ABSTRACT Coronary heart disease (CHD) remains prevalent worldwide, and accurate diagnosis is crucial for subsequent medical intervention. This study proposes a multimodal fusion model (SRNXFM), based on Squeeze‐and‐Excitation ResNet with Next (SEResNeXt), utilizing 12‐lead electrocardiogram (ECG) and actual clinical data to facilitate early diagnosis of CHD. This study is divided into three sections: research on the clinical data model, the ECG model and the multimodal fusion model. For the clinical data model, five machine learning algorithms and four deep learning algorithms were used to analyse this study. For the ECG model, this study proposes a SEResNeXt‐LSTM framework that inputs the features extracted by SEResNeXt into LSTM for the diagnosis of CHD. For the multimodal fusion model, this study proposes the SRNXFM model, a dual‐input framework based on feature fusion. It integrates ECG and clinical data from the same patient to diagnose CHD. Experimental results demonstrate that the model achieved an AUC value of 0.9485, significantly better than the values based solely on clinical data (best AUC: 0.8493) or ECG data (best AUC: 0.8581). Furthermore, the model attained an overall accuracy of 0.9529, repreenting improvements of 10.76% and 12.98% over clinical data (0.8603) and ECG (0.8434) models, respectively. It also outperformed the single‐modality approaches across other key metrics, including precision (0.9001), recall (0.9978) and F1‐score (0.9311). To improve the interpretability of proposed model, the contribution ranking of risk factor characteristics is also studied. This provides valuable insights into the prevention and treatment of CHD and supports clinicians in making accurate diagnoses.

As the main device of aircraft electric propulsion system, the voltage stabilization control of dc-dc converter is crucial to ensure flight safety and energy efficiency. Passivity-Based Control (PBC) relies on exact models. Physics-Informed Neural Network (PINN) lacks stability guarantees. To address these issues, this paper proposed a PINN-PBC algorithm. The algorithm takes the closed-loop dynamics equation of PBC as the explicit constraint term of PINN to construct an end-to-end control architecture. It is able to improve the dynamic response performance while ensuring the large-signal stability of the system. The results show that the output voltage fluctuation is ±1.7% when a large disturbance occurs in the CPL.

This article concentrates on the event-based collaborative design for strict-feedback systems with uncertain nonlinearities. The controller is designed based on neural network (NN) weights adaptive law. The controller and NN weights adaptive law are only updated at the triggering instants determined by a novel composite triggering threshold. Considering the conservativeness of event condition, the state-model error is integrated into constructing the composite condition and NN weights adaptive law. In the context of the proposed mechanism, the requirements of system information and the allowable range of event-triggering error are relaxed. The number of triggering instants is greatly reduced without deteriorating the system performance. Moreover, the stability of the closed-loop is proved by the Lyapunov method following time-interval and sampling instants. Simulation results show the effectiveness of the scheme proposed in this article.

At present, a lot of research work has been carried out to develop more complex MPPT algorithms. The computational burden, validity range and convergence speed of these algorithms are completely different and depend on the theoretical method used. In order to overcome the above limitations, this article aims to study the effectiveness of the MPPT method based on Swarm intelligence and predictive control. This work has studied the benefits of the novel implementation of the global MPPT algorithm through detailed analysis based on numerical simulations and highlighted its effectiveness and applicability when applied to photovoltaic systems in actual changing environments. The purpose of this method is to provide no information about the solar irradiance distribution and temperature on the components and when to provide the information. Under the circumstances, the maximum power of the photovoltaic system can be tracked quickly and accurately.