RESUMO
Aiming at the problem of present power harmonic and inter-harmonic detection methods, which are susceptible to noise interference and have low detection accuracy for inter-harmonics, a novel power harmonic and inter-harmonic detection method based on fractional wavelet transform (FRWT) combined with variational mode decomposition (VMD) is proposed. The FRWT-VMD algorithm first preprocesses the signal with FRWT and VMD. Second, the VMD is used to extract harmonics and inter-harmonics. The method incorporates the advantages of the FRWT and VMD methods, corrects the defect that the FRWT method is difficult to detect inter-harmonics, and improves the problem that the VMD method is susceptible to noise interference. Simulation tests on various power harmonic signals have shown that under low signal-to-noise ratio conditions, the FRWT-VMD algorithm can effectively reduce the impact of noise and has high accuracy in harmonic and inter-harmonic detection. It is a powerful method for detecting harmonics and inter-harmonics in power systems.
RESUMO
The thermal analysis kinetic behavior of pulverized coal combustion in different oxygen-rich atmospheres is studied with a thermogravimetric (TG) analyzer. The effects of heating rate on combustion characteristic are considered. The results showed that the combustion rate of pulverized coal increased and the burnout time decreased under the conditions of an oxygen-enriched atmosphere and high heating rate. As the heating rate increases, the TG and derivative TG curves of the coal samples generally move toward the high-temperature region, and the thermal hysteresis phenomenon occurs. The changes in oxygen concentration and heating rate mainly affect the combustion stage of coal samples. The decrease in heating rate or the increase in oxygen concentration will cause a decrease in ignition point temperature and burnout temperature. Under the condition of 40% O2, the oxygen concentration and heating rate have the greatest influence on the combustion characteristic parameters. In addition, the combustion reaction of pulverized coal in the warming process was analyzed by FWO and KAS methods, respectively. The results showed that the changes in oxygen concentration and heating rate affected the activation energy, and the activation energy of coal samples increased with the increase in oxygen concentration.
RESUMO
BACKGROUND: Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of using artificial intelligence-based methods in an eastern Chinese population. METHODS: A total of 9586 orthopantomograms (OPGs) (4054 boys and 5532 girls) of the Chinese Han population aged from 6 to 20 years were collected. DAs were automatically calculated using the two CNN model strategies. Accuracy, recall, precision, and F1 score of the models were used to evaluate VGG16 and ResNet101 for age estimation. An age threshold was also employed to evaluate the two CNN models. RESULTS: The VGG16 network outperformed the ResNet101 network in terms of prediction performance. However, the model effect of VGG16 was less favorable than that in other age ranges in the 15-17 age group. The VGG16 network model prediction results for the younger age groups were acceptable. In the 6-to 8-year-old group, the accuracy of the VGG16 model can reach up to 93.63%, which was higher than the 88.73% accuracy of the ResNet101 network. The age threshold also implies that VGG16 has a smaller age-difference error. CONCLUSIONS: This study demonstrated that VGG16 performed better when dealing with DA estimation via OPGs than the ResNet101 network on a wholescale. CNNs such as VGG16 hold great promise for future use in clinical practice and forensic sciences.
