- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Preservation of image edge feature based on snowfall model smoothing filter
摘要： This paper proposed a snowfall model as a novel smoothing filter. The pixel composition of the image was similar to the geographic features, so it could be smooth because of snow accumulation. In the snowfall processing, luminance changes are linked to terrain and snowfall amount. Curvature and luminance gradient decided the amount of snowfall; the amount of snowfall became large on the parts where the curvature was large, and it became little on the parts where the gradient was steep. Snowfall algorithm simulates the natural snowfall process, which nonlinear diffusion and the target feature could be preserved well. Snowfall model has the same function as the Gaussian filter. The number of regions was reduced after Gaussian filter and snowfall model smoothing, respectively. The contrast experiment was carried out based on Watershed algorithm. The image area segmentation that pretreated through snowfall model was compared with Gaussian filter smoothing. The experimental result showed that the proposed snowfall model was a smoothing filter. It was able to realize edge preservation, which was the original purpose, and it was also possible to apply to region segmentation.
关键词： Snowfall model,Smoothing filter,Region segmentation,Edge characteristics,Image preservation
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - Low-THz Wave Snow Attenuation
摘要： This paper assesses the attenuation of Low-THz waves through various intensities of snowfall experimentally at 300 GHz, and is characterized by measuring the ratio of the received power from the target through the snow precipitation and through the same path with no precipitation. Higher attenuation is measured at higher snowfall rate. Snow events are categorized as "dry snow" and "wet snow". The comparison between the measured attenuation through wet and dry snowfall shows larger attenuation is expected through wet snowfall compared to the same snowfall rate of dry snow. This study is fundamentally important to investigate the effect of adverse weather condition on Low-THz waves and to assess their feasibility for outdoor applications.
关键词： snowfall,specific attenuation,Low-THz wave
更新于2025-09-23 15:22:29
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Snow Loss Prediction for Photovoltaic Farms Using Computational Intelligence Techniques
摘要： With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.
关键词： snow loss,Intelligent prediction,snowfall,photovoltaic (PV) farm,machine learning
更新于2025-09-23 15:21:01