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Two Dimensional Phase Unwrapping Theory Algorithms And Software

In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.

1D-Local Binary Pattern Based Feature Extraction for Classification of Epileptic EEG Signals, Applied Mathematics and Computation243 (2014): 209-219.

Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder–decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.

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Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.

PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity image.

For overcoming conventional photoelasticity limitations when evaluating the stress field in loaded bodies, this paper proposes a Generative Adversarial Network (GAN) while maintaining performance, gaining experimental stability, and shorting time response. Due to the absence of public photoelasticity data, a synthetic dataset was generated by using analytic stress maps and crops from them. In this case, more than 100000 pair of images relating fringe colors to their respective stress surfaces were used for learning to unwrap the stress information contained into the fringes. Main results of the model indicate its capability of recovering the stress field achieving an averaged performance of 0.93±0.18 according to the structural similarity index (SSIM). These results represent a great opportunity for exploring GAN models in real time stress evaluations.

Generalized adversarial networks for stress field recovering processes from photoelasticity images

Extending photoelasticity studies to industrial applications is a complex process generally limited by the image acquisition assembly and the computational methods for demodulating the stress field wrapped into the color fringe patterns. In response to such drawbacks, this paper proposes an auto-encoder based on deep convolutional neural networks, called StressNet, to recover the stress map from one single isochromatic image. In this case, the public dataset of synthetic photoelasticity images `Isochromatic-art' was used for training and testing achieving an averaged performance of 0.95 +/- 0.04 according to the structural similarity index. With these results, the proposed network is capable of obtaining a continuous stress surface which represents a great opportunity toward developing real time stress evaluations.

StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

Evaluating the stress distribution in structures under temporal loads is being carry out by many of the engineering applications such as: impacts, cracks, bending, thermal-transient and other. In those cases, conventional photoelasticity techniques are more complex to evaluate the stress field because of their complicated and expensive experiments, quantity of computational procedures, and their time by time analysis. However, dynamic photoelasticity experiments produce temporal information, such as color variations, which could be analyzed, described, and classified in order to perform a whole stress field evaluation. In this paper, the one-dimensional local binary patterns (1D-LBP) are used to describe such color variations and use them to identify the stress values they belong. For different experimental configurations, this proposal achieved an accuracy of 98% when evaluating the stress field of cases with similar light sources than with a reference experiment, and 92% for experiments with other light conditions. These results make this descriptor able to determine categorical stress maps from a photoelasticity video itself, which significantly opens new opportunities to simplify the experimental and computational operations that limit the stress evaluation process in line with the dynamic experiment.

One-dimensional local binary pattern based color descriptor to classify stress values from photoelasticity videos

Isochromatic-art: a computational dataset for evaluating the stress distribution of loaded bodies by digital photoelasticity

Some engineering areas have the challenge to discover geometries with mechanical high performance against complex applications, which is a defiant design task. With this objective, recent works have demonstrated the powerful contributions that bioinspired experiments can offer a wide diversity of applications. In this sense, this paper analyzes differences in the mechanical response of a stress concentrator when comparing conventional rings and their respective bioinspired representation. Here, the bioinspired geometry comes from a cut of a transversal section of rice root due to its resistance to internal pressures. The mechanical analysis is carried out by hybrid integration of photoelasticity studies, digital image correlation, and finite element methods. In this case, results indicate that preserving the same quantity of material, bioinspired geometries are more sensitive to stress and strain than conventional rings.

Juan Carlos Briñez de León

Papers

Generalized adversarial networks for stress field recovering processes from photoelasticity images

StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

One-dimensional local binary pattern based color descriptor to classify stress values from photoelasticity videos

Isochromatic-art: a computational dataset for evaluating the stress distribution of loaded bodies by digital photoelasticity

Digital photoelasticity and DIC applied to stress and strain hybrid evaluation of bioinspired structures from rice root cross-section