With the development of science and technology, the application in artificial intelligence has been more and more popular, as well as smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of ELM (Extreme Learning Machine). At the same time, the sampling frequency with low rate is mentioned in this paper, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. And the intelligent hardware is designed by our team, which is wireless sensor network (WSN) composed by a lot of wireless sensors. Experiments in this paper show that the proposed method can accurately determine the using electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home.

No abstract

With the development of science and technology, the application in artificial intelligence has been more and more popular, as well as smart home has become a hot topic. And pattern recognition adapts to home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of ELM (Extreme Learning Machine). At the same time, the sampling frequency with low rate is applied in this paper, which can obtain the required data through intelligent hardware directly, as well as reducing the cost of investment. And, the intelligent hardware is designed by our team, which is wireless sensor network (WSN) composed by a lot of wireless sensors. Experiments in this paper show that the proposed method can accurately determine the using electrical appliances, and greatly improve the accuracy of identification, which can further improve the popularity of smart home.

Background: Most studies have shown that Beta-Casomorphin-7 (BCM-7) has many physiological functions, this study aims to investigate the effects of BCM-7 on renal function and immunity in naturally aging mice and to reveal the effects of BCM-7 on health and provide more theoretical reference for inflammatory aging.Methods: All mice were weighed weekly and the organ indexes were calculated, Paraffin sections were prepared for kidney histopathological examination with conventional Haematoxylin and Eosin (HE) staining, renal function indexes and cytokines were main measured by ELISA and biochemical colorimetry.Result: The weight of each group increased during gastric perfusion and the higher the intervention concentration, the lower the weight gain rate. The renal index was not affected by BCM-7, but the spleen index was significantly decreased by medium-dose BCM-7. There were inflammatory cell infiltration and other renal lesions in the aging control group and intervention group. A medium dose of BCM-7 can significantly reduce Cystatin C (Cys-C) content in aging mice, the content of Adrenocorticotropic hormone (ACTH) in aged mice decreased with the increase of BCM-7 intervention dose. BCM-7 and aging caused cytokine imbalance in mice.