Solar cookers can greatly reduce the overall carbon footprint of the cooking done in India. In the present work a funnel-type solar cooker is designed using cardboard. After making the solar cooker it is then analyzed on the various performance metrics namely the figures of merit, efficiency value and Cooker Opto–thermal Ratio (COR) which are dependent parameters. Paraffin wax which is a phase change material (PCM) is also incorporated in the testing process to evaluate the overall improvement in the thermal efficiency of the solar cooker. The time taken to break is also calculated. The experimental results show that the solar cooker is capable of reaching a temperature of 125 °C. From the results it can also be seen that using paraffin wax also offers significant improvement in the overall thermal efficiency. The results are tested on various parts of India considering the major cities such as Chennai, Trivandrum, Kanpur and Delhi with the ANN model, which is a deep learning model. The advantage of this model is that it can forecast and estimate the temperature of the absorber plate and water from weather forecasting data which is used to calculate F1 and F2 metrics for the performance of the solar cooker. For all the cities, the model’s R2 value is greater than 99% and RMSE values are small.
Buildings expand and contract in response to their environment, which results in cracks in the structure. This can pose a serious threat to the people who use it, and these movements are frequently too small to be observed, and thus go unnoticed. Cracks can be caused by a variety of factors, including defects in the construction process, ground movement, foundation failure, and decay of the building fabric. If a structure is unable to accommodate this movement, cracking is likely to occur, posing a serious risk to the building's structural integrity. Only after cracks are identified can they be treated, and existing manual methods of sketching the crack patterns are highly subjective to the person performing the analysis, are frequently constrained by high costs, equipment and tool availability, and are extremely time consuming. In this paper, 40,000 images divided into two and categorized into positive and negative cracks are used as input and the presence of cracks is detected using a deep learning technique. The following crack types are included in the experimentation: hairline, stepped, vertical, and horizontal. In comparison to conventional image processing and other deep learning-based techniques, the proposed Convolutional Neural Network (CNN) achieves significantly higher accuracy than the Recurrent Neural Network (RNN). This paper’s objective is to create a model which can detect the cracks through deep learning methodology, and this will be the innovative region in crack detection using neural net framework.
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