Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

Optimization of process variables in drilling of carbon fiber reinforced plastics using various multi criteria decision making approaches

Carbon fiber-reinforced polymers are one of the lightweight materials used in structural design due to their exceptional mechanical performances. The drilling operation is indispensable as it facilitates the assembling of various manufactured components. However, drilling of fibrous laminates is deemed difficult in comparison to the traditional metals because of the anisotropic and non-homogeneous nature. The present work addresses the parametric effect on the drilled hole delamination and further reduces it with an optimal combination of parameters for multi-objectives using different multi-criterion decision-making techniques. Initially, the response surface-based regression model of delamination as a function of three static inputs has been developed, further revised with induced thrust as well as mean torque for the improvisation of the prediction capability. Finally, for the overall improvement, a decision-making model has been used that includes grey relation analysis, technique for order performance by similarity to ideal solution, and VIšekriterijumsko Kompromisno Rangiranje method. The delamination was found to be minimum at a low drill point angle (100°), high spindle rotation (2150 min⁻¹ ), and low feed rate (0.025 mm/rev) due to reduced thrust force. The mean absolute prediction error was significantly improved considering root mean square torque rather than axial thrust with process variables.     

Gender-related differences in susceptibility of A/J mouse to benzo[a]pyrene-induced pulmonary and forestomach tumorigenesis

A commercial patient dose verification system utilizing non-invasive metal oxide semiconductor field effect transistor (MOSFET) dosimeters originally designed for radiotherapy applications has been evaluated for use at diagnostic energy levels. The system features multiple dosimeters that may be used to monitor entrance or exit skin dose and intracavity doses in phantoms in real time. We have characterized both the standard MOSFET dosimeter designed for radiotherapy dose verification and a newly developed "high sensitivity" MOSFET dosimeter designed for lower dose measurements. The sensitivity, linearity, angular response, post-exposure response, and physical characteristics were evaluated. The average sensitivity (free in air, including backscatter) of the radiotherapy MOSFET dosimeters ranged from 3.55 x 10(4) mV per C kg(-1) (9.2 mV R(-1)) to 4.87 x 10(4) mV per C kg(-1) (12.6 mV R(-1)) depending on the energy of the x-ray field. The sensitivity of the "high sensitivity" MOSFET dosimeters ranged from 1.15 x 10(5) mV per C kg(-1) (29.7 mV R(-1)) to 1.38 x 10(5) mV per C kg(-1) (35.7 mV R(-1)) depending on the energy of the x-ray field. The high sensitivity dosimeters demonstrated excellent linearity at high energies (90 and 120 kVp) and acceptable linearity at lower energies (60 kVp). The angular response was significant for free-in-air exposures, as illustrated by the sensitivity differences between the two sides of the dosimeter, but was excellent for measurements within a tissue equivalent cylinder. The post-exposure drift response is a complicated but reproducible function of time. Real-time monitoring requires little if any corrections for the post-exposure drift response. The MOSFET dosimeter system brings some unique capabilities to diagnostic radiology dosimetry including small size, real-time capabilities, nondestructive measurement, good linearity, and a predictable angular response.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.     

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000-2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Application of support vector machines in scour prediction on grade-control structures

Research into the problem of predicting the maximum depth of scour on grade-control structures like sluice gates, weirs and check dams, etc., has been mainly of an experimental nature and several investigators have proposed a number of empirical relations for a particular situation. These traditional scour prediction equations, although offer some guidance on the likely magnitude of maximum scour depth, yet applicable to a limited range of the situations. It appears from the literature review that a regression mathematical model for predicting maximum depth of scour under all circumstances is not currently available. This paper explores the potential of support vector machines in modeling the scour from the available laboratory and field data obtained form the earlier published studies. To compare the results, a recently proposed empirical relation and a feed forward back propagation neural network model are also used in the present study. The outcome from the support vector machines-based modeling approach suggests a better performance in comparison to both the empirical relation and back propagation neural network approach with the laboratory data. The results also suggest an encouraging performance by the support vector machines learning technique in comparison to both empirical relation as well as neural network approach in scaling up the results from laboratory to field conditions for the purpose of scour prediction.     

Structural,optical and electrical properties of Cu<Subscript>2</Subscript>Zn<Subscript>1−x</Subscript>Cd<Subscript>x</Subscript>SnS<Subscript>4</Subscript> quinternary alloys nanostructures deposited on porous silicon

The Cu₂Zn_1?xCd_xSnS₄ quinternary alloy nanostructures with different Cd contents were grown using spin coating technique on porous silicon (63.93 %) substrate. The structural properties of Cu₂Zn_1?xCd_xSnS₄/PS were investigated by X-ray diffraction and field emission-scanning electron microscope (FE-SEM). The optical properties studied through photoluminescence technique, indicated that the band gap is shifted as Cd content increases from 1.84 eV at x = 0 to 1.76 eV at x = 1. The electrical characterization of the Ag/n-PS/Cu₂Zn_1?xCd_xSnS₄/Ag diode through current to voltage (I–V) characterization shows the highest photo-response of (value if any) at Cu₂Zn_0.4Cd_0.6SnS₄ composition.     

A compact printed multistubs loaded resonator rectangular monopole antenna design for multiband wireless systems

In the present article, a compact triple‐band multistubs loaded resonator printed monopole antenna is proposed. The antenna consists of a quarter wavelength two asymmetrical inverted L‐shaped stubs to excite two resonant modes for 3.5/5.5 GHz bands and one integrated horizontally T‐shaped stub with inverted long L‐shaped stub to excite resonant mode for 2.5 GHz band. By loading these stub resonators along y‐axis with distinct gaps, the antenna resonates at three frequencies 2.57/3.52/5.51 GHz covering the desired bands while keeping compact size of 24 × 30 mm² (0.2 × 0.25 ). The proposed antenna is fabricated on Rogers RT/duroid 5880 substrate with thickness 0.79 mm and its performance experimentally verified. The measured results reveal that the antenna has the impedance bandwidths of about 210 MHz (2.50‐2.71 GHz), 260 MHz (3.37‐3.63 GHz), and 650 MHz (5.20‐5.85 GHz), for 2.5/3.5/5.5 GHz WiMAX and 5.2/5.8 GHz WLAN band systems. The antenna provides omnidirectional radiation patterns and flat antenna gains over the three operating bands. In addition, the design approach and effects of multistubs resonator lengths on the operating bands are also examined and discussed in detail.