The purpose of this work is to explore methods of visual communication based on generative artificial intelligence in the context of new media. This work proposes an image automatic generation and recognition model th...
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The purpose of this work is to explore methods of visual communication based on generative artificial intelligence in the context of new media. This work proposes an image automatic generation and recognition model that integrates the Conditional Generative Adversarial Network (CGAN) with the transformer algorithm. The generator component of the model takes noise vectors and conditional variables as inputs. Subsequently, a transformer module is incorporated, where the multi-head self-attention mechanism enables the model to establish complex relationships among different data points. This is further refined through linear transformations and activation functions to enhance feature representations. Ultimately, the self-attention mechanism captures the long-range dependencies within images, facilitating the generation of high-quality images that meet specific conditions. The model's performance is assessed, and the findings show that the accuracy of the proposed model reaches 95.69%, exceeding the baseline algorithm Generative Adversarial Network by more than 4%. Additionally, the Peak Signal-to-Noise Ratio of the model's algorithm is 33dB, and the Structural Similarity Index is 0.83, indicating higher image generation quality and recognition accuracy. Therefore, the model proposed achieves high recognition and prediction accuracy of generated images, and higher image quality, promising significant application value in visual communication in the new media era.
Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation *** of charging infrastructure is needed to accelerate technology adoption;however...
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Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation *** of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across *** this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including *** investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously *** find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of *** evidence contrasts with predictions in the *** European markets,where the performance is closer to *** also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.
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