Experimental Investigation and Error Analysis of High Precision FBG Displacement Sensor for Structural Health Monitoring

Li, Chuang; Sun, Li; Xu, Ziqiang; Wu, Xiangxiang; Liang, Tianqi; Shi, Weixing

doi:10.1142/s0219455420400118

Cited by 103 publications

(27 citation statements)

References 27 publications

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“…Image feature refers to the mathematical means of characterizing digital image processing and is the foundation of image processing technology. Generally speaking, as researchers deepen their understanding of images, image representation methods have also undergone an evolutionary process from shallow to deep [31][32][33][34]. Among them, the shallow representation of the image is mainly embodied in intuitive forms such as color features, geometric features, and shape features.…”

Section: Image Features In Motion Detectionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.

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Section: Image Features In Motion Detectionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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“…Following investigation of researchers in different fields of organic compounds, [ 20–53 ] here, we are focused on thermodynamic and kinetic aspects of silapyridine, 3 , as well as its fused ring analogues ( 4–12 ), in two singlet ( 4s–12s ) and triplet ( 4t–12t ) states (Scheme 2). Substituent effects of fused aromatic rings on two of sides of silapyridine are coupled with suitable canonical forms and MEP maps.…”

Section: Resultsmentioning

confidence: 99%

A computational perspective of novel N‐heterocyclic silylenes using density functional theory

Hassanpour

Heravi

Nezhad

et al. 2021

J of Physical Organic Chem

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In this theoretical survey, we inspect substituent effects of two fused benzene, pyrrole, phosphole, furan, and thiophene rings on the stability, polarity, charge distribution, nucleophilicity, electrophilicity, and aromaticity of singlet (s) and triplet (t) silylenes, at density functional theory (DFT). Singlet silylenes as ground state exhibit more stability than their corresponding triplet congeners. In contrast to previous reports on Hammick carbenes, here higher thermodynamic stability (singlet‐triplet energy difference or ΔEs‐t = Et − Es) is considered for silylene situated between two sulfur heteroatoms of two substituted thiophene rings, in a “chair” arrangement in the direction of the silylenic center. Regardless of how arranged, the order of stabilizing effect for fused rings is thiophene > furan > pyrrole > phosphole > benzene. The substituted Hammick silylenes with two fused heterocyclic rings show more stability than synthesized silylenes by Denk and Kira. Also, higher kinetic stability (the frontier molecular orbitals energy difference or ΔEHOMO‐LUMO) is revealed by silylene situated between two oxygen heteroatoms of two substituted furan rings, in a “W” arrangement to the divalent center. The scrutinized singlet structures display wider band gap than their triplet states. Every triplet silylene shows higher nucleophilicity (about 1/5 times) than its corresponding singlet state. Commonly, six‐membered aromatic N‐heterocyclic silylenes (NHSis) show higher N and lower ω than that of five‐membered aromatic NHSis. The highest N and the least ω character is anticipated for the substituted pyrrole NHSis in either “W” arrangement or “chair” orientation toward the silylenic center, among the fused species.

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“…Mathematical Problems in Engineering [59][60][61], whale optimization algorithm (WOA) [62][63][64], grey wolf optimizer (GWO) [65,66], bacterial foraging optimization (BFO) [67], and grasshopper optimization algorithm (GOA) [68]. e aim of optimization is to determine a suitable value for one or more parameters between all possible values for them in order to minimize or maximize a function and it can be applied to find feasible answer to many potential real-life applications such as deployment optimization [69], adaptive control concepts [35,[70][71][72], computer vision techniques [73], transportation networks [74], image and video processing [75][76][77][78][79][80], decision-making approaches [81][82][83], power allocation systems [84], sensor fusion approaches [85], monitoring systems [86][87][88][89], and deep learning models [19,[90][91][92][93]. e PSO algorithm as an optimization algorithm is a social interaction model between independent particles that use their social knowledge to find the minimum and maximum value of a function [15].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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Experimental Investigation and Error Analysis of High Precision FBG Displacement Sensor for Structural Health Monitoring

Cited by 103 publications

References 27 publications

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

A computational perspective of novel N‐heterocyclic silylenes using density functional theory

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

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