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This article investigates the voltage regulation of a dc–dc buck converter with the current constraint, which requires that the inductor current be held within a predetermined range to avoid hardware damage. Most of the existing methods face difficulties in maintaining satisfactory dynamic performance while complying with this current constraint. Meanwhile, the control performance of such a system is severely degraded by disturbances, especially mismatched disturbances, covering parameter uncertainties, input voltage fluctuations, and load resistance variations. For this reason, a robust finite-time control scheme is proposed by combining finite-time disturbance observer and finite-time control techniques. At the same time, a simple gain function-based strategy is introduced to restrict the inductor current in a specified region. Detailed finite-time stability of the complete closed-loop system driven by the recommended approach is guaranteed. Simulation and experimental results demonstrate that the newly designed control methodology outperforms the baseline control strategies in terms of the current constraint, dynamic performance, and disturbance rejection.

In the current research on brain-computer interface (BCI), the electroencephalography (EEG) signal is usually only represented by a 2-D matrix, and the installation position of the EEG electrodes and the correlation between them are not considered. Actually, the cerebral cortex is a continuous potential surface and the information collected directly from each electrode is influenced by the other electrodes, so direct use of the raw data results in information redundancy. This article converts the EEG signal into a graph, and then creates an information separator (IS) based on the Laplace matrix of the graph to obtain the independent source information of electrode nodes, and propose an IS-based area convolutional network (IS-ACN). Integrating the proposed IS with some advanced methods, the experimental results show that the incorporation of IS can enhance the performance of these methods. By observing and tracking samples with abnormal noise in the BCI competition IV data set 2a, it is demonstrated that the proposed method can greatly reduce the influence of noise and effectively obtain the source features of EEG signals with low signal-to-noise ratio, and the average accuracy and kappa coefficient of the proposed IS-ACN on this data set are 80.59% and 74.1%, respectively.

The parallel branches with independent optimized classification and localization capabilities are widely used in single-stage object detection. Defects such as feature conflicts, low level of information interaction, and empirical sample allocation scheme lead to weak spatial consistency of the outputs from different branches. In this work, we propose a Progressive Decoupled Task Alignment (PDTA) that enhances the information interaction between tasks while reducing the degree of feature coupling, and adopts a strategy based on sample screening and learning to achieve task alignment. First, we design the Discrepant Feature Decoupling Module (DFDM) embedded with the novel Oriented Decoupling Convolution (ODC) for the coupled features of the shared input, and the features extracted by ODC are utilized for disentanglement through the feed-in scheme with differences. Second, the Probabilistic Mapping Interaction Head (PMI-Head) utilizes the probabilistic mapping method to enhance task-specific semantics by information interaction. Finally, the network’s common attention to the content and position of the target is enhanced through the metric in the proposed Relevance-Guided Adaptive Task Alignment (RATA), in which an exponentially decaying manner is used to preserve the training samples that are more efficient for both tasks. During training, task-aligned learning is performed by Relevance-Guided Loss. Experiments on MS COCO and DIOR datasets demonstrate the effectiveness of our method, PDTA achieves better performance for object detection.

The electroencephalogram (EEG) signals acquired through scalp electrodes are susceptible to the volume conduction effect, which introduces a considerable degree of redundant information and consequently hinders decoding accuracy. In response to this challenge, we introduce a novel methodology that incorporates the Laplacian matrix of a graph to construct a source information extraction module, effectively mitigating the impact of volume conduction in electrodes. This innovation culminates in the development of the source information extract-based multiscale temporal fusion dynamic separable graph convolution network (SIE-TFDSGCN). The proposed method employs varying lengths of time convolution for fusion, thereby extracting temporal features inherent in EEG signals. Furthermore, a dynamic separable graph convolution (DSGCN) structure is introduced to extract spatial features, concurrently further reducing the influence of volume conduction. Performance evaluation on the BCI Competition IV 2a public dataset yielded notable results, with an average accuracy of 82.48% and a kappa value of 76.65. Comparative analysis against state-of-the-art classification methods underscores the significant enhancement in classification accuracy achieved by our approach. Expanding upon these findings, we leverage the developed methodology to design a brain-computer interface (BCI) smart home online control system. This system exhibits a high degree of reliability, enabling effective real-time control for smart homes. Our contributions mark a substantial advancement in EEG signal processing, with implications for both neuroscientific research and practical applications in smart home technology.

This brief addresses the trajectory tracking control of a category of n-link disturbed flexible joint robots (FJRs). First of all, two reduced-order generalized proportional integral observers (ROGPIOs) are constructed to estimate not only matched disturbances but also mismatched disturbances. Next on, with the assistance of estimations provided by the ROGPIOs, a newly robust tracking controller is recursively designed with the command filtered backstepping (CFBS) method. Meanwhile, second-order filters are employed to overcome the obstacle of “explosion of complexity” and an error compensation mechanism is developed to mitigate the adverse effect of filter errors on tracking performance. A rigorous theoretical analysis guarantees that the closed-loop system consisting of error compensation signals, estimation errors, compensated tracking errors, and tracking errors is uniformly ultimately bounded. Numerical tests on a two-link FJR are carried out to validate the viability of the presented strategy. The results confirm that the proposed control scheme has a solid ability to suppress both matched and mismatched disturbances.