The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.

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Numerous studies are conducted to improve the fl ow in the boundary layer to ensure laminar fl ow and in particular to increase fl ight safety. A new solution used to improve the laminar fl ow is the plasma actuator. The classic confi guration of DBD plasma actuators is commonly used with the asymmetric electrode system. The manuscript describes the results of tests with a plasma actuator. Experimental tests were carried out on the built model of the wing with the SD 7003 profi le, a plasma actuator was mounted on the upper surface. In contrast to the commonly used solution with solid tape copper electrodes, the novelty in the described research in the manuscript is the use of a large GND electrode (covering 70% of the upper surface of the wing) and a HV mesh electrode. The use of a plasma actuator on the upper surface of the wing aff ects the air fl ow in the boundary layer as a result of air ionization. The tests were carried out for a supply voltage from V = 7.0 kV to 12 kV and Reynolds number, R e = 87500 to 240000, fl ow velocity during the tests in the tunnel was in the range of U = 5-15 m/s and the angle of attack α = 5-15 degrees. On the basis of the results experimental tests, the percentage change in the lift coeffi cient was calculated for the switched on and off DBD system. The obtained results indicate a maximum 17% increase in the lift coeffi cient for the plasma actuator activated for air fl ow U = 5 m/s and angle of attack α = 5 degrees. In the remaining confi gurations, changes in the lift coeffi cient amounted to 4%.

The publication includes a review of information on the methods of pavement condition recognition using various methods. Measurement system has been presented that allows to determine the condition of the pavement using the Inertial Measurement Unit (IMU) and machine learning methods. Three machine learning methods were considered: random forest, gradient boosted tree and custom architecture neural network (roadNet). Due to the developed system the set of learning and validation data was created on 3 vehicles: Opel Corsa, Honda Accord, Volkswagen Passat. All of the listed vehicles have front wheel drive. The presented machine learning methods have been compared with each other. The best accuracy on the validation set was achieved by the artificial neural network (ANN). The study showed that asphalt condition classification is possible and the developed system fulfils its task.