Research on multi granularity underwater situation awareness through virtual physics of artificial intelligence

Autor: YIN Tangwen, SUN Yiyang, ZHANG Xiaoshuang

Publikováno v: Zhihui kongzhi yu fangzhen, Vol 45, Iss 3, Pp 7-16 (2023)

On the basis of analyzing the development of underwater multi-platform cluster variable granularity situational awareness, this paper studies the system of systems and the synchronization of spatiotemporal evolution for multi granularity underwater s

Externí odkaz: https://doaj.org/article/c939c52b47454876b4d27fdf27f76434

Information extraction from ultrawideband electromagnetic wave in near radiation zone

Autor: Akhmedov R. D.

Publikováno v: Tekhnologiya i Konstruirovanie v Elektronnoi Apparature, Iss 3-4, Pp 3-10 (2020)

The authors study the dependence of the shape of electromagnetic pulse received in near radiation zone of the antenna on the observation point. The paper discusses negative and positive effects of this phenomenon on the wireless impulse communication

Externí odkaz: https://doaj.org/article/bfaf38812dad45648160b42c65758ba4

Topology of a Neural Network

Autor: Risto Miikkulainen

Publikováno v: Encyclopedia of Machine Learning and Data Science ISBN: 9781489975027
Encyclopedia of Machine Learning and Data Mining ISBN: 9781489975027
Encyclopedia of Machine Learning

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c3d1904948894dbdc6b281da226e594
https://doi.org/10.1007/978-1-4899-7502-7_843-2

Artificial Neural Networks

Autor: Miroslav Kubat

Publikováno v: An Introduction to Machine Learning ISBN: 9783030819347
An Introduction to Machine Learning ISBN: 9783319639123
An Introduction to Machine Learning ISBN: 9783319200095

Polynomial classifiers can model decision surfaces of any shape; and yet their practical utility is limited because of the easiness with which they overfit noisy training data, and because of the sometimes impractically high number of trainable param

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::434091251e9eb1dd795a46feba554f81
https://doi.org/10.1007/978-3-030-81935-4_6

Cross-Layer Design Exploration for Energy-Quality Tradeoffs in Spiking and Non-Spiking Deep Artificial Neural Networks

Autor: Bing Han, Abhronil Sengupta, Kaushik Roy, Aayush Ankit

Publikováno v: IEEE Transactions on Multi-Scale Computing Systems. 4:613-623

Deep learning convolutional artificial neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, desp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9845936621915dec121b100becc8ea4a
https://doi.org/10.1109/tmscs.2017.2737625