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OBJECTIVE: The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis. METHODS: Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the "BiLSTM+Dilated Convolution+3D Attention+CRF" deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular "Bert+BiLSTM+CRF" approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases. RESULTS: The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy. CONCLUSION: Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.
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The COVID-19 has become a global pandemic that dramatically impacted human lives and economic activities. Due to the high risk of getting affected in high-density population areas and the implementation of national emergency measures under the COVID-19 pandemic, both travel and transportation among cities become difficult for engineers and equipment. Consequently, the costly physical commissioning of a new manufacturing system is greatly hindered. As an emerging technology, digital twins can achieve semi-physical simulation to avoid the vast cost of physical commissioning of the manufacturing system. Therefore, this paper proposes a digital twins-based remote semi-physical commissioning (DT-RSPC) approach for open architecture flow-type smart manufacturing systems. A digital twin system is developed to enable the remote semi-physical commissioning. The proposed approach is validated through a case study of digital twins-based remote semi-physical commissioning of a smartphone assembly line. The results showed that combining the open architecture design paradigm with the proposed digital twins-based approach makes the commissioning of a new flow-type smart manufacturing system more sustainable.
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[This corrects the article DOI: 10.1016/j.jclepro.2021.127278.].
RESUMO
Additive manufacturing (AM), also known as 3D printing, is a new manufacturing trend showing promising progress over time in the era of Industry 4.0. So far, various research has been done for increasing the reliability and productivity of a 3D printing process. In this regard, reviewing the existing concepts and forwarding novel research directions are important. This paper reviews and summarizes the process flow, technologies, configurations, and monitoring of AM. It started with the general AM process flow, followed by the definitions and the working principles of various AM technologies and the possible AM configurations, such as traditional and robot-assisted AM. Then, defect detection, fault diagnosis, and open-loop and closed-loop control systems in AM are discussed. It is noted that introducing robots into the assisting mechanism of AM increases the reliability and productivity of the manufacturing process. Moreover, integrating machine learning and conventional control algorithms ensures a closed-loop control in AM, a promising control strategy. Lastly, the paper addresses the challenges and future trends.