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SE-RWNN: an synergistic evolution and randomly wired neural network-based model for adaptive underwater image enhancement

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IET IMAGE PROCESSING 2020年第16期14卷 4349-4358页

作者： Li, Yang Chen, Rong Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China

Under water images are likely to suffer from severe degradation such as colour distortion, low contrast, and fuzz content, caused by the absorption and scattering effects of the water. To improve the visual appearance of the image, the authors present an adaptive algorithm for effective underwater image enhancement using a randomly wired neural network (RWNN) and synergistic evolution (SE). In doing so, they sequentially conduct colours adjustment, contrast improvement and luminance enhancement while enhancing details by an edge-preserving technique. To set up the system, they develop a multi-strategy cooperating evolution algorithm to figure out the optimal parameter values. Extensive experimental results show that the proposed model improves both subjectively and quantitatively the quality of underwater images.

关键词： image enhancement image denoising neural nets image colour analysis oceanographic techniques geophysical image processing edge detection SE-RWNN synergistic evolution neural network-based model adaptive underwater image enhancement water images severe degradation colour distortion fuzz content absorption scattering effects visual appearance adaptive algorithm effective underwater image enhancement randomly wired neural network colours adjustment contrast improvement luminance enhancement edge-preserving technique multistrategy cooperating evolution algorithm underwater images

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A neural network Extension for Solving the Pareto/Negative Binomial Distribution model

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INTERNATIONAL JOURNAL OF MARKET RESEARCH 2022年第3期64卷 420-439页

作者： Xie, Shao-Ming 2lpha Mkt Dept Data Intelligence 21 Yangshang Nanping 353600 Fujian Peoples R China

This research proposes a new estimation scheme to solve the estimation burden of the Pareto/NBD model and to release its power in out-of-sample prediction and then builds a neural network-based model (NNA-based model) for parameter estimation of the Pareto/NBD model, by designing a loss function to include the likelihood of the Pareto/NBD model and the mean absolute error. Following empirical analysis with a real dataset and simulation datasets, the main results are as follows. (1) The NNA-based model encounters a relatively skewed prediction in inactive prediction and focuses more on inactive prediction, while the heuristic method has a balanced prediction. (2) The Pareto/NBD model has better repeat purchase prediction compared to that under the mean absolute error, but the NNA-based model has a better fitting in the repeat purchase distribution. (3) The distribution of estimated parameters between these two models is quite different, but is able to realize the same prediction purpose. The NNA-based model also has greater application opportunities in the commercial environment because the learned estimator is applicable to different transaction timing patterns. These findings clarify that the NNA-based model solves the estimation burden and releases the predictive power of the Pareto/NBD model.

关键词： neural network algorithm pareto negative binomial distribution model heuristic method neural network-based model out-of-sample prediction

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Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

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REMOTE SENSING 2022年第23期14卷 5960-5960页

作者： Jiao, Zhonghu China Earthquake Adm Inst Geol State Key Lab Earthquake Dynam Beijing 100029 Peoples R China

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth's energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m(2), root mean squared error (RMSE) of 35.37 W/m(2), and coefficient of determination (R-2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of -2.26 W/m(2), RMSE of 32.94 W/m(2), and R-2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of -2.98 and -4.04 W/m(2), RMSEs of 6.50 and 13.42 W/m(2), and R-2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.

关键词： surface downward longwave radiation surface upward longwave radiation neural network-based model surface radiation budget AMSR2 passive microwave remote sensing

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Accuracy improvement of remaining capacity estimation for energy storage batteries

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The Journal of Engineering 2017年第13期2017卷 1833-1837页

作者： Cheng Xu Guo'an Liu Kangli Wang School of Electrical and Electronic Engineering State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei Province People's Republic of China

Scheduling lithium-ion batteries for energy storage applications in power systems requires accurate estimation of their remaining capacity. Due to the varying discharge rate during a cycle caused by complex operating conditions, conventional estimation methods suffer greatly from poor estimation accuracy. Therefore, this study proposes an improved Ah-counting method for remaining capacity estimation, in which a neural network-based model is introduced to consider the rate capability of batteries. Moreover, to avoid the inherent error caused by Ah-counting, a straightforward method is applied to the estimation. Simulations show that the estimation accuracy can be effectively improved by the proposed method.

关键词： secondary cells lithium compounds power engineering computing neural nets accuracy improvement capacity estimation energy storage batteries energy storage applications power systems complex operating conditions Ah-counting method discharge rate neural network-based model Li

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Application of neural network model to Vehicle Emissions

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INTERNATIONAL JOURNAL OF URBAN SCIENCES 2010年第3期14卷 264-275页

作者： Kim, Daehyon Lee, Jeong Chonnam Natl Univ Dept Civil & Environm Engn Gwangju South Korea Sunchon Natl Univ Dept Landscape Architecture Sunchon South Korea

The issue of air quality is now a major concern around the world and the vehicle emissions model is very important. Most of the current vehicle emission models are multiple regression techniques. In this study, a neural network-based model has been proposed to achieve better estimation accuracy. The estimation performance of two models, the proposed neural network-based model and a general regression model, has been compared using mean absolute error (MAE). A comparative study between two models to estimate vehicle emissions, the proposed neural network-based model and a general regression model, has been conducted to assess the estimation performance of the proposed model in terms of mean absolute percentage error. Experimental results in this study revealed that the neural network model performed better as it was able to decrease the error for emission estimation comparing with the multiple regression models. More importantly, in this study a lookup table (LUT) method has been proposed to overcome the black-box problem, which is a disadvantage of the neural network models. It could be useful for any other researches to estimate emissions without developing and training the neural network model which can be a time-consuming task.

关键词： vehicle emissions model multiple regression neural network-based model lookup table (LUT)

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