Акустооптическая вейвлет-обработка биоэлектрических сигналов

Wavelet analysis is one of the most efficient methods of the informative signals characteristics investigation. First shown the possibility of informative signals wavelet processing by means of the acousto-optic processor with time integration. The possibility of the realization of both power spectrum calculation and wavelet transform performance of bioelectric signals in the real time mode has been proved. The analysis is listed which describes its operation in different modes

Real-Time Optimal Negotiation Mode Selection Based on Three-Way Decision for Decentralized Remote Sensing Satellite Cluster

The mission planning for multisatellite is a complex optimization problem, which is sensitive to time delay caused by communication and decision. Different modes are suitable for different situations. Therefore, we design the workflows of three modes: the independence mode, the MAS mode, and the ground-based mode. And then, a real-time mode selection method based on the three-way decision is proposed to choose the best mode onboard. The experiments proved the effectiveness and advantage of our proposed method.

Data transmission with Simulink on 6-DoF platform on CAN BUS

Use of CAN BUS for data transmission in Real-Time mode with Simulink on control objects is considered (6-DoF a platform).It is revealed that software of CAN_ API.dll adapters, created in the Microsoft Visual Studio (MVS) does not work with TDM-GCC-64 Matlab/Simulink because of different approach in names of the dll functions according to the standard C ++ 11/17. Recompile by the developer of the adapter of its software (dll) in the TDM-GCC-64 environment under Windows is required.It is established that CAN BUS considerably reduces modeling speed by 4.5 times. The way of information compression and fall forward of exchange twice due to byte-by-byte entering of two float values in the data field is offered. Use of identical values of identifiers is applied to two cylinders 6-DoF of a platform and the subsequent their division in the program microcontrollers of cylinders.For implementation of a Real-Time mode in addition to compression it is offered to transfer data with the smaller frequency (quantization) by what a modeling clock period. It was considered that 6-DoF platforms reproduce frequency band to 10–12 Hz. The program of transfer/data exchange with Simulink on stand control devices with quantization is developed. Influence of parameter of quantization for the period of modeling is investigated. It is established that the Real-Time mode of modeling is provided in the range of parameters of quantization (chc=1/350–1/1000). Frequency of exchange with 6 cylinders at the same time corresponds to 230, 150 Hz.

Analysis of erypsoidal heights errors based on GNSS-leveling results

Research the influence of errors on the measurement results is always an urgent task. Analysis of such values makes it possible to assess the nature of the change and the magnitude of the impact of errors for their further consideration or compensation, or minimization. In this paper, the errors in determining ellipsoidal heights from GNSS observations are considered. In determining the ellipsoidal heights, this method can achieve an accuracy of 1–2 cm in static mode (Static) and 2–4 cm in real time mode (RTK). Thus, the accuracy of the chosen mode of observations will indicate the initial limits of the ellipsoidal heights errors influence, and the factors that arise directly during observations and data processing will determine the extent to which these errors will change relative to the initial limits. The purpose of this work is to analyze the errors of ellipsoidal heights based on the results of GNSS-leveling obtained in the static and RTK modes. Method. The study used GNSS-leveling data at 17 points (wall and soil benchmarks) of leveling lines of I–II classes, which are located within a radius of 15 km from the permanent station SULP of the Lviv Polytechnic National University. Observations were performed in static mode (4-hour) and RTK (8–10 measurements). Points are divided into three categories (5–6 points): 1) statics on wall benchmarks; 2) real-time mode on wall benchmarks; 3) static mode on soil benchmarks. By combining methods and categories, four GNSS networks were formed, including 11, 11, 12 and 17 points. Results. For each category, the percentages within which the errors of ellipsoidal heights change in static observation mode and real-time mode using the GNSS leveling method are presented. On the basis of the received information it is established that for the first case errors of ellipsoidal heights on the average change within ± 43 %, for the second ± 36 %, and for the third ± 14 %. The analysis of statistical characteristics for each category shows that the standard deviation of the static mode data is 2 % and 19 %, and the RTK mode – 12 %, respectively. Scientific novelty and practical significance. The nature of the change in the error limits of the ellipsoidal heights determination gives an idea of what accuracy should be expected when performing GNSS-leveling depending on the mode of observation. Such data play an important role in solving scientific and applied problems by GNSS leveling, such as the constructions of new leveling networks or monitoring the height points of existing networks.

FLECK QUANTIFICATION OF THE NUMBER OF INFECTIOUS SARS-COV-2 CORONAVIRUS PARTICLES

The principle of obtaining sensors for detecting infectious particles of RNA viruses in samples was proposed for the diagnosis of infectious particles SARS-CoV-2. Previously, the similar sensor pattern was successfully applied in relation to the hepatitis C virus. It was founded that the ratio of the RNA titer, determined in the RT-PCR reaction in Real Time mode, refers to the number of infectious («shine») centers formed on the cell sensor culture, approximately as 100: 1, in the «coronavirus-positive» sample.

Linear MPC Operating Far From Design Condition: Assessment Derived From Experiments

Model Predictive Control (MPC) is a well-known control architecture that has encountered an enormous variety of applications since the very beginning until current days. The pros and cons of such control technique are very well known and both of them rely on the embedded model, which is used to determine the control trajectory. Still, the discrepancy between embedded model and real operative conditions can affect the control response due to uncertainties in measurement chain, noise and so on. Still, it is hardly available in literature what would happen in case the plant is operating far from the design condition of the model. This is of particular interest once a linear MPC is governing a non-linear process where the linearization of the target plant must be processed to tune the MPC itself. This paper analyses experimental results from a fuel cell gas turbine hybrid system, namely SOFC/GT emulator test rig, where a linearized MPC was adopted to control stack inlet temperature. The test rig is constituted by a modified Turbec T100 micro gas turbine where a volume of 4 m3 is interposed between compressor and turbine. This emulates the impact of the SOFC on the GT. The system is connected in real-time mode to a model, which runs in parallel and reads what is going on the plant side and simulates the behavior of the associated SOFC stack. The MPC controller governs the plant according to the stack inlet temperature computed by the model in real-time mode. This MPC must be considered as a supervisor of the system, as the gas turbine was still equipped with its original control system. The plant was subject to an on-purpose strong degradation -operated via a constant venting of air from compressor to ambient. This operation strongly influenced the performance of the system, which were no longer able to operate at a level of temperature and power for which the controller was designed for. Still, a ramping down in power and back up was performed and the MPC showed performance which were in agreement with the design performance. Such surprisingly good result is explained with the complexity of the embedded model, which was derived from a completely physical model of the target system and constituted by more than 200 states.