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- 저자 : Yilin Ma (검색결과 4 건)
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Catalytic decomposition of N2O over RhOx supported on metal phosphates
YiLin,Zhen Ma,Tao Meng 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.28 No.-
RhOx/M–P–O (M = Mg, Al, Ca, Fe, Co, Zn, La) catalysts were tested in N2O decomposition. RhOx/Ca–P–Oshowed the highest activity, achieving complete N2O conversion at 300 8C. RhOx/La–P–O is the secondmost active catalyst, achieving almost complete conversion at 375 8C. RhOx/Mg–P–O, RhOx/Co–P–O, andRhOx/Al–P–O showed lower activities, whereas RhOx/Fe–P–O and RhOx/Zn–P–O were completelyinactive. The high activity of RhOx/Ca–P–O is ascribed to the presence of very small RhOx particles, morebasic sites, and the easiness of desorbing O2 at low temperatures. The inhibiting effects of co-fed O2 and/or H2O on the catalytic activity of RhOx/Ca–P–O are reversible
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Research on stock trading strategy based on deep neural network
Yilin Ma,Ruizhu Han 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
Deep neural network is widely concerned with the concept of deep learning. However, there are few researches focus on the application of deep neural network for stock trading strategy. Excellent trading strategies can not only help investors get high profit, but also effectively reduce the income risk. This paper studies 7 trading strategies based on a deep neural network, and uses the 2009-2015 years historical data of Shanghai Composite Index for experiments through sliding window approach, and adopts the accuracy, rate of excess return, volatility of yield and information ratio to measure the advantages and disadvantages of different trading strategies. According to the experimental results, a trading strategy suitable for the deep neural network is found. This trading strategy can not only achieve a high predictive accuracy but also have a low volatility, which can help investors reduce the risk of loss effectively while obtaining satisfactory returns.
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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.
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Yilin Jie,Zhaoxia Cai,Shanshan Li,Zhuqing Xie,Meihu Ma,Xi Huang 한국고분자학회 2017 Macromolecular Research Vol.25 No.9
In a tenfold-concentrated simulated body fluid, a strategy for rapid deposition of a biomimetic calcium phosphate layer on the scaffolds of electrospun collagen nanofiber membranes was developed. The aim of this study was to explore the effects of mineralization conditions and phosvitin (PV) on hydroxyapatite nucleation and growth. The mineralization model, the pH of the environment, and the deposition time were optimized. Scanning electron microscopy (SEM) images demonstrated that homogeneous and well-crystallized inorganic mineral layers were generated in the dynamic mineralization model system after incubating 3 h at pH 5.7. PV, which possesses the highest level of phosphorylation among egg proteins, was used as a model protein to investigate the contribution of PV in the mineralization process. The morphological structure and composition of the collagen/calcium phosphate composite nanofibers were also characterized by energy dispersive spectroscopy, scanning photoelectron spectrometer, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy. XRD results showed the transformation process of mineralization materials from dicalcium phosphate dihydrate (DCPD) to HA through the changes of characteristic peaks at approximately 11° of DCPD and 31.8° of HA. 1.0 mg/mL. Phosvitin significantly promoted the phase transformation from DCPD to hydroxyapatite. High performance liquid chromatography results indicated that PV induced the mineralization rather than being the part of the hydroxyapatite. The minerals formed on electrospun collagen nanofiber membranes were identified to be from hydroxyapatite. These findings extended the potential application field of PV to biomimetic material.
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