scholarly journals Impact Localization Method for Composite Plate Based on Low Sampling Rate Embedded Fiber Bragg Grating Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Zhuo Pang ◽  
Mei Yuan ◽  
Hao Song ◽  
Zongxia Jiao
Keyword(s):  
Fiber Bragg Grating ◽  
Sampling Rate ◽  
Classification Model ◽  
Support Vector ◽  
Bragg Grating ◽  
Localization Algorithm ◽  
Experimental Conditions ◽  
Localization Accuracy ◽  
The Impact ◽  
Impact Localization

Fiber Bragg Grating (FBG) sensors have been increasingly used in the field of Structural Health Monitoring (SHM) in recent years. In this paper, we proposed an impact localization algorithm based on the Empirical Mode Decomposition (EMD) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) to achieve better localization accuracy for the FBG-embedded plate. In our method, EMD is used to extract the features of FBG signals, and PSO-SVM is then applied to automatically train a classification model for the impact localization. Meanwhile, an impact monitoring system for the FBG-embedded composites has been established to actually validate our algorithm. Moreover, the relationship between the localization accuracy and the distance from impact to the nearest sensor has also been studied. Results suggest that the localization accuracy keeps increasing and is satisfactory, ranging from 93.89% to 97.14%, on our experimental conditions with the decrease of the distance. This article reports an effective and easy-implementing method for FBG signal processing on SHM systems of the composites.

scholarly journals Study on the Optimal Groove Shape and Glue Material for Fiber Bragg Grating Measuring Bolts

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1799 ◽  
Author(s):  
Yiming Zhao ◽  
Nong Zhang ◽  
Guangyao Si ◽  
Xuehua Li
Keyword(s):  
Fiber Bragg Grating ◽  
Linear Regression Analysis ◽  
Tensile Load ◽  
Tensile Tests ◽  
Bragg Grating ◽  
Engineering Practice ◽  
Direct Tension ◽  
Groove Shape ◽  
Fbg Sensors ◽  
The Impact

Fiber Bragg grating (FBG) measuring bolts, as a useful tool to evaluate the behaviors of steel bolts in underground engineering, can be manufactured by gluing the FBG sensors inside the grooves, which are usually symmetrical cuts along the steel bolt rod. The selection of the cut shape and the glue types could perceivably affect the final supporting strength of the bolts. Unfortunately, the impact of cut shape and glue type on bolting strength is not yet clear. In this study, based on direct tension tests, full tensile load–displacement curves of rock bolts with different groove shapes were obtained and analyzed. The effects of groove shape on the bolt strength were discussed, and the stress redistribution in the cross-section of a rock bolt with different grooves was simulated using ANSYS. The results indicated that the trapezoidal groove is best for manufacturing the FBG bolt due to its low reduction of supporting strength. Four types of glues commonly used for the FBG sensors were assessed by conducting tensile tests on the mechanical testing and simulation system and the static and dynamic optical interrogators system. Using linear regression analysis, the relationship between the reflected wavelength of FBG sensors and tensile load was obtained. Practical recommendations for glue selection in engineering practice are also provided.

scholarly journals Dynamic Deformation Reconstruction of Variable Section WING with Fiber Bragg Grating Sensors

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3350 ◽  
Author(s):  
Zhen Fu ◽  
Yong Zhao ◽  
Hong Bao ◽  
Feifei Zhao
Keyword(s):  
Finite Element ◽  
Fiber Bragg Grating ◽  
Real Time ◽  
Beam Theory ◽  
Reconstruction Algorithm ◽  
Support Vector ◽  
Bragg Grating ◽  
Variable Section ◽  
The Difference ◽  
Fuzzy Network

In order to monitor the variable-section wing deformation in real-time, this paper proposes a dynamic reconstruction algorithm based on the inverse finite element method and fuzzy network to sense the deformation of the variable-section beam structure. Firstly, based on Timoshenko beam theory and inverse finite element framework, a deformation reconstruction model of variable-section beam element was established. Then, considering the installation error of the fiber Bragg grating (FBG) sensor and the dynamic un-modeled error caused by the difference between the static model and dynamic model, the real-time measured strain was corrected using a solidified fuzzy network. The parameters of the fuzzy network were learned using support vector machines to enhance the generalization ability of the fuzzy network. The loading deformation experiment shows that the deformation of the variable section wing can be reconstructed with the proposed algorithm in high precision.

