![]() He was not involved in the research but has tested Pangu-Weather. Pangu-Weather and similar models, such as Nvidia’s FourcastNet and Google-DeepMind’s GraphCast, are making meteorologists “reconsider how we use machine learning and weather forecasts,” says Peter Dueben, head of Earth system modeling at ECMWF. In the past year, multiple tech companies have unveiled AI models that aim to improve weather forecasting. Pangu-Weather is exciting because it can forecast weather much faster than scientists were able to before and forecast things that weren’t in its original training data, says Fuhrer. This finding shows that machine-learning models are able to pick up on the physical processes of weather and generalize them to situations they haven’t seen before, says Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. Pangu-Weather was also able to accurately track the path of a tropical cyclone, despite not having been trained with data on tropical cyclones. Comput.The researchers tested Pangu-Weather against one of the leading conventional weather prediction systems in the world, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF), and found that it produced similar accuracy. Junsheng, M., Youheng, T., Dongliang, X., Fangpei, Z., Xiaojun, J.: CNN and DCGAN for spectrum sensors over rayleigh fading channel. In: IEEE Journal on Selected Areas in Communications. ģ-D Deployment of UAV Swarm for Massive MIMO Communications. In: IEEE Wireless Communications Letters. Signal Processing 41(1) (1993)Īerial RIS-Assisted High Altitude Platform Communications. ![]() 25(10), 3301–3304 (2021)Ĭhandran, V., Elgar, S.L.: Pattern recognition using invariants defined from higher order spectra: one dimensional inputs. Junsheng, M., Gong, Y., Zhang, F., Cui, Y., Zheng, F., Jing, X.: Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks. Kunming University of Science and Technology (2020) Yutao, H.: Radar Emitter Signal Recognition Based on Deep Learning and ambiguity function. Zhe, Z.: Radar Signal Recognition and Parameter Estimation Based on FRFT. Jiahuang, S., Jianchong, H., Yongcheng, Z.: Summary of rapid recognition of radar emitter signal. Journal of Physics: Conference Series, 1952 (2021) Jialu, L., Huaidong, S., Bin, Z.: Radar signal classification based on bayesian optimized support-vector machine. Experimental results show that radar signal classification based on bispectrum features and convolutional neural networks can effectively improve the effect of radar signal classification. Therefore, the images of the signal bispectrum after pre-processing and data enhancement can train convolutional neural networks to obtain deeper signal features. ![]() Aiming at the problem of low accuracy of radar signal classification in a low signal-to-noise ratio environment, a classification method based on bispectrum feature and convolutional neural network is proposed, it increases the accuracy of signal classification by taking advantage of bispectrum, which suppresses the Gaussian noise and retains the phase information. Radar signal classification is the key link in electronic information warfare, but as radar modulation becomes more sophisticated and the electromagnetic environment of the battlefield becomes complex, it is increasingly difficult to classify the radar signal. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |