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| 1 | A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data显示文摘Although 12-lead electrocardiograms(ECGs)provide a wide range of spatiotemporal characteristics,interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets.Herein,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data.Our model was trained,validated,and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University.The experiments revealed that our approach had a classification accuracy of 94.41%in the classification task of seven types of rhythms,which was markedly superior to related single-lead and 12-lead ECG classification methods.Moreover,the average receiver operating characteristic area under the curve reached a value of 0.940,and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91,respectively,with specificity values of 0.996 and 0.994.By employing our boosting method,we were able to improve the accuracy to 94.85%.To investigate the performance degradation of the proposed neural network in some classes,an ECG cardiologist was enlisted to review questionable ECGs;this process provides a promising direction for network performance improvement.Therefore,the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’heart conditions based on real-time 12-lead ECG or grade their disease severity in advance. | LIU YunQing QIN ChengJin LIU JinLei JIN YanRui LI ZhiYuan ZHAO LiQun LIU ChengLiang | 2024 | Science China(Technological Sciences)2024，67，2： | 0 |
| 2 | Machinery fault diagnostic method based on numerical simulation driving partial transfer learning显示文摘Artificial intelligence(AI),which has recently gained popularity,is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines.The effectiveness of AI is influenced by the quality of the labeled training data.However,in engineering scenarios,available data on mechanical equipment are scarce,and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult.In response to the inadequacy of training samples,a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed.First,a suitable simulation model of critical components in a mechanical system is developed using the finite element method(FEM),and numerical simulation is performed to acquire FEM simulation samples containing different fault types.Second,several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network.Subsequently,the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance.Finally,the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation.The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory,achieving average classification accuracy of 99.54%and 99.64%,respectively.Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems. | LOU YunXia KUMAR Anil XIANG JiaWei | 2023 | Science China(Technological Sciences)2023，66，12： | 0 |
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