Classification of coronary artery stenosis is essential in assisting physicians in diagnosing cardiovascular diseases. However, due to the complexity of medical diagnosis and the confidentiality of medical images, it is difficult to obtain many coronary artery stenosis image samples for scientific research in general. In addition, the degree, location, and morphology of coronary artery stenosis in different patients, as well as the noise in computed tomography angiography (CTA) images, make it challenging to extract typing features effectively. To address the above problems, first, a joint segmentation method is proposed based on maximum between-class variance and region growing to extract key regions from CTA images to facilitate further feature extraction. Then, a coronary artery stenosis classification model based on convolutional block attention module (CBAM) and transfer learning is constructed, which can effectively improve the model training effect under the insufficient samples. Finally, a dataset of CTA images from actual patients is applied for experimental verification. The experimental results show that the accuracy (Acc) of the proposed method is up to 98.99%, which is greatly improved compared with several machine learning algorithms and neural network methods. It can be concluded that the accuracy of coronary stenosis classification can be considerably improved, and a reasonable scientific basis is provided for clinical medical diagnosis.

In this paper, a state estimation method is proposed based on the multi-rate asynchronous sensors fusion with missing measurements for high-speed train. Firstly, considering the actual operating environment, a multi-rate asynchronous speed measurement state space model is established. Then, a maximum likelihood evaluation criterion is constructed, and the multiple imputation fusion is proposed for the recovery of intermittent and continuous missing data. Finally, based on the improved adaptive attenuation particle filter-unscented Kalman filter (PF-UKF) and the matrix weighted fusion, the global estimation is fused by linear-weighted summation of the local estimations of train state. The simulation results show that the train state can be accurately fused and estimated by the proposed method while ensuring real-time performance, whether the monitoring data is intermittent missing or continuous missing, and the effectiveness and feasibility of the method can be verified.

By analyzing the mechanism of pure air emergency brake for high-speed train, the discrete emergency brake model is established. Aiming at the problem that time-varying hidden parameters cannot be observed directly, the sliding window-based expectation maximization is proposed, and the unobserved time-varying brake parameters are identified. Firstly, the position and size of the sliding window are selected; then, the sliding window-based expectation maximization is used for brake parameter identification; finally, combined with the gradient optimization, the optimal identifications of emergency brake parameters are obtained. The simulation results show that the brake parameters can be identified quickly and accurately by the proposed method. Under uniform noise, the identification errors of friction coefficient and braking ratio are ±0.0068 and ±0.0349, respectively, and the maximum relative errors between the identifications and true values are 2.4807% and 1.3154%, respectively, which can meet the actual requirements of the brake system. The effectiveness and practicability of the proposed model and method are verified.

In this article, a condition monitoring approach is proposed based on vibration signal, aiming at improving the adaptability of feature extraction and the accuracy of classification. First, the original vibration signal acquired under certain working condition is preprocessed by dividing it into multiple segments, followed by the signal decomposition. Then, the features of each decomposed signal are extracted based on the theory of diversity entropy (DE). Two parameters in the DE are optimized considering the fact that these parameters are crucial for the classification result. The optimization objective is to make the different segments of the signal collected in the same working condition have approximate feature characterization. By this means, the feature of the signal is captured adaptively and accurately using the optimized entropy value. Finally, the support vector machine is used to identify the extracted feature vectors to realize condition classification. The experiments on three representative platforms, including a crystal lifting-rotation system in our laboratory, are conducted to verify the effectiveness of the proposed method.

In this paper, we propose an output feedback flight tracking control scheme for helicopter attitude and altitude systems with unmeasured states under full state constraints. Firstly, a state observer is constructed based on the measured output signals, which is proven to be rigorous since all states are constrained within the desired and assigned scopes. Secondly, the flight tracking controller is built using the state estimations with the full state constraints control method. Then, the Barrier Lyapunov function method is adopted to guarantee the stability of the composite closed-loop nonlinear error systems. Meanwhile, the linear matrix inequality technology is applied to calculate the gains of the state observer. Finally, a numerical simulation example is provided to confirm the reasonableness of the full state constraint output feedback flight tracking control method.

To solve the particle degradation and sample impoverishment, a maximum correntropy-based extended particle filter method is proposed by optimizing the important density function. In the proposed method, the importance sampling is guided by the measurement information, the entropy criterion and high-order moments information of the signal are fully utilized, and the non-Gaussian heavy-tailed distribution noise and the noise with outliers are effectively suppressed. The simulation results demonstrate the effectiveness and feasibility of the filtering method.