The Object According State Prediction to Diagnostic Data

The predicting methodology the state of the object based on diagnostic data is considered. With the selected parameter that determines the state of the object, it is measured in real time at a fixed sampling step. According to the measurement data, the value of this parameter is predicted in the future. This operation is implemented by an extrapolator of the l order - a l degree polynomial, built using the least squares method based on the previous measurements results. The changing process model of the diagnosed parameter is a random time function described by the stationary centered random component sum and a mathematical expectation deterministic change. The estimating prediction error method and the extrapolator parameters influence on its value are presented.

Multi-Step Predictions for Adaptive Sampling using Proximal ADMM

<p>The paper presents a novel approach by using multi- step predictions to address the adaptive sampling problem in a resources and obstacles constrained mobile robotic sensor network to efficiently monitor environmental spatial phenomena. It is first proposed to employ the Gaussian process (GP) to represent the spatial field, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, a conditional entropy based optimality criterion is proposed, which aims to minimize prediction uncertainty of the GP model. By predicting possible measurements the mobile sensors potentially take in a horizon of multiple sampling steps ahead and exploiting the chain rule of the conditional entropy, a multi-step predictions based adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead, which then provides their benefits in terms of better navigation, deployment and data collection with more informative sensor readings. However, the optimization problem is nonconvex, complex, constrained and mixed-integer. Therefore, it is proposed to employ the proximal alternating direction method of multipliers algorithm to efficiently solve the problem. More importantly, the solution obtained by the proposed approach is theoretically guaranteed to be converged to a stationary value. Effectiveness of the proposed algorithm was extensively validated by the real- world dataset, where the obtained results are highly promising.</p>

Electromagnetic research in the Altai-Sayan mountain region

A new software-measuring complex has been developed for recording non-stationary electromagnetic soundings based on mobile modules created on the basis of 24-bit ADCs, with built-in GPS receivers, with recorders located directly at the field sensors, arithmetic sampling step and recording of all signal realizations. The deep structure of the Uimon depression in Gorny Altai has been studied based on the time-domain electromagnetic sounding. Research is relevant due to the high seismic hazard of the area, and is also in demand for prospecting and exploration of predicted minerals here. To construct geoelectric models, data from several years of measurements were used, during which more than 60 soundings were performed. At this stage, the interpretation was performed using computer systems within the framework of a horizontally layered model. The interpretation results are presented in the form of sections and threedimensional visualizations, which clearly reflect the structure of the depression. Further, three-dimensional modeling and additional measurements are planned to verify and refine the results obtained.

Multi-Step Predictions for Adaptive Sampling using Proximal ADMM

<p>The paper presents a novel approach by using multi- step predictions to address the adaptive sampling problem in a resources and obstacles constrained mobile robotic sensor network to efficiently monitor environmental spatial phenomena. It is first proposed to employ the Gaussian process (GP) to represent the spatial field, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, a conditional entropy based optimality criterion is proposed, which aims to minimize prediction uncertainty of the GP model. By predicting possible measurements the mobile sensors potentially take in a horizon of multiple sampling steps ahead and exploiting the chain rule of the conditional entropy, a multi-step predictions based adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead, which then provides their benefits in terms of better navigation, deployment and data collection with more informative sensor readings. However, the optimization problem is nonconvex, complex, constrained and mixed-integer. Therefore, it is proposed to employ the proximal alternating direction method of multipliers algorithm to efficiently solve the problem. More importantly, the solution obtained by the proposed approach is theoretically guaranteed to be converged to a stationary value. Effectiveness of the proposed algorithm was extensively validated by the real- world dataset, where the obtained results are highly promising.</p>

Multi-Step Predictions for Adaptive Sampling using Proximal ADMM

<p>The paper presents a novel approach by using multi- step predictions to address the adaptive sampling problem in a resources and obstacles constrained mobile robotic sensor network to efficiently monitor environmental spatial phenomena. It is first proposed to employ the Gaussian process (GP) to represent the spatial field, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, a conditional entropy based optimality criterion is proposed, which aims to minimize prediction uncertainty of the GP model. By predicting possible measurements the mobile sensors potentially take in a horizon of multiple sampling steps ahead and exploiting the chain rule of the conditional entropy, a multi-step predictions based adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead, which then provides their benefits in terms of better navigation, deployment and data collection with more informative sensor readings. However, the optimization problem is nonconvex, complex, constrained and mixed-integer. Therefore, it is proposed to employ the proximal alternating direction method of multipliers algorithm to efficiently solve the problem. More importantly, the solution obtained by the proposed approach is theoretically guaranteed to be converged to a stationary value. Effectiveness of the proposed algorithm was extensively validated by the real- world dataset, where the obtained results are highly promising.</p>

Increasing the noise immunity of radio receiving paths with automatic sensitivity control

