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RISS 인기검색어
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- 저자 : Dai Yuntao (검색결과 2 건)
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Dai, Yuntao,Witkamp, Geert-Jan,Verpoorte, Robert,Choi, Young Hae American Chemical Society 2013 ANALYTICAL CHEMISTRY - Vol.85 No.13
<P>Developing green solvents with low toxicity and cost is an important issue for the biochemical industry. Synthetic ionic liquids and deep eutectic solvents have received considerable attention due to their negligible volatility at room temperature, high solubilization ability, and tunable selectivity. However, the potential toxicity of the synthetic ionic liquids and the solid state at room temperature of most deep eutectic solvents hamper their application as extraction solvents. In this study, a wide range of recently discovered natural ionic liquids and deep eutectic solvents (NADES) composed of natural compounds were investigated for the extraction of phenolic compounds of diverse polarity. Safflower was selected as a case study because its aromatic pigments cover a wide range of polarities. Many advantageous features of NADES (such as their sustainability, biodegradability combined with acceptable pharmaceutical toxicity profiles, and their high solubilization power of both polar and nonpolar compounds) suggest their potential as green solvents for extraction. Experiments with different NADES and multivariate data analysis demonstrated that the extractability of both polar and less polar metabolites was greater with NADES than conventional solvents. The water content in NADES proved to have the biggest effect on the yield of phenolic compounds. Most major phenolic compounds were recovered from NADES with a yield between 75% and 97%. This study reveals the potential of NADES for applications involving the extraction of bioactive compounds from natural sources.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancham/2013/ancham.2013.85.issue-13/ac400432p/production/images/medium/ac-2013-00432p_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ac400432p'>ACS Electronic Supporting Info</A></P>
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Dai Yuntao,Peng Lizhang,Juan Zhaobo,Liang Yuan,Shen Jihong,Wang Shujuan,Tan Sichao,Yu Hongyan,Sun Mingze 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4
In the fault diagnosis problem, where sample data of fault cases are imbalanced, data generation and expansion are performed based on a generative adversarial network to obtain balanced data for training. Combining a gated recurrent neural network and an autoencoder model, the GRU-BEGAN model for generating multiple time series data is proposed for the intelligent fault diagnosis of imbalanced nuclear power plant data. To guarantee the consistency of the probability distribution between the generated data and real data, the K-L losses are included as a part of the loss function of the generator. At the same time, the potential feature vector of the real data obtained by the discriminator encoder is introduced as a hidden variable in the generator, and the similarity between the generated data and the real data is controlled by introducing the hidden variables according to the probability to make the generated data diverse. For the imbalanced fault dataset of the nuclear power plant thermal–hydraulic systems, the proposed GRU-BEGAN model is used to expand the original data to obtain a balanced state. Then, a 1D-CNN fault diagnosis model is established based on a convolutional neural network. The experimental results show that the fault diagnosis accuracy of the total test data is improved by 1.45% after data expansion, and the fault diagnosis accuracy of the minority sample is improved by 6.8% after data expansion.
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