A novel method for the estimation of the temperature distribution in a rapid thermal processing (RTP) system is developed in this paper. The proposed method uses a proper orthogonal decomposition algorithm to extract the dominant modes of the temperature distribution and a reduced-order model is obtained. Then a reduced-order sliding mode observer is developed to capture the dynamics of the dominant modes. The estimated dynamics of the dominant modes can be used to re-construct the temperature distribution. It is proved that the estimation error would be drawn into a small boundary rapidly. Test results confirm the effectiveness of the proposed observer for the RTP system.

The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a loss function that follows both the physical model constraints and the vibration modal conditions, we identify optimal input-shaping parameters to minimize residual vibration. With the use of powerful computational resources to handle multimode information about the vibration, the PINN-based approach outperforms traditional input-shaping methods in terms of computational efficiency and performance. Extensive simulations are carried out to validate the effectiveness of the method and highlight its potential for complex control tasks in flexible robotic systems.

Complex physical processes, which could evolve in both spatial and temporal dimensions and be represented by partial difference equations, could also operate in a repetitive mode with iterative learning methods as suitable control laws. For these three-dimensional systems (of the spatial, temporal, and iterative dimensions), the stability in the iterative direction is critical for many applications, which can be analyzed and synthesized under the proposed concept of iterative dissipativity. The definition of iterative dissipativity, which is first introduced in this paper, encapsulates the dominant information in both the spatial and temporal dimensions, while also placing a particular emphasis on the iteration improvement. This property allows for the derivation of sufficient conditions for asymptotic stability in the iteration direction, which are represented by linear matrix inequality criteria that can be readily solved. Performance in both the spatial and temporal dimensions can also be satisfied under this iterative dissipativity concept, even in absence of real-time feedback. Moreover, the optimization solutions of the control parameters can be determined. Finally, a thermal process and a numeric example are presented to illustrate the effectiveness of the proposed iteratively dissipative learning control approach.

Fueled by concurrent advances in sensing and computing, braincomputer interfaces are evolving from single-electroencephalography modalities toward generalized fusion systems that synergistically integrate electrical, hemodynamic, optical, magnetic, and peripheral physiological signals. Flexible dry-electrode arrays and optically pumped magnetometers now deliver high spatio-temporal resolution within everyday wearable form factors; cross-modal attention and graph-network approaches unify millisecond-scale electrophysiology with second-scale hemodynamics at the feature level; decision-level and deeplearning frameworks further enable adaptive modality weighting and robust transfer. Building on these advances, we propose the concept of generalized fusion, emphasizing dynamic synergy among heterogeneous signals across the entire encodingdecodingfeedback loop, and discuss the emerging role of generative AI in completing missing modalities, balancing signal-to-noise trade-offs, and expanding demographic coverage. Finally, we summarize the distinct demands of medical rehabilitation, humancomputer interaction, and intelligent control scenarios on fusion accuracy and user load, highlighting that hardware comfort, algorithmic real-time capability, and multidimensional evaluation must be continually refined to translate multimodal braincomputer interfaces from laboratory validation into real-world deployment.

A low-dimensional non-linear observer is developed in this paper for the estimation of the thermal distribution in a rapid thermal processing (RTP) process. Based on the reduced model obtained from Galerkin's method, a low-dimensional non-linear Luenberger observer is designed to re-construct the thermal distribution from the output data of the point temperature sensors. Test results in a RTP system show fast convergence speed and small estimated errors of the proposed observer.

Solving the EEG forward problem is essential for EEG source localization but remains computationally expensive for realistic head models when using conventional numerical methods such as the Boundary Element Method. In this paper, a Boundary Potential-Informed Neural Network (BPINN) framework is proposed to efficiently solve the boundary electric potential. The proposed framework embeds boundary potential constraints into the loss function of the neural network to ensure physical consistency and introduces a solid-angle-based geometric transformation layer to extract spatial geometric features and reduce learning complexity. The effectiveness of the proposed framework is validated on both a single-sphere conductor with an analytical solution and a realistic head model with complex geometry. Numerical results demonstrate high accuracy, good generalization, and significant acceleration through transfer learning when system configurations vary. These results indicate that the proposed BPINN provides an efficient and flexible alternative for EEG forward problem.