Irrational numberNIST test suitePrevious studies have developed pseudo-random number generators, where a pseudo-random number is not perfectly random but is practically useful. In this paper, we propose a new system for pseudo-random number generation. Recurrent neural networks with long short-term ...
communication delay, and uneven distribution of measurement points [3]. To solve this problem, researchers have proposed the solution of pseudo-measurement generation, i.e., artificially generating currently available "measurement data" using existing data...
Cross-domain few-shot learning is one of the research highlights in machine learning. The difficulty lies in the accuracy drop of cross-domain network learning on a single domain due to the differences between the domains. To alleviate the problem, accor
The authors would like to thank the Key Project of Colleges and Universities of Henan Province, China through project number 23A52002 and Science and Technology Innovation 2030-“New Generation of Artificial Intelligence” through project number 2021ZD0111000 for funding this research work.References...
ytidb::LAST_RESULT_ENTRY_KEYStores the user's video player preferences using embedded YouTube video Maximum Storage Duration: PersistentType: HTML Local Storage YtIdbMeta#databasesUsed to track user’s interaction with embedded content. Maximum Storage Duration: PersistentType: IndexedDB yt-remote-ca...
Internal Combustion Engines (ICEs) are used as the primary or secondary source of power generation for traditional and hybridized vehicles, respectively, and are unlikely to be phased out long-term. Currently, there are no safe alternative fuel sources with comparable energy density to fossil fuels...
It is prohibitively expensive to install laboratory grade sensors on and within the engine to be used in combination with dynamometers for developing characteristic engine maps. Using a set of pseudo engine dynamometer-based datasets generated from GT-Power, we developed multiple sets of Artificial Ne...
Few-shot learning’s objective is to develop precise models with fewer samples. A CGAN is a type of generative adversarial network that involves the conditional generation of images by a generator model. This enables discernible image generation of a specified type. Semi-supervised learning employs ...
using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we...