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A NOVEL PARALLEL METHOD FOR SPECKLE MASKING RECONSTRUCTION USING THE OPENMP
Xuebao Li,Yanfang Zheng 한국천문학회 2016 Journal of The Korean Astronomical Society Vol.49 No.4
High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256$\times$256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.
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GPU-ACCELERATED SPECKLE MASKING RECONSTRUCTION ALGORITHM FOR HIGH-RESOLUTION SOLAR IMAGES
Yanfang Zheng,Xuebao Li,Huifeng Tian,Qiliang Zhang,Chong Su,Lingyi Shi,Ta Zhou 한국천문학회 2018 Journal of The Korean Astronomical Society Vol.51 No.3
The near real-time speckle masking reconstruction technique has been developed to accelerate the processing of solar images to achieve high resolutions for ground-based solar telescopes. However, the reconstruction of solar subimages in such a speckle reconstruction is very time-consuming. We design and implement a new parallel speckle masking reconstruction algorithm based on the Compute Unified Device Architecture (CUDA) on General Purpose Graphics Processing Units (GPGPU). Tests are performed to validate the correctness of our program on NVIDIA GPGPU. Details of several parallel reconstruction steps are presented, and the parallel implementation between various modules shows a significant speed increase compared to the previous serial implementations. In addition, we present a comparison of runtimes across serial programs, the OpenMP-based method, and the new parallel method. The new parallel method shows a clear advantage for large scale data processing, and a speedup of around 9 to 10 is achieved in reconstructing one solar subimage of 256$\times$256 pixels. The speedup performance of the new parallel method exceeds that of OpenMP-based method overall. We conclude that the new parallel method would be of value, and contribute to real-time reconstruction of an entire solar image.
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MODIFIED CONVOLUTIONAL NEURAL NETWORK WITH TRANSFER LEARNING FOR SOLAR FLARE PREDICTION
Yanfang Zheng,Xuebao Li,Xinshuo Wang,Ta Zhou 한국천문학회 2019 Journal of The Korean Astronomical Society Vol.52 No.6
We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class are within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class ares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.6400.075 and TSS = 0.5260.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class are prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for are forecasting with reasonable prediction performance.
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Zhiwen Zhou,Gaoming Huang,Xuebao Wang 한국전자통신연구원 2019 ETRI Journal Vol.41 No.6
Presently, the extraction of hand‐crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high‐level abstract representations from the time‐frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning‐based architecture for multi‐platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.
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Wang Xiaoqing,Xiang Yang,Guo Zhiyong,Xia Xuebao,Shi Yuxiao,Xue Peng,Wu Shaowei 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.4
An experimental method of measuring acoustic surface radiation resistance is developed. The principle of the method is based on obtainingsource velocity and field pressure generated by the source. To measure surface radiation resistance, measuring probe was developedand measuring system was set up. Then, major factors that affect measurement accuracy is discussed and great improvements aregot. After that, experiments of measuring baffled circular piston were conducted to analyze its applicable frequency range. To verifyactual application effect, experiments of measuring the tube and cylinder heads of a diesel engine were performed. The results show thatthis measuring system can obtain resistance values in the frequency range from 460 to 1900 Hz with high precision. The measuring systemhas features of simple operation, convenient use, and high accuracy. Therefore, it can be used to determine surface resistance matrixof various structures.
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