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In fluid mechanics research, understanding the motion behavior of particles in a flow field is crucial for comprehending particle transport, mixing, and deposition processes. However, due to the complex interactions between particles and fluids, a single method is insufficient to accurately describe the particle motion. To tackle this problem, this study proposes an unsupervised heterogeneous domain adaptation method based on fuzzy principles, called Particle Flow Motion Analysis (PMFA). First, the data originating from both the source and target domains are preprocessed and subjected to Principal Component Analysis (PCA). Then, fuzzy rules are introduced for feature selection. Finally, the Maximum Mean Discrepancy (MMD) and Canonical Correlation Analysis (CCA) algorithms are employed to optimize the distribution disparities and correlations between the heterogeneous domains. By constructing domain adaptation tasks and comparing with five other methods, the performance of the proposed method is evaluated. The results demonstrate that the PFMA achieves an average accuracy of 89.44%, an average recall rate of 85.87%, and an $F$1 value of 89.26% across four tasks, outperforming the other five comparative methods. The proposed method holds significant importance in gaining in-depth understanding of particle motion phenomena in fluids and revealing the underlying physical mechanisms and patterns.

In recent years, machine learning methods based on epileptic signals have shown good results with brain-computer interfaces (BCIs). With the continuous expansion of their applications, the demand for labeled epileptic signals is increasing. For a large number of data-driven models, such signals are not suitable, as they extend the calibration cycle. Therefore, a new domain-adaptive TSK fuzzy system model based on multisource data fusion (DA-TSK) is proposed. The purpose of DA-TSK is to maintain high classification performance when the amount of labeled data is insufficient. The DA-TSK model not only has a strong learning ability to learn characteristic information from EEG data but is also interpretable, which aids in the understanding of the analytic process of the model for medical purposes. In particular, this model can make full use of a small amount of labeled EEG data in the source domain and target domain through domain adaptation. Therefore, the DA-TSK model can reduce data dependence to a certain extent and improve the generalization performance of the target classifier. Experiments are performed to evaluate the effectiveness of the DA-TSK model on public EEG datasets based on epileptic signals. The DA-TSK model can obtain satisfactory accuracy when the labeled data are insufficient in the target domain.