191,531 research outputs found
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models
Deep convolutional neural networks have recently achieved great success on
image aesthetics assessment task. In this paper, we propose an efficient method
which takes the global, local and scene-aware information of images into
consideration and exploits the composite features extracted from corresponding
pretrained deep learning models to classify the derived features with support
vector machine. Contrary to popular methods that require fine-tuning or
training a new model from scratch, our training-free method directly takes the
deep features generated by off-the-shelf models for image classification and
scene recognition. Also, we analyzed the factors that could influence the
performance from two aspects: the architecture of the deep neural network and
the contribution of local and scene-aware information. It turns out that deep
residual network could produce more aesthetics-aware image representation and
composite features lead to the improvement of overall performance. Experiments
on common large-scale aesthetics assessment benchmarks demonstrate that our
method outperforms the state-of-the-art results in photo aesthetics assessment.Comment: Accepted by ICIP 201
Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models
In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available
Assessing the effects of power quality on partial discharge behaviour through machine learning
Partial discharge (PD) is commonly used as an indicator of insulation health in high voltage equipment, but research has indicated that power quality, particularly harmonics, can strongly influence the discharge behaviour and the corresponding pattern observed. Unacknowledged variation in harmonics of the excitation voltage waveform can influence the insulation's degradation, leading to possible misinterpretation of diagnostic data and erroneous estimates of the insulation's ageing state, thus resulting in inappropriate asset management decisions. This paper reports on a suite of classifiers for identifying pertinent harmonic attributes from PD data, and presents results of techniques for improving their accuracy. Aspects of PD field monitoring are used to design a practical system for on-line monitoring of voltage harmonics. This system yields a report on the harmonics experienced during the monitoring period
Interpretation of partial discharge activity in the presence of harmonics
Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification of harmonic influenced partial discharge patterns. The Total Harmonic Distortion (THD) index was varied to a maximum of 40%. The results show progressive development for interpretation of condition monitoring data, improving the asset manager's holistic view of an asset's health
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