2 research outputs found
Detection of Atmospheric Gravity Waves: Two classification approach - Image classification and meteorological feature classification
No full textWith the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces, generated by topographical or meteorological obstacles in neutral or stable surface atmospheric conditions. AGWs are frequent over offshore regions and affect offshore wind farms as the event occurs over a large area. Detecting them through satellite images is easy by an eye test, but not so much when viewed digitally through meteorological data. Weather data can be obtained from reanalysis data which combines past weather forecasts with observational data assimilation. This project aims to develop machine learning models that detect AGWs in satellite images and detect AGWs from atmospheric conditions, such as temperature and wind speed profile with height. The models learn using the reanalysis data and satellite images. The same satellite images are used to label the reanalysis data so that the model is taught to pick out gravity waves in the case of having no satellite image. Thus the final objective of the project is to train a model to detect an AGW event, based solely on reanalysis data. The trained model is then used to predict the percentage of time an AGW occurs or will occur over a chosen wind farm site.Electrical Engineering | Sustainable Energy Technolog
A 1-D Convolutional Neural Network with Gradient Mapped Intensity Features for Detection of Mitosis in Histopathological Images
Full text linkThis paper proposes a mitosis detection algorithm that utilizes gradient-mapped intensity (GMI) features integrated into a one-dimensional convolutional neural network (1-D CNN) for the classification of mitotic cells in histopathological images. The proposed framework begins by preprocessing the input images through intensity compensation, followed by contrast enhancement using adaptive histogram equalization. Mitosis candidates are subsequently identified using adaptive thresholding techniques and morphological operations. From each detected candidate, GMI features are extracted through gradient estimation in both the x and y directions, construction of gradient histograms, and mapping of gradient magnitudes with corresponding intensity values. These features, derived from the red, green, and blue (RGB) channels, are used to train a 1-D CNN classifier that categorizes the inputs into two classes: mitosis and non-mitosis. The effectiveness of the proposed approach is evaluated using two benchmark datasets, ICPR 2012 and ICPR 2014, with performance measured via precision, recall, and F1-score metrics. The proposed model achieves an F1-score of 0.846, a recall of 0.859, and a precision of 0.863 on the ICPR 2012 dataset, demonstrating competitive performance compared to existing methods
