| 1 | Fractal model for simulation of frost formation and growth显示文摘A planar fractal model for simulation of frost formation and growth was proposed based on diffusion limited aggregation(DLA)model and the computational simulation was carried out in this paper.By changing the times of program running circulation and the ratio of random particles generated,the simulation figures were gained under different conditions.A microscope is used to observe the shape and structure of frost layer and a digital camera with high resolution is used to record the pattern of frost layer at different time.Through comparing the simulation figures with the experimental images,we find that the simulation results agree well with the experimental images in shape and the fractal dimension of simulation figures is nearly equal to that of experimental images.The results indicate that it is reasonable to represent frost layer growth time with the program circulation times and to simulate the frost layer density variation during its growth process by reducing the random particle generation probability.The feasibility of using the suggested model to simulate the process of frost formation and growth was justified.The insufficiencies and its causes of this fractal model are also discussed. | LIU YaoMin,LIU ZhongLiang,HUANG LingYan&SUN JunFang Education Ministry Key Laboratory of Enhanced Heat Transfer and Energy Conservation,College of Environmental and Energy Engineering,Beijing University of Technology,Beijing 100124,China | 2010 | Science China(Technological Sciences)2010，53，3： | 4 |
| 5 | Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features显示文摘Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection. | Xinyue Huang Yi Ma Zongchen Jiang Junfang Yang | 2024 | Acta Oceanologica Sinica2024，43，3： | 0 |
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