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Smart Sensors for Structural Health Monitoring

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ISBN: 9783039217588 9783039217595 Year: Pages: 342 DOI: 10.3390/books978-3-03921-759-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Abstract

Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. This Special Issue covers a wide range of related topics such as innovative sensors and sensing technologies for crack, displacement, and sudden event monitoring, sensor optimization, and novel sensor data processing algorithms for damage and defect detection, operational modal analysis, and system identification of a wide variety of structures (bridges, transmission line towers, high-speed trains, masonry light houses, etc.).

Keywords

optical crack growth sensor --- digital sampling moiré --- 2D crack growth --- calibration --- concrete crack --- feature extraction --- mapping construction --- fuzzy classification --- rotary ultrasonic array --- bending stiffness --- damage identification --- environmental noise --- bridge --- test vehicle --- structural impact monitoring --- sensors distribution optimization --- NSGA-II --- energy analysis of wavelet band --- principal component analysis --- transmission tower --- settlement --- wind force --- acceleration --- modal frequencies --- sudden event monitoring --- wireless smart sensors --- demand-based nodes --- event-triggered sensing --- data fusion --- patch antenna --- sensor --- structural health monitoring --- crack identification --- resonant frequency --- damage identification --- sensor optimization --- Virtual Distortion Method (VDM) --- Particle Swarm Optimization (PSO) algorithm --- sensitivity --- structural health monitoring --- piezoelectric wafer active sensors --- active sensing --- passive sensing --- damage detection --- acoustic emission --- uniaxial stress measurement --- structural steel members --- amplitude spectrum --- phase spectrum --- shear-wave birefringence --- acoustoelastic effect --- damage detection --- smartphones --- steel frame --- shaking table tests --- wavelet packet decomposition --- low-velocity impacts --- strain wave --- impactor stiffness --- data processing --- feature selection --- impact identification --- crack --- strain --- distributed dense sensor network --- structural health monitoring --- fibre bundle --- reflective optical sensor --- tip clearance --- turbine --- aero engine --- principal component analysis --- space window --- time window --- damage detection --- length effect --- stress detection --- electromagnetic oscillation --- steel strand --- concrete structures --- SHM --- stretching method --- model updating --- displacement sensor --- helical antenna --- resonant frequency --- perturbation theory --- normal mode --- wheel minor defect --- high-speed train --- online wayside detection --- Bayesian blind source separation --- FBG sensor array

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

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