Precise hyperacuity estimation of spike timing from calcium imaging

2019 ◽  
Author(s):  
Huu Hoang ◽  
Masa-aki Sato ◽  
Shigeru Shinomoto ◽  
Shinichiro Tsutsumi ◽  
Miki Hashizume ◽  
...  
Keyword(s):  
Sampling Rate ◽  
Time Estimation ◽  
Spike Timing ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Spike Time ◽  
Experimental Conditions ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

SummaryTwo-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression

Optik ◽  
2019 ◽  
Vol 180 ◽  
pp. 244-253 ◽  
Author(s):  
Shizeng Lu ◽  
Mingshun Jiang ◽  
Xiaohong Wang ◽  
Hongliang Yu ◽  
Chenhui Su
Keyword(s):  
Support Vector Regression ◽  
Fiber Bragg Grating ◽  
Prediction Method ◽  
Support Vector ◽  
Bragg Grating ◽  
Damage Degree

scholarly journals TOWARDS PREDICTING RICE LOSS DUE TO TYPHOONS IN THE PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 63-70 ◽  
Author(s):  
S. Boeke ◽  
M. J. C. van den Homberg ◽  
A. Teklesadik ◽  
J. L. D. Fabila ◽  
D. Riquet ◽  
...  
Keyword(s):  
Binary Classification ◽  
Open Data ◽  
Absolute Error ◽  
The Philippines ◽  
Classification Model ◽  
Support Vector ◽  
Average Value ◽  
Early Action ◽  
Support Vector Regressor ◽  
The Impact

Abstract. Reliable predictions of the impact of natural hazards turning into a disaster is important for better targeting humanitarian response as well as for triggering early action. Open data and machine learning can be used to predict loss and damage to the houses and livelihoods of affected people. This research focuses on agricultural loss, more specifically rice loss in the Philippines due to typhoons. Regression and binary classification algorithms are trained using feature selection methods to find the most important explanatory features. Both geographical data from every province, and typhoon specific features of 11 historical typhoons are used as input. The percentage of lost rice area is considered as the output, with an average value of 7.1%. As for the regression task, the support vector regressor performed best with a Mean Absolute Error of 6.83 percentage points. For the classification model, thresholds of 20%, 30% and 40% are tested in order to find the best performing model. These thresholds represent different levels of lost rice fields for triggering anticipatory action towards farmers. The binary classifiers are trained to increase its ability to rightly predict the positive samples. In all three cases, the support vector classifier performed the best with a recall score of 88%, 75% and 81.82%, respectively. However, the precision score for each of these models was low: 17.05%, 14.46% and 10.84%, respectively. For both the support vector regressor and classifier, of all 14 available input features, only wind speed was selected as explanatory feature. Yet, for the other algorithms that were trained in this study, other sets of features were selected depending also on the hyperparameter settings. This variation in selected feature sets as well as the imprecise predictions were consequences of the small dataset that was used for this study. It is therefore important that data for more typhoons as well as data on other explanatory variables are gathered in order to make more robust and accurate predictions. Also, if loss data becomes available on municipality-level, rather than province-level, the models will become more accurate and valuable for operationalization.

scholarly journals Prediction Modeling of Household’s Preparedness of Natural Hazards Mitigation

2021 ◽  
Author(s):  
Chen Xia ◽  
Yuqing Hu
Keyword(s):  
Disaster Preparedness ◽  
Critical Factor ◽  
Personal Characteristics ◽  
Household Survey ◽  
Classification Model ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Household Preparedness ◽  
Distribution Studies ◽  
The Impact

Natural disasters are showing an increase in the magnitude, frequency, and geographic distribution. Studies have shown that individuals’ self-sufficiency, which largely depends on household preparedness, is very important for hazard mitigation in at least the first 72 hours following a disaster. However, for factors that influence a household’s disaster preparedness, though there are many studies trying to identify from different aspects, we still lack an integrative analysis on how these factors contribute to a household’s preparation. This paper aims to build a classification model to predict whether a household has prepared for a potential disaster based on their personal characteristics and the environment they located. We collect data from the Federal Emergency Management Agency’s National Household Survey in 2018 and train four classification models - logistic regression, decision trees, support vector machines, and multi-layer perceptron classifier models- to predict the impact of personal characteristics and the environment they located on household prepare for the potential natural disaster. Results show that the multi-layer perceptron classifier model outperforms others with the highest scoring on both recall (0.8531) and f1 measure (0.7386). In addition, feature selection results also show that among other factors, a household’s accessibility to disaster-related information is the most critical factor that impacts household disaster preparation. Though there is still room for further parameter optimization, the model gives a clue that we could support disaster management by gathering publicly accessible data.

Identification of Impact Location by Using Fiber Bragg Grating Based on Wavelet Transform and Support Vector Classifiers

2014 ◽  
Vol 41 (3) ◽  
pp. 0305006 ◽  
Author(s):  
路士增 Lu Shizeng ◽  
姜明顺 Jiang Mingshun ◽  
隋青美 Sui Qingmei ◽  
赛耀樟 Sai Yaozhang ◽  
曹玉强 Cao Yuqiang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fiber Bragg Grating ◽  
Support Vector ◽  
Bragg Grating ◽  
Impact Location
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