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SAR Image De-noising Based on Residual Image Fusion and Sparse Representation
( Xiaole Ma ),( Shaohai Hu ),( Dongsheng Yang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.
Stock prediction based on random forest and LSTM neural network
Yilin Ma,Ruizhu Han,Xiaoling Fu 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
The data in the stock market are intricate. Principal Component Analysis (PCA) based on LSTM neural network can remove noise and improve the accuracy of stock prediction. A stock prediction model based on random forest and LSTM neural network is proposed to further improve the performance of stock prediction. Based on the data of Shanghai Composite Index from 2013 to 2017, this model and PCA + LSTM neural network model are simulated and compared. The experimental results show that this model is more suitable for stock prediction than PCA + LSTM model. In addition, the returns of trading strategies based on the above two models are higher than the benchmark buy-and-hold strategy, and the trading strategies based on the proposed model perform best.
Complete Mitochondrial Genome of the Chagas Disease Vector, Triatoma rubrofasciata
Li Dong,Xiaoling Ma,Mengfei Wang,Dan Zhu,Yuebiao Feng,Yi Zhang,Jingwen Wang 대한기생충학열대의학회 2018 The Korean Journal of Parasitology Vol.56 No.5
Triatoma rubrofasciata is a wide-spread vector of Chagas disease in Americas. In this study, we completed the mitochondrial genome sequencing of T. rubrofasciata. The total length of T. rubrofasciata mitochondrial genome was 17,150 bp with the base composition of 40.4% A, 11.6% G, 29.4% T and 18.6% C. It included 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and one control region. We constructed a phylogenetic tree on the 13 protein-coding genes of T. rubrofasciata and other 13 closely related species to show their phylogenic relationship. The determination of T. rubrofasciata mitogenome would play an important role in understanding the genetic diversity and evolution of triatomine bugs.
Preparation of anhydrous from red gypsum and effect of high strength gypsum on its properties
Changrong Liu,Lu Wang,Hongbin Tan,Faqin Dong,Xiaoling Ma,Feihua Yang 한양대학교 청정에너지연구소 2022 Journal of Ceramic Processing Research Vol.23 No.6
Red gypsum waste comes from titanium dioxide production by sulphuric acid method. Anhydrite was prepared from thewaste. The effects of calcined temperature on the properties of anhydrite were studied. The normal consistency of anhydritedecreased with the increase of calcined temperature, while the compressive strength firstly increase and then decrease. Theeffect of high strength gypsum on the properties of anhydrite was also studied. The normal consistency of sample decreasedwith the increase of high strength gypsum content, while the setting time firstly decrease and then increase, strength anddensity increased.
Liu Kunqi,Wang Junxia,Wu Anhang,Wang Jin,Liu Die,Ma Xiaoling 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.6
In this work, Sr0⋅5Zr2(PO4)3-SmPO4 dual-phase ceramics were prepared via in-situ synthesis process, which is a potential novel nuclear waste form for immobilizing the fission product 90Sr and the trivalent actinide radionuclides in high-level waste (HLW). And the preparation technology, microstructure and chemical durability of Sr0⋅5Zr2(PO4)3-SmPO4 dual-phase ceramics were systematically investigated. It was confirmed that the optimum microwave-sintering temperature (1050 ◦C) and heat preservation time (1.5 h) is estimated by Archimedes method. Besides, the as-prepared samples that were consisted of strontium zirconium phosphate (SrZP) and monazite showed the remarkable densification, in which the two crystalline phases were intermixed well with each other. Meanwhile, the formation and evolution of microstructure was also consistent with the variational rule of Sr0⋅5Zr2(PO4)3/SmPO4, indicating that there was not mutual reaction during the in-situ synthesis process. The PCT and MCC-1 experimental results demonstrated that the elemental normalized leaching rates of tested samples are all at a low level (LRSr ~10 4 g‧m 2‧d 1, LRZr ~10 8-10 6 g‧m 2‧d 1, LRSm ~10 7-10 5 g‧m 2‧d 1 and LRP ~10 4 g‧m 2‧d 1). It is indicated that Sr0⋅5Zr2(PO4)3-SmPO4 dual-phase ceramics possesses excellent chemical durability for HLW disposal.