In this paper three image quality metrics are compared: mean-square error (MSE), noise quality measure (NQM), and structural information metric (SIM). Such a comparison is made in order to evaluate the performance of those different metrics under practical conditions. Experimental results along with statistical indices of performance are provided. The referred results suggest that there are situations in which the MSE-based metric outperforms the two other considered metrics.

We present a synergistic investigation into ozone production dynamics within a high-voltage reaction chamber operating from 1.75 kV to 10 kV, at 60 Hz. The output ozone concentration dependence on a 60 Hz root mean square high-voltage for an air input pressure of 0.1 bar was measured using an ultraviolet-based sensor. A finite-element numerical model of the reaction chamber was developed and used for obtaining the reaction rate for the generated ozone for each level of input voltage and the internal distribution of the gas vector velocity field and ozone concentration. An expression to estimate the ozone reaction rates from the measured ozone concentrations was also derived. It was then compared and validated using the developed computational model. Therefore, in this work, a formula is proposed to estimate the ozone production by providing the experimental reaction rates. The other objective is to analyze the hysteresis phenomena observed in the partial discharges’ levels in the reaction chamber and in the output ozone concentration. Finally, we establish a connection between the total power of partial discharges and the consequent ozone production, clarifying the role of cumulative partial discharges’ power levels in governing the resultant output ozone concentration.

This paper addresses the use of full reference metrics in the characterization of perceptual difference in a pair of images, in which one of them is degraded by additive white Gaussian noise (AWGN). Conventional (SNR, PSNR, and CC) and perceptual (MSSIM, IFC, VIF, and C4) full reference metrics are considered. The subjective experiment, which upholds the presented results, has been carried out with approximately 120 human subjects, considering a test set of 60 images divided into 5 groups. The difference detection threshold is defined as the value for a given metric from which human subjects perceive the difference between the reference and test images. In this sense, the performed data analysis aims both to derive difference detection thresholds for each tested metric and to evaluate their consistency.

Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling.