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High-quality data is one of the key requirements for any engineering application. In earthquake engineering practice, accurate data is pivotal in predicting the response of structure or damage detection process in an Structural Health Monitoring (SHM) application with less uncertainty. However, obtaining high-resolution data is fraught with challenges, such as significant costs, extensive data channels, and substantial storage requirements. To address these challenges, this study employs super-resolution generative adversarial networks (SRGANs) to improve the resolution of time-history data such as the data obtained by a sensor network in an SHM application, marking the first application of SRGANs in earthquake engineering domain. The time-series data are transformed into RGB values, converting raw data into images. SRGANs are then utilized to upscale these low-resolution images, thereby enhancing the overall sensor resolution. This methodology not only offers potential reductions in data storage requirements but also simplifies the sensor network, which could result in lower installation and maintenance costs. The proposed SRGAN method is rigorously evaluated using real seismic data, and its performance is compared with traditional enhancement techniques. The findings of this study pave the way for cost-effective and efficient improvements in the resolution of sensors used in SHM systems, with promising implications for the safety and sustainability of infrastructures worldwide.

The suitability of the QDL method for analyzing the performance of ac power systems has been evaluated by application to a microgrid. The QDL method is based on a combination of Quantized State Systems (QSS) methods and the Latency Insertion Method (LIM). The accuracy and computational intensity of QDL simulations were evaluated relative to an industry-standard reference method. The key advantages expected of the QDL approach -- including high computational efficiency when the system is operating in steady-state and, when not in steady-state, the need to update only those states that have been affected by quantum level changes of connected states. The expected advantages were largely realized, but with some complications that remain unresolved and require further research such as limit cycle oscillations that emerge in some states after disturbances that should have returned to stationarity. Also, the method in its current state may not be feasible for fault analysis because of the low computational efficiency that results when large disturbances (e.g. faults) create large excursions simultaneously in many system states. The relative strengths and weaknesses of the method are discussed, and some improvements to the method are proposed to overcome the weaknesses. The revelation of observed problems is intended to inspire additional research to overcome those problems.

The Linear Implicit Quantized State System (LIQSS) method has been evaluated for suitability in modeling and simulation of long-duration mission profiles of Naval power systems which are typically characterized by stiff, nonlinear, differential algebraic equations. A reference electromechanical system consists of an electric machine connected to a torque source on the shaft end and to an electric grid at its electrical terminals. The system is highly non-linear and has widely varying rate constants; at a typical steady state operating point, the electrical and electromechanical time constants differ by three orders of magnitude being 3.2 ms and 2.7 s respectively. Two important characteristics of the simulation, accuracy, and computational intensity both depend on the quantization size of the system state variables. At a quantization size of about 1 percent of a maximum value of a variable, results from the LIQSS1 method differed by less than 1 percent from results computed by well-known continuous system state space methods. The computational efficiency of the LIQSS1 method increased logarithmically with increasing quantization size, without significant loss of accuracy, up to some particular quantization size, beyond which the error increased rapidly. For the particular system under study, a sweet spot was found at a particular quantum size that yielded both high computational efficiency and good accuracy., Comment: 11 pages