Multimodal peripheral physiological signal fusion for emotion recognition seeks to perceive or recognize human emotions using peripheral modalities such as electromyography, electrodermal activity, and respiratory wave. Previous approaches to multimodal fusion primarily focus on emotion-sensitive signals such as electroencephalogram (EEG), often overlooking the potential value of peripheral physiological signals in emotion recognition. Moreover, the inherent heterogeneity among different modalities continues to pose challenges to fusion quality. In this article, we propose a multimodal decoupled multimodal fusion (DMF) to address these issues, enabling flexible feature decoupling, cross-modal feature interaction, and relational knowledge learning. Specifically, each modality’s representation is first decoupled into two components: common features and modality-specific features; second, the DMF employs progressive cross attention to facilitate the exchange of modality-specific features across different modalities; and finally, it uses relational knowledge to learn multimodal spliced features, embedding both inter-modal and intra-modal feature relationships. DMF offers a dynamic multimodal emotion recognition framework that leverages the emotional information contained in diverse modalities. Experimental results demonstrate that the DMF method consistently outperforms previous approaches and provides a viable solution for multimodal peripheral physiological signal fusion.

The prediction of remaining useful life (RUL) for aircraft engines stands as a critical process ensuring the safe operation of aircraft. Traditional methods for estimating RUL primarily mine temporal features within data, frequently overlooking the complex interdependencies among multivariate signals. Furthermore, in actual physical systems such as aircraft engines, there is a dynamic linkage among the sensor data. Ignoring these subtleties can result in a reduction of estimation accuracy. Addressing this challenge, the article proposes a novel graph attention reconcile network integrated with priori knowledge to predict aircraft engine RUL. It constructs a time-series graph by fusing sensor data with aerothermodynamics equations. The network employs dual attention mechanisms to capture spatio-temporal features: a spatial attention module to identify relationships between sensors, and a temporal attention module to highlight the importance of different time steps. Additionally, a LSTM layer is incorporated to enhance RUL prediction accuracy. Comparative analysis using the Commercial Modular Aero-Propulsion System Simulation datasets and new Commercial Modular Aero-Propulsion System Simulation datasets demonstrates the superior performance of the proposed model over other prevailing approaches.

With the wide application of deep learning in condition monitored system prognostics, its inadequate interpretability has always been questioned. This article proposes interpretable remaining useful life (RUL) estimation routine for RUL prediction, which consists of an augmenter network based on ordinary differential equations and an estimator network utilizing feature-temporal attention. The augmenter is implemented to suppress additive noise in original data and infer unobservable health-related variables with embedded formulas. Subsequently, the uncertainty-aware estimator is executed to predict RUL with quantile regressive module, while detecting dominant features and temporal dependencies via attention. Extensive evaluations are carried out on the N-CMAPSS aeroengine dataset. Compared with the baseline approaches, our method obtains 14% improvement for NASA's score and 7% for root-mean-square error. Moreover, the interpretability of our framework is further analyzed, in which the proposed method is able to imply physical constraints, detect abnormal subsystems and identify critical stages of flight with insufficient prior knowledge.

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

In this article, classification problems for a class of tensor datasets with a noisy environment are investigated. To address such issues, a novel fuzzy support tensor machine (FSTM) dual model with robustness is established. First, for each input sample in the noisy tensor dataset, we define three kinds of fuzzy membership functions, such as linear, cosine, and exponential forms. In particular, the reconstruction process from one-dimensional (1-D) vector data to third-order tensor data is also derived in the Appendix. Second, the original optimization model of an FSTM on fuzzy membership is designed by constructing the vector pattern of the traditional support vector machine (SVM) models into a tensor pattern. Next, by introducing the Lagrangian multiplier method and tensor-Tucker decomposition method to the original FSTM model, an FSTM dual model without tensor inner product operation is obtained for the first time. Such a dual model with tensor-Tucker decomposition form can avoid conservativeness caused by the vectorization of tensor data in the traditional SVM model. Furthermore, an FSTM classifier is derived by the designed numerical algorithm, and the classification generalization error bound of the FSTM model with a general form is developed. It is worth noting that a linear least-squares FSTM (LLS-FSTM) equation with tensor-Tucker decomposition is also designed in the Appendix to further reduce the slightly time-consuming problem of the solving the dual optimization model FSTM. Finally, two numerical examples are presented to verify the feasibility and validity of the derived FSTM classifier.

In this article, a novel adaptive dynamic programming (ADP) approach is proposed for the optimal control problem of nonlinear continuous control systems with unknown dynamics. First, an alternating iteration algorithm based on Hamilton-Jacobi-Bellman equation is proposed for the optimal control of known nonlinear control systems. Then, the convergence results of the alternating iteration algorithm are obtained by using mathematical induction and monotone bounded convergence theorem. Moreover, the global asymptotic stability of the nonlinear closed-loop system is proved. Second, based on the scheme of alternating iteration algorithm, an ADP algorithm for the optimal control problem with unknown nonlinear dynamic model is developed by using the basis function approximation method and Newton-Leibniz formula, which can update the control strategy online by utilizing input and output information of the system. In addition, the convergence analysis of the proposed ADP algorithm is derived. Finally, the feasibility of the established results is verified by two examples, and the ADP method is applied to the optimal tracking fuel control problem of turbofan engines.

This article considers the problem of event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input. First, an event-triggered condition is given to make the closed-loop switched system asymptotically stable. Second, a novel method, event-triggered heuristic dynamic programming (ETHDP), is applied to derive the optimal control policy. Two neural networks (NNs) are utilized to approximate the value function and control law, respectively. When the event-triggered condition is violated, the weights of the two NNs are updated, which can decrease the networks calculation and transmission load notably. A proof of the convergence of the ETHDP is also carried out. Finally, the effectiveness of the proposed method is verified by an example.