RESUMEN
Load recognition remains not comprehensively explored in Home Energy Management Systems (HEMSs). There are gaps in current approaches to load recognition, such as enhancing appliance identification and increasing the overall performance of the load-recognition system through more robust models. To address this issue, we propose a novel approach based on the Analysis of Variance (ANOVA) F-test combined with SelectKBest and gradient-boosting machines (GBMs) for load recognition. The proposed approach improves the feature selection and consequently aids inter-class separability. Further, we optimized GBM models, such as the histogram-based gradient-boosting machine (HistGBM), light gradient-boosting machine (LightGBM), and XGBoost (extreme gradient boosting), to create a more reliable load-recognition system. Our findings reveal that the ANOVA-GBM approach achieves greater efficiency in training time, even when compared to Principal Component Analysis (PCA) and a higher number of features. ANOVA-XGBoost is approximately 4.31 times faster than PCA-XGBoost, ANOVA-LightGBM is about 5.15 times faster than PCA-LightGBM, and ANOVA-HistGBM is 2.27 times faster than PCA-HistGBM. The general performance results expose the impact on the overall performance of the load-recognition system. Some of the key results show that the ANOVA-LightGBM pair reached 96.42% accuracy, 96.27% F1, and a Kappa index of 0.9404; the ANOVA-HistGBM combination achieved 96.64% accuracy, 96.48% F1, and a Kappa index of 0.9434; and the ANOVA-XGBoost pair attained 96.75% accuracy, 96.64% F1, and a Kappa index of 0.9452; such findings overcome rival methods from the literature. In addition, the accuracy gain of the proposed approach is prominent when compared straight to its competitors. The higher accuracy gains were 13.09, 13.31, and 13.42 percentage points (pp) for the pairs ANOVA-LightGBM, ANOVA-HistGBM, and ANOVA-XGBoost, respectively. These significant improvements highlight the effectiveness and refinement of the proposed approach.
RESUMEN
Critical-care helicopter transport has demonstrated improvements in morbidity and mortality to those patients who utilise the service, but this has largely excluded developing country populations due to set up costs. Haiti Air Ambulance is the first completely publicly-available helicopter ambulance service in a developing country. US standards were adopted for both aviation and aeromedical care in Haiti due to proximity and relationships. In order to implement properly, standards for aviation, critical care, and insurance reimbursement had to be put in place with local authorities. Haiti Air Ambulance worked with the Ministry of Health to author standards for medical procedures, medication usage, and staff training for aeromedical programs in the country. Utilisation criteria for the helicopter were drafted, edited, and constantly updated to ensure the program adapted to the clinical situation while maintaining US standard of care. During the first year, 76 patients were transferred; 13 of whom were children and 3 pregnant women. Three patients were intubated and two required bi-level mask ventilation. Traumatic injury and non-emergency interfacility transfers were the two most common indications for service. More than half of the transfers (54%) originated at one of six hospitals, mostly as a result of highly-involved staff. The program was limited by weather and the lack of weather reporting, radar, visual flight recognition, thus also causing an inability to fly at night. In partnership with the government and other non-governmental organisations, we seek to implement a more robust pre-hospital system in Haiti over the next 12-24 months, including more scene call capabilities.