Volume 163

License plate recognition in foggy environments is one of the core challenges in Intelligent Transportation Systems (ITS). Fog causes blurred license plate images and reduced contrast, severely degrading recognition accuracy. This paper systematically reviews the key technologies for foggy license plate recognition, covering traditional and deep learning-based image dehazing methods, as well as advances in license plate localization and recognition algorithms, while analyzing their strengths and limitations. The study highlights that current methods face bottlenecks in dynamic fog density adaptation and generalization in extreme weather conditions. Future improvements require multi-modal data fusion and adaptive optimization to enhance performance. This work aims to provide theoretical references for optimizing and deploying license plate recognition technologies in foggy environments.

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Accurate carbon price forecasting plays a key role in promoting emission reductions and advancing the low-carbon economy. Given the strong nonlinear nature of carbon prices and the subjective challenge in tuning hyperparameters for traditional LSTM networks, this study introduces a prediction framework combining a Hybrid Particle Swarm Optimization (HPSO) algorithm with an LSTM neural network. Using China’s national carbon market data, both univariate and multivariate time series predictions are conducted. Results demonstrate that the HPSO algorithm efficiently tunes LSTM hyperparameters, enhancing performance compared to multilayer perceptron (MLP) models. Moreover, incorporating multiple variables yields superior predictive outcomes over using historical prices alone.

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