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In this paper, a non-intrusive load identification algorithm for residents based on prior knowledge and statistical learning model is proposed to solve the problem of insufficient electric heating subdivision capability in traditional identification technology. In this paper, the electric heating subdivision research is carried out for the auxiliary heating equipment of washing machine, electric kettle, electric rice cooker, electric water heater. The subdivision of auxiliary heating equipment is realized through the equipment operation association algorithm, and the model training of non-auxiliary heating equipment classification is realized based on the limited feedback information of users and expert annotation. The experimental results show that the technical framework proposed in this paper realizes the subdivision of electric heating equipment on the basis of the event detection load identification algorithm and F1 socre above 0.9 is achieved in the decomposition of operation state.

The crystal structures of oP-C16, oP-C20, and oP-C24.

ABSTRACT We present an investigation of effect of alkyl chain length and hydroxyl group functionalisation on the tribological properties of imidazolium tetrafluoroborate ionic liquids. Four kinds of hydroxyl-functionalised ionic liquids with the alkyl chain of C1-C10 were synthesised, and the relationships between their structure and physicochemical properties such as viscosity, thermal stability and corrosion, before and after incorporation of a hydroxyl group, were measured. In particular, the studies on their lubrication properties as ionic liquid (IL) lubricants for steel-steel contacts, including the friction coefficient, the wear volume and so on, were particularly emphasised. Moreover, an in-depth exploration about the function mechanism and failure mode of the ILs during the friction process were studied using the scanning electron microscope and X-ray photoelectron spectroscope technologies, and a proposed interaction model between ILs and steel substrate was presented. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To improve the performance of traditional nucleation additives and their compatibility with other additives, as well as to solve complex problems in practical applications, SiO2 precursors were reactively grafted in situ with the symmetrical hydroxyl groups of the nucleation agents. With this approach, the dissymmetrical part of the nucleation agents plays an important role. As a result, the prepared composite additive—the nanoparticle precursor (NPP0)—produces excellent results. The NPP0-PP composite has a transparency of over 93%, haze lower than 15%, and a heat distortion temperature (HDT) of 142°C, which is more than the 110°C of the pure PP matrix. In addition, it maintains over 90% of the mechanical properties with a deformation coefficient less than 1%. This indicates a strong nanoeffect. [ABSTRACT FROM AUTHOR]

Background: Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science., Methods: This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers., Results: The classification accuracy for the healthy and insomnia subjects' spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects' spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes., Discussion: These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Liang, Belkacem, Song, Wang, Ai, Wang, Guo, Fan, Wang, Ji and Wang.)