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18,798 results on '"Xu, Zhang"'

298. Research Progress on End-Tidal Carbon Dioxide Detection Technology Based on Non-Dispersive Infrared Method

Author

Yanan LIU, Mingyue LI, Fuhao KANG, Lin HUANG, Yan HANG, Jilun YE, and Xu ZHANG

Subjects
gas detection, end-tidal carbon dioxide, non-dispersive infrared, technical progress, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5
Abstract

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal. Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice. With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology, its application in clinical practice in China has become increasingly widespread in recent years. The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle, gas sampling methods, key technologies, and technological progress of end-tidal carbon dioxide detection technology. It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

Published
2024
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299. Development of Wireless Wearable Sleep Monitoring System Based on EEG Signal

Author

Fuhao KANG, Jieying SHAN, Zexi LI, Yanan LIU, Jilun YE, Xu ZHANG, Chunsheng LIU, and Fan WANG

Subjects
wearable, eeg, sleep monitoring, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5
Abstract

A wireless wearable sleep monitoring system based on EEG signals is developed. The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display. The system is small in size, low in power consumption, and light in weight. It can be worn on the patient's forehead and is comfortable. It can be applied to home sleep monitoring scenarios and has good application value. The key performance indicators of the system are compared with the industry-related medical device measurement standards, and the measurement results are better than the special standards.

Published
2024
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300. Innovative Label Embedding for Food Safety Comment Classification: Fusion of Self-Semantic and Self-Knowledge Features

Author

Yiming Zhang, Haozheng Liu, Jiaming Feng, and Xu Zhang

Subjects
bert, label embedding, siamese network, pre-trained models, short text classification, food safety lead discovery., Technological innovations. Automation, HD45-45.2
Abstract

Food safety comment classification represents a specialized task within the realm of text classification. The objective is to efficiently identify a large volume of food safety comments, aiding relevant authorities in timely food analysis and safety alerts. Traditional methods typically employ one-hot encoding for label processing. However, in real-world situations, classified labels often convey valuable semantic information and guidance. This paper introduces an innovative approach to enhance the classification performance of food safety comments by embedding label information. Initially, we extracted generic sentiment pivot words from various classification labels as label description information. Subsequently, we employ a joint embedding approach to integrate this label description information into the text. This process will pool the expressions of the pivot word into the corresponding sentiment labels in the known domains after averaging to get the embedded expression. This aims to acquire highly detailed self-semantic feature vectors and self-knowledge feature vectors that are integrated with labeled descriptive information. Then, feed the semantic representation of comments and the word-embedded representation of labeled description information into a time-step-based multilayer Bi-LSTM and a step-based multilayer CNN, respectively. Ultimately, we concatenate these two feature vectors to facilitate matching, thereby fusing the self-semantic and self-knowledge features of labeled description information to train a classification model for food safety comments. Experimental results on the food safety comment dataset showcase a noteworthy improvement of 1.74% and 1.27% in Macro_Precision and Macro_F1 metrics, respectively, compared to BERT, BERT-RNN, and BERT-CNN. Through extensive ablation experiments and additional studies, our method effectively embeds labeling information, demonstrating a clear advantage over traditional methods in the task of classifying food safety comments. Doi: 10.28991/HIJ-2024-05-01-013 Full Text: PDF

Published
2024
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