Electroencephalogram (EEG)-based emotion identification enables accurate emotional interaction in brain-computer fusion by decoding brain signals, thereby enhancing the intelligence of human-computer collaboration. Data augmentation (DA) techniques offer a promising solution to the challenge of data scarcity in emotion identification. However, traditional DA methods often overlook the physiological mechanisms underlying EEG data, limiting their effectiveness and constraining the performance of emotion classification. To address this, a DA model based on human nerve conduction mechanisms (NCMs), named the gustatory-emotion coupling model and multiblock attention module (GECM-MBAM), is proposed to improve the performance of emotion identification. First, the 1/ f characteristics and synchronization of brain responses are reproduced in the GECM output when stimulated by EEG. The bionic performance of the model in EEG processing is validated, demonstrating brain-like perception of EEG signals via the GECM. Second, the MBAM is designed based on the characteristics of the GECM output, facilitating data augmentation of emotion-related EEG. Comparative experiments demonstrate that GECM-MBAM remarkably outperforms multiple existing DA models in recognition accuracy ( p < 0.05), confirming its effectiveness and superiority in EEG data augmentation. Finally, when compared with state-of-the-art algorithms and in ablation studies, GECM-MBAM demonstrates superior performance in emotion recognition. Specifically, GECM-MBAM attains accuracies of 96.91 % and 94.52 %, recalls of 96.23 % and 93.86 %, and kappa coefficients of 95.45 % and 94.29 % on the SEED and SEED-IV datasets, respectively. In conclusion, the performance of emotion identification is improved using the GECM-MBAM, offering a novel bionic processing approach for affective computing.

To construct an accurate crash prediction model, the road safety performance function (SPF), which provides a safety guide for the management department, is often used. In traditional parametric SPFs, the importance of traffic features is calculated using analytic expression, but the model is inaccurate and low in generalization. This article proposes a machine learning-based method to replace parametric SPFs, this framework is built based on integrated visual feature importance, global model training, and a structure self-organizing scheme. From the analysis, this model can not only predict multiregional car crashes accurately but can also provide a feature importance and selection guide for the management department to better understand it. At last, experiments using real-world data collected from Highway 401 Ontario Canada and several highways in the U.S. show that the proposed framework outperformed other State-of-the-Art models in terms of interpretability, accuracy, generalizability, and model conciseness.

The rapid development of communication transmission, including 6G technology, is creating increasing challenges for real-world object recognition tasks in transportation, which now must operate within complex external environments and the requirement of time efficiency. Although machine learning-based hybrid intelligence has attracted significant attention and achieved much success in recent years, the current models are often ineffective and have poor generalization in extreme weather. This is because the training of a deep learning model is often uncourteous, meaning that the models can easily fail, even during the feature extraction step. An adaptive hybrid attention-based convolutional neural network (AHA-CNN) framework is proposed in this paper to address these shortcomings. First, fuzzy c-means and maximum entropy algorithms are utilized for image feature pre-extraction. A heuristic search-based adaptive attention mechanism is then presented, which adaptively combines the previously extracted features and generates fused images. By applying this mechanism, the key areas of an image are reinforced in a more intelligent and interpretable way, and less important areas are ignored. The processed images are then transferred into a modified region-CNN for further training. Finally, four real-world experiments on traffic sign detection, vehicle license plate recognition, road surface condition monitoring, and pavement disease detection are carried out. Results show that the proposed framework has high testing accuracy compared with other existing methods. The features fused with the cognition mechanism are also easier to interpret.

Operational optimal control (OOC) is an essential component of wastewater treatment process (WWTP). The control variables usually are high-dimensional, nonlinear, and strongly coupled, which can easily fail traditional optimization control methods. Mathematically, these operational variables usually are in the unknown low-dimensional space embedded in the high-dimensional space. Therefore, the OOC problem of WWTP can be resolved as an optimization challenge involving low-dimensional space, and the unknown low-dimensional space is presented in the form of a set of controlled variables in a high-dimensional space, which is normal in real-world industries. Here, a dimension-reducible data-driven optimization control framework for WWTP is proposed. Considering the difficulty in elucidating the whole space of set points, a neural network is designed to approximate the constraint relationship between control variables. The search process is based on optimization methods in low-dimensional space embedded into Euclidean spaces. Furthermore, the convergence of the process is ensured via mathematical analysis. Finally, the experimental simulation of wastewater treatment revealed that this approach is effective for an optimal solution in control systems.

There are few results concerning consensus of multiagent systems (MASs) based on partial differential equations (PDEs), and the problem of how to act boundary control based on distributed measurement on spatial boundary points of MASs has not been solved. This paper addresses boundary control based on distributed measurement for output consensus of leader-following directed MASs modeled by parabolic PDEs. First, a boundary controller acting on spatial boundary points is designed by considering the delivered information produced by agents communicating with neighborhoods. Without considering input constraint, the Lyapunov's direct method is used to obtain a sufficient condition on the existence of the boundary controller to achieve output consensus. The condition is expressed as a form of the feasibility of LMIs. After that, the whole input constraint for MASs is given. And then, one more condition on control gains is obtained to ensure the existence of the boundary controller with input constraint. Finally, one numerical example with two cases illustrates the theoretical analysis results.

This paper deals with the problem of complex spatio-temporal networks, which is modeled by coupled partial integro-differential equations (PIDEs). A spatial proportional-integral-derivative (SPID) state-feedback controller is studied. With Laypunov direct method, a sufficient condition on synchronization of the complex PIDE network is investigated in terms of linear matrix inequality (LMIs). Finally, a numerical example shows the effectiveness of the proposed results.