The logging is the leading cause for the reduction in the forest area in the world. At the same time the number of forest clearcuts continues to grow. However despite the massive scale such incidents are difficult to track in time. As a result huge areas of forests are gradually being cut down. Therefore there is a need for regular and effective monitoring of changes in forest cover. The multitemporal data sources like Copernicus Sentinel-2 allow enhancing the potential of monitoring the Earth's surface and environmental dynamics including forest plantations. In this paper we present a baseline U-Net model for deforestation detection in the forest-steppe zone. Training and evaluation are conducted on our own dataset created on Sentinel-2 imagery for the Kharkiv region of Ukraine (31 400 km 2). As a part of the research, we present several models with the ability to work with time-dependent imagery. The main contribution of this paper is to provide a baseline model for the forest change detection inside Ukraine and improve it adding the ability to use several sequential images as an input of the segmentation model.

An experimental research facility has been developed to receive vibration signals from mechanisms with installed rolling bearings. A control block for all equipment has been created. For the repeatability of the experiment, an external microcontroller with a programmed proportional-integral-derivative regulator was used. Experiments were carried out to obtain initial data for different types of bearings. The processed data were grouped and made publicly available for further research. It is proposed to solve the problem of emergency stop of the generator, arising during operation due to bearings worn, by recognizing the pre-emergency conditions of rotary rig based on the use of advanced machine learning techniques: to highlight the signs of vibration and build clusters according to the degree of worn.