Data Augmentation Using Contour Image for Convolutional Neural Network

With the development of artificial intelligence-related technologies such as deep learning,various organizations,including the government,are making various efforts to generate and manage big data for use in artificial intelligence.However,it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage.There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology.Therefore,this study proposes a mixed contour data augmentation technique,which is a data augmentation technique using contour images,to solve a problem caused by a lack of data.ResNet,a famous convolutional neural network(CNN)architecture,and CIFAR-10,a benchmark data set,are used for experimental performance evaluation to prove the superiority of the proposed method.And to prove that high performance improvement can be achieved even with a small training dataset,the ratio of the training dataset was divided into 70%,50%,and 30%for comparative analysis.As a result of applying the mixed contour data augmentation technique,it was possible to achieve a classification accuracy improvement of up to 4.64%and high accuracy even with a small amount of data set.In addition,it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.

Image information transfer via electromagnetically induced transparency-based slow light

In this work, we experimentally demonstrate an image information transfer between two channels by using slow light based on electromagnetically induced transparency（EIT） in a solid. The probe optical image is slowed due to steep dispersion induced by EIT. By applying an additional control field to an EIT-driven medium, the slowed image is transferred into two information channels. Image intensities between two information channels can be controlled by adjusting the intensities of the control fields. The similarity of output images is further analyzed. This image information transfer allows for manipulating images in a controlled fashion, and will be important in further information processing.

An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal

Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.

THERMAL IMAGING STUDY OF SCALAR TRANSPORT IN SHALLOW WAKES

The thermal imaging technique relies on the usage of infrared signal to detect the temperature field.Using temperature as a flow tracer,thermography is used to investigate the scalar transport in the shallow-water wake generated by an emergent circular cylinder.Thermal imaging is demonstrated to be a good quantitative flow visualization technique for studying turbulent mixing phenomena in shallow waters.A key advantage of the thermal imaging method over other scalar measurement techniques,such as the Laser Induced Fluorescence（LIF）and Planar Concentration Analysis（PCA）methods,is that it involves a very simple experimental setup.The dispersion characteristics captured with this technique are found to be similar to past studies with traditional measurement techniques.