Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands.

Autor: Lupea, Iulian¹ (AUTHOR) adrian.coroian@mep.utcluj.ro, Lupea, Mihaiela² (AUTHOR) mihaela.lupea@ubbcluj.ro, Coroian, Adrian¹ (AUTHOR)

Publikováno v: Sensors (14248220). Jun2024, Vol. 24 Issue 11, p3337. 25p.

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks.

Autor: Lupea, Iulian¹ (AUTHOR) iulian.lupea@mep.utcluj.ro, Lupea, Mihaiela² (AUTHOR) mihaela.lupea@ubbcluj.ro

Publikováno v: Sensors (14248220). Nov2023, Vol. 23 Issue 21, p8769. 22p.

AutoAt: A deep autoencoder-based classification model for supervised authorship attribution

Autor: Briciu, Anamaria, Czibula, Gabriela, Lupea, Mihaiela

Publikováno v: In Procedia Computer Science 2021 192:397-406

Machine Learning Techniques for Multi-Fault Analysis and Detection on a Rotating Test Rig Using Vibration Signal.

Autor: Lupea, Iulian¹ (AUTHOR) iulian.lupea@mep.utcluj.ro, Lupea, Mihaiela² (AUTHOR)

Publikováno v: Symmetry (20738994). Jan2023, Vol. 15 Issue 1, p86. 17p.

A chain dictionary method for Word Sense Disambiguation and applications

Autor: Tatar, Doina, Serban, Gabriela, Mihis, Andreea, Lupea, Mihaiela, Lupsa, Dana, Frentiu, Militon

Publikováno v: Studia Universitatis Babes-Bolyai, Special Issue, KEPT 2007, Knowledge Engineering: Principles and Technologies, Cluj-Napoca, June 6-8, 2007, pp 33-40

A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorithm, which exploits the sense definitions in the dictionary directly. Our approach uses

Externí odkaz: http://arxiv.org/abs/0806.2581