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Current fiber-optic distributed acoustic sensor (DAS) target recognition technologies continue to prioritize updating the feature learning tools while neglecting varying contributions of information from different dimensions. Few works focus on designing recognition algorithms from the perspective of multi-dimensional information extraction and utilization. In this paper, we introduce an end-to-end three-dimensional attention-assisted convolutional neural network (3-D ACNN) into DAS for the first time. This novel approach allows for automatic and concurrent extracttion of three-dimensional input in time, frequency, and space, aiming to cooperatively recognize sensing targets with higher accuracy. Our new scheme also includes comparative examination of 2-D and 3-D attention mechanisms to leverage the most efficient information in various dimensions, thereby enhancing recognition accuracy while maintaining computation efficiency. Field tests demonstrate that the 3-D CNN outperforms commonly used 2-D networks with only time-frequency (T-F), time-space (T-S), or space-frequency (S-F) inputs, improving recognition accuracy from 95.47% to 98.67%. Moreover, the proposed 3-D ACNN can further improve accuracy up to 99.33%, outperforming the basic 3-D CNN. It is also found that T-F information in DAS is richer and more recognized than T-S and S-F information. The use of 3-D attention yields better results than 2-D attention. Additionally, the processing time required for each spatial sensing point is only 0.22 ms on a commercial GPU (NVIDIA GeForce (R) GTX 1080 Ti), which is approximately 1/171 of the time required for the equivalent 2-D network with the same T-F input (38.52 ms). The 3-D ACNN enabled smart fiber-optic DAS (sDAS) represents a significant breakthrough, enabling fast and accurate multi-dimensional collaborative recognition. This advancement is expected to have a substantial impact for distributed sensing applications.

This paper is concerned with the port-Hamiltonian modeling of a Timoshenko beam subject geometric nonlinearities through von Kármán strains, material nonlinearity considering hyperelasticity with the assumption of neo-Hookean or Mooney-Rivlin material, in addition to the incompressible deformation constraint that corresponds to the preservation of volume. The model is suitable for representing the behavior of rubber like beams within the range of moderate deformations and rotations. Numerical simulations are carried out to illustrate the accuracy of the proposed model.

This paper is concerned with the modeling and control of a curling Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator using the port-Hamiltonian (PH) approach. For that purpose, we use a modular approach and consider the HASEL actuator as an interconnection of elementary subsystems. Each subsystem is modeled by an electrical component consisting of a capacitor in parallel with an inductor connected through the conservation of volume of the moving liquid to a mechanical structure based on inertia, linear, and torsional springs. The parameters are then identified, and the model is validated on the experimental setup. Position control is achieved by using Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) with integral action (IA) for disturbance rejection. Simulation results show the Efficiency of the proposed controller.