Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases.Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated.Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups.Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.

With the development of power electronics technology, the capacity of power conversion devices is getting larger and larger, and the current level requirements for high-power IGBT are also getting higher and higher. A single device often cannot meet the requirements, and multiple IGBT need to be used in parallel. When IGBT is connected in parallel, due to the differences in device parameters, external circuit parameters and heat dissipation conditions, the current distribution is easily unbalanced, resulting in device performance waste or even damage. Therefore, the current sharing problem is the core problem to be solved in parallel connection of IGBT. In order to solve the problems such as difficulty in analyzing a single factor for coupling of various influencing factors, difficult control of temperature and high cost of circuit, this paper adopts Saber software simulation method to analyze the influence of various internal and external factors on IGBT’s dynamic and static current sharing, thus obtaining the relationship between these factors and corresponding countermeasures, and obtaining an effective IGBT parallel current sharing scheme. Finally, the correctness and effectiveness of this scheme are verified by experiments.

Human action recognition is a very popular field in computer vision research, and the research results are widely used in people's lives. This paper explores Kinect-based algorithm of human action recognition and applies it to the quality evaluation of cardiopulmonary resuscitation (CPR) operation. At present, the main means of CRP training is through physical auxiliary equipment, which has a large limitation and can only be carried out under specific conditions. CPR simulation training under general conditions can be effectively carried out by means of computer vision, which is a strategy worth popularizing. Using Kinect's powerful skeleton tracking capabilities to obtain key human skeleton data and then perform fine-grained human action analysis. Our model can obtain the critical compression depth (CCD) and compression frequency (CCF) of CPR. Compared with the-state-of-the-art, our algorithm has better stability and real-time performance. At the same time, our algorithm improves the time efficiency by about 60% while guaranteeing high accuracy. In addition, we guide the human body to perform standard movements by setting joint angle specifications.Moreover, our system has been proven to be valid by professional medical staff.