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Large deployable mesh reflectors play a critical role in satellite communications, Earth observation, and deep-space exploration, offering high-gain antenna performance through precisely shaped reflective surfaces. Traditional dynamic modeling approaches—such as wave-based and finite element methods—often struggle to accurately capture the complex behavior of three-dimensional reflectors due to oversimplifications of cable members. To address these challenges, this paper proposes a novel spatial discretization framework that systematically decomposes cable member displacements into boundary-induced and internal components in a global Cartesian coordinate system. The framework derives a system of ordinary differential equations for each cable member by enforcing the Lagrange’s equations, capturing both longitudinal and transverse internal displacement of the cable member. Numerical simulations of a two-dimensional cable-network structure and a center-feed parabolic deployable mesh reflector with 101 nodes illustrate the improved accuracy of the proposed method in predicting vibration characteristics across a broad frequency range. Compared to standard finite element analysis, the proposed method more effectively identifies both low- and high-frequency modes and offers robust convergence and accurate prediction for both frequency and transient responses of the structure. This enhanced predictive capability underscores the significance of incorporating internal cable member displacements for reliable dynamic modeling of large deployable mesh reflectors, ultimately informing better design, control, and on-orbit performance of future space-based reflector systems. 

This dataset is collected from a lab scale gear system.  The experiments are conducted across 9 different fault conditions and three load conditions with 6 different sensor locations.  More details can be found in the word documentation. 

Multiple line outages that occur together show a variety of spatial patterns in the power transmission network. Some of these spatial patterns form network contingency motifs, which we define as the patterns of multiple outages that occur much more frequently than multiple outages chosen randomly from the network. We show that choosing N-k contingencies from these commonly occurring contingency motifs accounts for most of the probability of multiple initiating line outages. This result is demonstrated using historical outage data for two transmission systems. It enables N-k contingency lists that are much more efficient in accounting for the likely multiple initiating outages than exhaustive listing or random selection. The N-k contingency lists constructed from motifs can improve risk estimation in cascading outage simulations and help to confirm utility contingency selection. 

This paper introduces a simulation framework and a corresponding Robust Optimal Control (ROC) for docking Unmanned Underwater Vehicles (UUVs) that leverages Marine Renewable Energy (MRE) for improved autonomy in docking and charging operations. The proposed simulation framework integrates the dynamics of the Wave Energy Converter (WEC), docking station, and UUV within a unified system. Utilizing the WEC-Sim for the hydrodynamic modeling and MoorDyn for mooring dynamics, and in-house UUV dynamics in Simulink, the simulation effectively accounts for complex interactions under dynamic ocean conditions. The ROC docking controller, consisting of a Linear Quadratic Regulator (LQR) and a Sliding Mode Control (SMC), is designed to enhance robustness against environmental disturbances and system uncertainties. This controller utilizes input-output linearization to transform the nonlinear dynamics into a manageable linear form, optimizing docking performance while compensating for disturbances and uncertainties. The combined simulation and control approach is validated under various ocean conditions, demonstrating effective docking precision and energy efficiency. This work lays a foundational platform for future advancements in autonomous marine operations for UUV docking systems integrated with WEC. In addition, this work also demonstrates the feasibility of using MRE to significantly extend the operational duration of UUVs; such a platform will be leveraged for further development of structural health monitoring and fault diagnosis techniques for offshore structures such as WECs and Floating Offshore Wind Turbines. 

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%–5% accuracy increase, indicating its robustness in performance enhancement.