Бібліографічний опис:
Savoskin, Valerii. Unsupervised Anomaly detection / Valerii Savoskin; Supervisor: Oles Dobosevych; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2021. – 32 p.: ill.
Короткий опис (реферат):
The only way for the world to move into the bright future is to move from nonrenewable
resources into renewable ones. Creating and maintaining new economic
spheres always requires human care and supervision, the magnitude of which can be
lowered by using machine learning techniques. This work demonstrates the models
that are created to solve the task of anomaly detection in an unsupervised fashion.
This kind of methodology imposes a lot fewer restrictions on the data used while
providing a framework to find cracks on a wind turbine. Moreover, it is a good
building block for later research of unsupervised anomaly detection in the fields,
where getting data might cost a lot, and the cost of mistakes is high. The success of
the work can reduce the amount of time, money, and human resources for the big
companies that utilize green energy and invest in the future of our planet.
