic grain, and the response width of the sounding system. The analysis is conducted by inverting sets of synthetic data with known statistics. The mean and standard deviation of the inverted parameters can be directly compared with the input parameters and the standard errors output from the inversion. Experiments show that resolution of the covariance parameters is strongly dependent on the number of characteristic lengths which are sampled. Root-mean-square seafloor height can be estimated to within ~15%, and anisotropic orientation to within ~5% (for a strong lineation), using track lengths as short as three characteristic lengths     

Attitude determination using a multi‐antenna GPS system for hydrographic applications

A four‐antenna GPS attitude determination system was used to estimate roll, pitch, and heading parameters of a 52‐meter surveying vessel in an operational marine environment. The least squares algorithm for platform attitude estimation using multiple baseline vector observables is presented. An efficient on‐the‐fly carrier phase ambiguity searching method is derived, which utilizes the Cholesky decomposition method and the known baseline constraints between the GPS antennas to construct the potential ambiguity sets on the sphere. The accuracy of the estimated attitude parameters from the GPS multi‐antenna system was assessed with an independent inertial navigation system (INS). Results from sea trials show that the proposed GPS multi‐antenna system and processing algorithms delivered a satisfactory performance under various ship maneuvers. The accuracy of GPS estimated ship attitude parameters is better than 0.06 degrees at an output rate of 10 Hz. Such a performance demonstrates a new alternative means to provide accurate, reliable, and cost‐effective ship attitude information for hydrographic applications.