RESUMEN
The aim of this study is to explore the characteristics of an active Free-Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time-intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to expedite the process. To construct a nonlinear model, 5000 samples are extracted from simulation results. Input parameters included in the model are the hot and cold source temperatures, the voltage given to the DC motor, spring stiffness, and the mass of the power piston, while output parameters are the amplitude and frequency of power piston displacement. The proposed ANN model structure comprises two hidden layer with 10 and 20 neurons, respectively, indicating the applicability of the ANN model in estimating significant parameters of AFPSE in a shorter amount of time. The firefly optimization algorithm is utilized to determine the unknown input parameters of ANN and maximize the output power. Results indicate that a maximum output power of 23.07 W can be attained by applying 8.5 V voltage on the DC motor. This study highlights the potential of machine learning techniques to explore the primary features of AFPSE.
RESUMEN
A female term infant was born with multiple bony and facial abnormalities involving the mandible, palate, tongue, nasopharynx, vertebrae, ribs and brain. Death occurred at 1 week and a rare combination of forebrain anomalies, were found in a big head, with complete agenesis of the corpus callosum with duplication of the infundibulum, pituitary, anterior third ventricle and aqueduct. The CAT scan was diagnostic of agenesis of corpus callosum. Incomplete twinning or incompetent neural induction with genetic and epigenetic factors have been suggested as possible mechanisms responsible for this rare malformation.
