Identification of earthquake induced structural damage based on synchroextracting transform

Several popular time-frequency techniques,including the Wigner-Ville distribution,smoothed pseudo-Wigner-Ville distribution,wavelet transform,synchrosqueezing transform,Hilbert-Huang transform,and Gabor-Wigner transform,are investigated to determine how well they can identify damage to structures.In this work,a synchroextracting transform(SET)based on the short-time Fourier transform is proposed for estimating post-earthquake structural damage.The performance of SET for artificially generated signals and actual earthquake signals is examined with existing methods.Amongst other tested techniques,SET improves frequency resolution to a great extent by lowering the influence of smearing along the time-frequency plane.Hence,interpretation and readability with the proposed method are improved,and small changes in the time-varying frequency characteristics of the damaged buildings are easily detected through the SET method.

DFD-Net:lung cancer detection from denoised CT scan image using deep learning

The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer.The noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every nodule.In this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this complexity.The introduced model is composed of denoising and detection part in an end to end manner.First,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing stage.Then,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is employed.The two paths focus on the joint integration of local and global features.To this end,each path employs different receptive field size which aids to model local and global dependencies.To further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative features.Finally,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance.We found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and size.Our intensive experimental results achieved competitive results.