In order to fulfill the responsibilities of international Maritime environmental protection and respond to the national "dual carbon" strategic policy, as well as to cope with the rising fuel prices, the relevant research on ship energy saving and emission reduction technologies has gradually received the attention of the International Maritime Organization. The focus of relevant organizations such as IMO, national maritime authorities and shipowners. In the context of shipping data, the effective use of ship energy consumption monitoring data to accurately predict ship operation energy consumption is becoming more and more important to achieve shipping energy conservation and emission reduction, and also an effective means to respond to national strategies. From the perspective of energy saving, it relies on the pycharm platform, analyzes and preprocesses multiple types of open source data collected during the actual voyage of a passenger ship, establishes the system input features, and the ship fuel consumption prediction model based on the Stacking model. The prediction results are compared with those single model. The advantage of the integrated learning model of Stacking is proved.
In the aerospace field, in the process of identifying and tracking air targets, it is faced with inaccurate or incomplete target recognition. This research is based on the auxiliary analysis and recognition capabilities of deep learning convolutional neural networks to realize the recognition of aerial targets, so as to improve the recognition accuracy and reduce the recognition error rate. Take the aircraft data provided by the Institute of Aeronautics and Astronautics as the research object, perform image preprocessing on it, build the aircraft data set, build a network framework using python language in the TensorFlow environment, and perform recognition training on the model, and finally test the trained model and result analysis.
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