This work presents a methodology to optimize the selection of multiple parameter levels of an image acquisition, degradation, or post-processing process applied to stimuli intended to be used in a subjective image or video quality assessment (QA) study. It is known that processing parameters (e.g. compression bit-rate) or technical quality measures (e.g. peak signal-to-noise ratio, PSNR) are often non-linearly related to human quality judgment, and the model of either relationship may not be known in advance. Using these approaches to select parameter levels may lead to an inaccurate estimate of the relationship between the parameter and subjective quality judgments -the system's quality model. To overcome this, we propose a method for modeling the relationship between parameter levels and perceived quality distances using a paired comparison parameter selection procedure in which subjects judge the perceived similarity in quality. Our goal is to enable the selection of evenly sampled parameter levels within the considered quality range for use in a subjective QA study. This approach is tested on two applications: (1) selection of compression levels for laparoscopic surgery video QA study, and (2) selection of dose levels for an interventional X-ray QA study. Subjective scores, obtained from the follow-up single stimulus QA experiments conducted with expert subjects who evaluated the selected bit-rates and dose levels, were roughly equidistant in the perceptual quality space -as intended. These results suggest that a similarity judgment task can help select parameter values corresponding to desired subjective quality levels.
Microgrids provide multiple benefits to end-use customers and electric utilities, including enhanced reliability and resilience, reduced operational costs, streamlined renewable generation integration, and improved energy efficiency. However, the microgrid technology's large capital cost remains a major barrier to establishing its economic viability. This paper addresses this challenge by proposing a practical methodology for microgrid generation sizing. The proposed methodology uses the concept of robust optimization and a multi-criteria decision-making process, taking overall cost, emission reduction, and demand response into account as important factors in optimal generation sizing. The objective is to minimize the supply gap throughout the year, which is defined as the unmet load or required load curtailment under various load and solar generation scenarios. Numerical simulations on a real-world microgrid, ComEd's Bronzeville Community Microgrid (BCM) on Chicago's South Side, exhibit the practicality of the proposed method and its applications for electric utilities. The study proposes an optimal size of 4.8 MW considering the commercially available generator sizes for the BCM, which has a total peak load of 7 MW, 0.75 MW of PV and 0.5MW/2MWh of Battery energy storage installed.
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