The objectives of this study were to investigate the effects of soybean isoflavone (SI) and astragalus polysaccharide (APS) mixture on the colostrum components, serum antioxidant, immune and hormone levels of lactating sows. A total of 72 healthy Yorkshire × Landrace lactating sows, were randomly divided into four treatments with six replicates and three lactating sows for each replicate. The control group was fed the basal diet, while the experimental groups were fed the basal diet with 100, 200 and 300 mg/kg SI and APS mixture in the form of powder, respectively. Compared with the control group, (a) the total lactation yield of the 200 mg/kg group was significantly higher (p < 0.05) at 21 days, (b) there was no significant difference in colostrum composition, (c) TG, CHO and MDA content in each treatment group were significantly decreased (p < 0.05), (d) IgA, GH, IGF-1, TNF-α and SOD contents in the 200 mg/kg group were significantly increased (p < 0.05). The SI and APS mixture could improve the average daily feed intake, lactation yield, serum antioxidant activities, immune function, and hormone levels of lactating sows, and the optimum dosage in this study was 200 mg/kg.
Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.
Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection. Firstly, the variational mode decomposition (VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy (mRMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network (BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine (LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.
Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.