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Liying Niu,Dajing Li,Chunquan Liu,Fuguo Liu 한국식품과학회 2015 Food Science and Biotechnology Vol.24 No.3
Lipoxygenases (LOX) in milk-stage sweet corn and waxy corn were extracted using a phosphate buffer (pH 7.0) and enzyme activities were determined using linoleic acid as a substrate. Michaelis constant (Km) values, decimal reduction times (D value), temperature sensitivity parameters (Z value), and activation energies (Ea) were calculated. Enzymes from both corn types followed first-order inactivation kinetics within 0-25 min and 50- 70℃. However, enzymes exhibited different pH profiles and affinities toward linoleic acid. Km values (4.34 and 1.40 mM for sweet corn and waxy corn, respectively), heat stability values, and Ea values (116.81 and 246.82 kJ/mol) were different. Waxy corn LOX was more heat stable below 65℃ with a higher D value, but was more temperature sensitive with a lower Z value. The different characteristics suggested the presence of different isoenzymes and necessitated the use of different parameters for blanching.
A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting
Cheng Yuxin,Le Haozhe,Li Chunquan,Huang Jiehui,Liu Peter X. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
It is still a challenging problem for most existing forecasting methods to obtain accurate and rapid prediction performance in short-term load forecasting because of the complexity and non-linearity of the electric load signals. To solve this problem, this paper proposes a hybrid forecasting model. In this hybrid forecasting model, an effi cient hybrid decomposition method is fi rst developed by a new combination mechanism between the ensemble empirical mode decomposition, approximate entropy, and empirical wavelet transform, enhancing the effi ciency and accuracy problems of traditional decomposition methods. Afterward, a new hybrid neural network called broad learning system-back propagation (BLS-BP) is established to predict multiple signal sequences from the proposed hybrid decomposition method. Specifi cally, in the proposed BLS-BP, a broad learning system can eff ectively reduce the computational cost, however, BP can eff ectively improve the prediction accuracy. Therefore, a reasonable combination of BLS and BP is established to obtain the compromise between computational cost and prediction accuracy. Finally, to improve the generalization ability of the model, a hybrid network based on the sliding window and cross-validation method is proposed, further improving the predictive accuracy. Owing to the novel and eff ective cooperation of the above three aspects, the proposed hybrid forecasting model has higher accuracy, faster effi ciency, and better robustness compared with other state-of-the-art algorithms. The experimental result demonstrates the above facts.