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Статья посвящена эстетическому воспитанию с помощью хореографического искусства. Хореография является одним из средств музыкального воспитания, физического, художественно-эстетического развития и образования.

Статья посвящена развитию двигательных навыков и музыкальности посредством занятий хореографией. Автор отмечает важность работы концертмейстера в хореографии.

This master thesis addresses the energy management system design for robots operating in harsh and hazardous environment. Intelligent mechatronic solutions aim at minimizing personnel exposition to radiation and maximizing machine uptime, therefore rational energy usage of on-board power supply is crucial to ensure the fulfillment of these goals. Main objectives of the energy management system include possibility of performing reliable forecasts of power consumption, opportunity of monitoring the actual energy usage through battery state of charge and development of tools for planning energy efficient operations. The problem is tackled using sequential approach. First, the power consumption model of robotic arm based on machine learning techniques is developed. Second, different methods to build a power consumption model of the locomotion are studied and the polynomial model is chosen according to established criteria: accuracy, robustness, speed and complexity. Third, the solution of a constrained nonlinear optimization problem of constructing an optimal velocity profile for predefined trajectory length and desired traverse time is presented for trapezoidal and minimum energy velocity profiles. Finally, a method to compute the battery state of charge is proposed. Created algorithms are tested on the robot in the real environment to prove their validity and evaluation of the achieved results is presented. Developed energy management system is implemented in the robot’s hardware and is integrated into the graphical interface of the robotic framework.

The goal of this project was to work on room occupancy and to provide solutions and ideas on how to detect if a room is occupied or not using generic and non expensive sensors. This is motivated by the fact that buildings account for 50% of the total energy consumption in the US and 30% of it is wasted mainly due to a bad management of light, heating, cooling and ventilation systems. Being able to know if rooms are occupied or not would then enable us to automatize these systems and make them more energy efficient. The sensors studied in the process were a CO2, relative humidity and temperature sensor, a camera, a WiFi sniffer and a PIR(Passive infrared) sensor. All of them were implemented and tested to see how they could measure occupancy. After analyzing each sensor alone we had to combine them to obtain the best possible accuracy. To do so, two methods were tried. The first using all the sensors mentioned was tested and the accuracy achieved was 93%. The second was based on the use of neural network on images provided by the camera to detect people and the accuracy was 97%.

Electric vehicles’ market penetration has been rising due to new technological developments and awareness of the climate change threat. Instead of being a burden for today’s electricity production, such as increasing peak demands, they can also have a positive impact when charged smartly, coupled with clean solar electricity (PV). Smart charging models have to be able to account for the uncertainty of production and consumption forecast, the driver’s satisfaction, and other objectives such as operational cost, PV self-consumption, and peak shaving. The goal of this thesis is to build a smart charging model capable of forecasting uncertainty, include it in the optimization problem and implement real-time solution adjustment through dynamic control. The results show that combining Artificial Intelligence, stochastic optimization, and Model Predictive Control in a smart charging model leads to a decrease in the cost of operation, an increase in PV-self consumption, and reduces peak demand to the grid.