Artificial intelligence (AI) has become increasingly popular as a tool to model, identify, optimize, forecast, and control renewable energy systems. This work aimed to evaluate the capability of the artificial neural network (ANN) procedure to model and forecast solar power outputs of photovoltaic power systems (PVPSs) by using meteorological data. For this purpose, based on the literature review, important factors affecting energy generation in a PVPS were selected as inputs, and a recurrent neural network (RNN) architecture was established. After completing the trained network, the RNN capability was assessed to predict the energy output of the PVPS for days not included in the training database. The performance evaluation of the trained RNN revealed a regression value of 0.97774 for test data, whereas the RMSE and the mean actual output power for a sample day were 0.0248 MJ and 0.538 MJ, respectively. In addition to RMSE, an error histogram and regression plots obtained by MATLAB were employed to evaluate the network’s capability, and validation results represented a sufficient prediction accuracy of the trained RNN.
The present study was designed for the experimental and modeling investigations on thermodynamic analysis of drying onion slices using four microwave power levels (100, 350, 500, and 750 W) and the four samples thicknesses (2.5, 5, 7.5, and 10 mm). A multilayer feedforward artificial neural network was employed in order to predict energy and exergy performance of the dryer and the prediction success were compared using three evaluation criteria. The average values for energy efficiency and specific energy loss ranged from 13.52% to 37.94% and from 1.35 to 7.43 MJ/kgwater, respectively. Findings showed that the exergy efficiency changed from 11.79% to 30.84%. In addition, the statistical analysis revealed that higher microwave power levels and the thinner samples significantly (p < .05) enhanced both the energy and exergy efficiencies. The obtained results for exergy improvement potential (accounted for 37.83%–69.41% from the total exergy inlet) indicated that the drying process has good potential for exergy performance improvement. Based on the modeling outcomes, the energy efficiency was well predicted by an artificial neural network with a topology of 3–18–18–1 and LM training algorithm and threshold function of Tan–Tan–Lin. However, the best topology for the exergy efficiency prediction had 3–20–16–1 structure, LM training algorithm and Log–Log–Lin transfer function (R2 of .94). Practical Applications In this study, onion slices were dried in a domestic microwave oven and the influence of the power level and thickness of the samples on energy and exergy parameters was investigated. In addition to experimental evaluation, the multi‐layer feed‐forward neural network can be used to model and predict changes in energy efficiency and exergy during the process.
Introduction:Colorectal cancer, as one of the most important fatal cancers, is caused by the lack of timely diagnosis of colorectal polyps. Presently, because of the advancements in CT imaging of the colorectal device, the CTC-CAD is a promising method for the duly diagnosis of these appendages. In this regard, Electronic Colon Cleansing (ECC) is one of the effective factors that enhance diagnostic accuracy in the methods used in CTC-CAD. To date, various methods have been utilized for ECC (e.g., the mosaic decomposition (MD) method) that each has advantages and limitations. Therefore, the aim of this study is to combine the methods of linear computing of previous studies and also some image processing methods to improve the quality of electronic cleansing of data on residual materials existed in CT colorectal images. This proposed method is called LM_ECC. Method:In this study, to implement ECC, the thresholding method, statistical functions, and image processing methods were combined. Then, to evaluate the proposed method, 22 images were randomly selected and ranked by seven radiologists. Regarding the extent of the interpretable, the images taken before and after ECC were collected using MD and LM_ECC methods. The concordance of concordance of all three categories of opinions was calculated based on Kendall's tau-b correlation coefficient test. Next, the average of the ranked opinions obtained for the main images and the results of the LM_ECC method, as well as for the MD and the LM_ECC method, were included in two T-tests. Findings:The value of t-test between the mean score of radiologists' opinions for the main images and the results of the LM_ECC method (p <0.001) is -9.355, while it is -5.414 between the mean score of radiologists for the MD results and the results obtained from the LM_ECC method (p <0.001). Conclusion:Based on the coefficient of concordance, it is found that there is a high agreement between the ranked opinions of the radiologists, based on which the results of the T-tests show the significant effect of the LM_ECC method on electronic cleansing compared to the main images and the results of MD method. Therefore, it can be concluded that the LM_ECC method is able to improve the quality of electronic cleansing of colorectal CT images.
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