A Spatial Decision Support System for Tourism Land Use Planning

One of the most complex challenges the tourism industry faces is keeping up to date with information technology developments caused by the globalisation of information and advances in technology. The development of robust decision support systems for tourism land use planning is a way to address this challenge.This paper demonstrates how a spatial decision support system (SDSS), called the Land Use Decision sUpport System (LUDUS), can contribute in allocating complex forms of tourism. The system combines an artificial intelligence technique, called ontologies, with Geographic Information Systems and object-oriented programming to support decision-making in spatial planning. The system consists of two subsystems: the Insert Data Subsystem and the Graphic Imaging and Decision Support Subsystem. The core of the system is an ontology that is aligned to a standard of the Open Geospatial Consortium, called Geosparql.The case study of this paper is the Mastichochoria area of Chios Island, Greece. Therefore, the structure of the ontology was modelled according to the provisions of Greek legislation. The results produced confirmed the correct coding and application of the system’s criteria. The validity, accuracy and reliability of the results were also confirmed.The adopted approach facilitates the identification of alternative options for allocating, among other land use types, complex forms of tourism development in suburban areas, by examining the provisions of the legal framework as well as their geology and terrain

Probabilistically-robust nonlinear control of offshore structures

A controller design for offshore structures is discussed in this study. Stochastic simulation is considered for evaluation of the system's performance in the design stage. This way, nonlinear characteristics of the structural response and excitation are explicitly incorporated into the model assumed for the system. Model parameters that have some level of uncertainty are probabilistically described. In this context, the controller is designed for optimal reliability, quantified as the probability, based on the available information, that the performance will not exceed some acceptable bounds. This treatment leads to a robust-to-uncertainty design. The methodology is illustrated in an example involving the control of a Tension Leg Platform in a random sea environment. Multifold nonlinearities are taken into account for the evaluation of the platform's dynamic response and a probabilistic description is adopted for characterizing the random sea environment

Monitoring the Response of connected moored floating modules

In the present paper a recently installed Sensor Network for Monitoring the Response (SNMR) of a Floating Structure (FS) is presented. SNMR is deployed on a pontoon-type FS operating as a floating breakwater, located 300m from the coast in the port of Neos Marmaras in Greece. The developed SNMR consists of: (i) sensors for real time measurement of FS's critical response quantities related with the structural integrity and safety of the FS as well as of environmental parameters and (ii) data acquisition and data transfer and storage system. Characteristic examples of time series of the measure quantities obtained during the operation of the SNMR are presented and preliminary assessed. Copyright © 2013 by the International Society of Offshore and Polar Engineers (ISOPE)

A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence

This article belongs to the Special Issue Deep Learning Applications with Practical Measured Results in Electronics IndustriesWind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions

Implementation of pattern recognition algorithms in processing incomplete wind speed data for energy assessment of offshore wind turbines

Offshore wind turbine (OWT) installations are continually expanding as they are considered an efficient mechanism for covering a part of the energy consumption requirements. The assessment of the energy potential of OWTs for specific offshore sites is the key factor that defines their successful implementation, commercialization and sustainability. The data used for this assessment mainly refer to wind speed measurements. However, the data may not present homogeneity due to incomplete or missing entries; this in turn, is attributed to failures of the measuring devices or other factors. This fact may lead to considerable limitations in the OWTs energy potential assessment. This paper presents two novel methodologies to handle the problem of incomplete and missing data. Computational intelligence algorithms are utilized for the filling of the incomplete and missing data in order to build complete wind speed series. Finally, the complete wind speed series are used for assessing the energy potential of an OWT in a specific offshore site. In many real-world metering systems, due to meter failures, incomplete and missing data are frequently observed, leading to the need for robust data handling. The novelty of the paper can be summarized in the following points: (i) a comparison of clustering algorithms for extracting typical wind speed curves is presented for the OWT related literature and (ii) two efficient novel methods for missing and incomplete data are proposed

Clustering techniques for data analysis and data completion of monitored structural responses of an offshore floating structure

Offshore Floating Structures (OFSs) present a major category of offshore structures that are often subjected to severe environmental conditions and harsh critical loading scenarios. The state of an OFS during its life-cycle must remain in the domain specified in the design, although this can be altered by normal aging due to usage, the action of the environment and accidental events. In recent years, the field of Structural Health Monitoring (SHM) has been growing at a fast rate, especially in different applications within the offshore structures' field (e.g. platforms and systems in oil and gas technology, risers, and offshore wind technology). Based on the monitored data of the SHM a diagnosis and most importantly a prognosis of the health status of the OFS can be assessed. Usually, measured data in long time span of different structural response quantities are used for the aforementioned assessment with, in some cases, unmeasured data. This paper deals with two objectives for the case of monitored structural response data of an OFS: (i) the implementation of clustering techniques for analysis of the structural response data and (b) the completion of missing structural response data based on appropriate clustering techniques

Robust-to-Modeling-Uncertainties Nonlinear Control Design for Offshore Structures

A controller design methodology for offshore structures is investigated. Because stochastic simulation is used for evaluation of the system’s performance in the design stage, nonlinear characteristics of the structural response and excitation can be explicitly incorporated into the assumed system model. Model parameters whose values are uncertain are probabilistically described. In this context, the controller is designed for optimal reliability, quantified as the probability that the performance will not exceed some acceptable bounds over some time duration. The methodology is illustrated with an example involving the control of a tension leg platform in an uncertain sea environment

Robust Reliability-based Design of Liquid Column Mass Dampers under Earthquake Excitation using an Analytical Reliability Approximation

The robust reliability-based design of Tuned Liquid Column Dampers (TLCD) and Liquid Column Vibration Absorbers (LCVA) under earthquake excitation is studied. The design objective is the minimization of the probability of failure, where failure is defined as the first-passage of the dynamical system trajectory out of a hypercubic safe region in the space of the performance variables. These variables correspond to response characteristics of the system that are considered important. Versions of the approach are described for the case of a nominal model and the case considering model uncertainty. In the latter case the concept of robust probability of failure is employed which considers a set of possible models for the dynamic system. The nonlinear characteristics of the damper response are addressed by including the excitation intensity as an uncertain parameter in the system description. An analytical approximation is used for the reliability estimation that allows for computationally efficient, gradient-based design optimization. Numerical issues are discussed. The validity of the reliability approximation is checked by comparing the results to those derived through direct Monte Carlo simulation of the nonlinear model. Applications to dynamical systems with single and multiple degrees of freedom are presented. For the latter case, other standard control synthesis methods are also considered and significant differences are illustrated between them and robust reliability-based design. Although this study focuses on optimization of TLCDs and LCVAs, it shows the efficiency of the proposed methodology for other systems that also involve model uncertainty