Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. The simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. Therefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for efficient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. The experimental results show that the proposed model can significantly improve the accuracy and operational efficiency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its effectiveness and advancement.

The analysis and structural transformation of power-related picture elements is an essential result of regional power grid research. This paper proposes a new idea for extracting monolithic insulator images based on analysing the characteristics of scanned colour grid power insulators. At the same time, the article extracts the RGB colour matrix of the insulator based on the BP neural network algorithm. Then, it uses it as a characteristic parameter for training and analysis. Combining the characteristics of image data, it is found that the model proposed in this paper enhances the ability to express images, thereby improving the accuracy of image classification. Furthermore, many experiments on the accurate data set of insulator monoliths show the effectiveness of this model.

This article designs a dynamic thermal emitter based on phase change material VO ₂ . This structure avoids emission loss at low temperatures and increases heat dissipation at high temperatures.

In this work, we reported a wireless network composed of silver film-based graphene oxide-fluorescence resonance energy transfer (GO-FRET) lysozyme aptasensor nodes. At the sensor node level, we optimized silver substrate structure, concentrations of the aptamers, and graphene oxide and tested lysozyme detection performance with a model analyte. At the network level, we analyzed the complexity and transmission success rate using fractal measurements. We implemented the wireless network composed of the aptasensor with a portable Wi-Fi fluorescent reader. Transmission success rate testing results show that an increase in node hops can promote the rate of transmission success dramatically. When the hop count is larger than 6, the rate of transmission success can reach more than 90% if the transmission failure probability and sleep probability are 0.1 and 0.5, respectively.