Microwave power transmission (MPT) has been one of the most promising systems in the far-field wireless power transmission system. In MPT system, the harmonic energy generated by conventional diode rectifying circuit has great impact on the overall efficiency. Moreover, the passive impedance network leads to power reflection, which is the other factor leading to the overall efficiency decline. In this study, a 10 W output 100 MHz two-stage adaptive impedance-matching rectifying circuit is proposed which can make input impedance constant. About 100 MHz belongs to the band of radio frequency (RF) and the conception of microwave. The first-stage rectifier is modelled mathematically, which plays an essential role in constructing the initial input impedance and designing the control method of second-stage converter. By adjusting the duty cycle of the secondstage converter, the impedance of the whole rectifier realises 50 Ω under various input and the output conditions by means of simulations and experiments. The simulation and experimental results are shown to verify the effectiveness of the proposed RF to direct current converter and its control method. The overall efficiency with full load is up to nearly 65%.
Aspect-level sentiment classification aims to solve the problem, which is to judge the sentiment tendency of each aspect in a sentence with multiple aspects. Previous works mainly employed Long Short-Term Memory (LSTM) and Attention mechanisms to fuse information between aspects and sentences, or to improve large language models such as BERT to adapt aspect-level sentiment classification tasks. The former methods either did not integrate the interactive information of related aspects and sentences, or ignored the feature extraction of sentences. This paper proposes a novel multi-grained attention representation with ALBERT (MGAR-ALBERT). It can learn the representation that contains the relevant information of the sentence and the aspect, while integrating it into the process of sentence modeling with multi granularity, and finally get a comprehensive sentence representation. In Masked LM (MLM) task, in order to avoid the influence of aspect words being masked in the initial stage of the pre-training, the noise linear cosine decay is introduced into n − gram. We implemented a series of comparative experiments to verify the effectiveness of the method. The experimental results show that our model can achieve excellent results on Restaurant dataset with numerous number of parameters reduced, and it is not inferior to other models on Laptop dataset.INDEX TERMS Aspect-level sentiment classification, ALBERT, natural language processing, deep learning.
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.