The relevance of the study of automatic sensitivity control systems (ASC) is determined by their demand for the creation and modernization of radio receiving paths (RRP) with increased noise immunity for radar systems, radio navigation and radio communication. The article analyzes typical attenuating ASCs, which are traditionally widely used to match the dynamic range (DR) of the RRP with the DR of a group radio signal, determined by the current state of the electromagnetic environment at the receiving system location. The fundamental possibility of increasing the noise immunity of RRPs with attenuating ASCs is shown on the basis of the current analysis of the resulting output signal in the IF main filter band. At the same time, it was found that the procedure for determining the optimal value of the attenuator transmission coefficient is characterized by low response speed. In addition, an increase in noise immunity in a RRP with such ASC leads to a significant loss of sensitivity. To overcome the disadvantages of attenuating ASCs, structures that implement the exchange of the transmission coefficient of the RRP to DR and linearity are proposed. Studies of various possible ASC structures have shown that with a proportional exchange of the transmission coefficient for the DR, an improvement in the noise immunity of the RRP is provided while maintaining a high sensitivity of the system. An original ASC system is proposed, which is invariant to the sampling step of the transmission coefficients of controlled elements with increased performance. The considered structural solutions and algorithms make it possible to optimize the technical appearance of RRPs for radar, radio navigation and radio communication with increased noise immunity and to adapt their characteristics to the conditions of nonstationary electromagnetic environment.

Solving the Coriolis Vibratory Gyroscope Motion Equations by Means of the Angular Rate B-Spline Approximation

The exact solution of the movement equation of the Coriolis vibratory gyroscope (CVG) with a linear law of variation of the angular rate of rotation of the base is given. The solution is expressed in terms of the Weber functions (the parabolic cylinder functions) and their asymptotic representations. On the basis of the obtained solution, an analytical solution to the equation of the ring dynamics in the case of piecewise linear approximation of an arbitrary angular velocity profile on a time grid is derived. The piecewise linear solution is compared with the more rough piecewise constant solution and the dependence of the error of such approximations on the sampling step in time is estimated numerically. The results obtained make it possible to significantly reduce the number of operations when it is necessary to study long-range dynamics of oscillations of the system, as well as quantitatively and qualitatively control the convergence of finite-difference schemes for solving the movement equations of the Coriolis vibratory gyroscope.

Development of the Analysis Procedure of the Efficiency of Methods for Digital Filtering of the Incoherent Scatter Signal

The article considers the methodological features of creating procedures that help in the implementation of effective methods of recognition and suppression in the input signal, which are recorded by the incoherent scatter radar, noise and impulse interference. It is known that the frequency band of the input signal depends on the observation period of the ionosphere, as well as on its height above the Earth’s surface. Therefore, approaches to using analog filters with time-controlled characteristics in a radar receiver are being practiced. These filters, however, require a priori information about of the ionosphere state. At the present time, it is possible to obtain digital samples of a signal with a very small sampling step (tens of thousands of samples during a radar range sweep) using high-speed analog-to-digital conversion. It is also possible to record these readings in the computer memory relative to each sweep. This gives the prospect, based on the results of the experiment, to carry out auxiliary adaptive digital filtering of the obtained data. In this case, filter properties are selected for each altitude range in order to obtain the maximum possible signal-to-noise ratio. In order to introduce high-quality digital filtering methods, this development is aimed at creating a special software-algorithmic procedure, which allows you to control the effectiveness of the proposed filtration methods. The essence of this procedure is that according to the amplitude-frequency and phase-frequency spectra set by the researcher, the corresponding model of the scattering signal is synthesized and its autocorrelation function is calculated. These characteristics in the following steps as reference are used for comparison with similar characteristics, but obtained from the distorted scattering signal. The experimenter has the ability to apply noise and impulse interference to the scattered signal and to test the digital filtering method he proposed. All these steps in software implementation are accompanied by a clear graphical visualization of the results obtained.

ANALYSIS OF THE STATE OF AIRCRAFT CREW MEMBER BY THE SPEECH USING GAUSSIAN MODELS OF MIXTURES

The work is devoted to the study of the effectiveness of the application of models of Gaussian mixtures for the recognition of abnormal deviations in the speaker's speech. The practical application of the developed algorithms for revealing the emotional state of the crew member by the phrase uttered by such crew member is proposed. The spectral characteristics of the speech signal are used as the main criterion for distinguishing using the Gaussian mixture model. In connection with a rather small sampling step in frequency and, accordingly, with the presence of 255 frequency components in the signal spectrum, it is proposed to compress the spectrum to 10 components. This approach made it possible to reduce the number of key parameters in the Gaussian model to 10, which, in turn, made it possible to simplify the analysis process when constructing multivariate distributions. To assess the quality of the proposed algorithm, test phrases were recorded. At the same time, various psychological states of the speaker were imitated. We used both simple unregulated speech structures and messages regulated in accordance with the Federal Aviation Rules when conducting radio exchange in civil aviation on the territory of the Russian Federation. Taking into account the limitations on the prior knowledge of the model and clustering by spectral characteristics, all recordings of the model were made by one speaker. Three classes of the speaker's emotional state were considered. At the output, the recognition system put such marks as a calm state, a tired state, a stressful state. Various states were artificially simulated during data preparation. On a test sample of 48 messages, a Gaussian model of 3 components and 10 parameters without preliminary training immediately allowed to achieve a result of about 65%, while the probability of recognizing the correct class with 3 equal classes a priori is 33%. As further research, it is proposed to apply preliminary training using neural networks or correlation algorithms. This approach will allow further clustering at a deeper level, when, for example, the gender of the speaker is determined, a typical message of the radio exchange is determined, and then the emotional state of the speaker is revealed.

Effects of environmental parameters on the nycthemeral migratory behaviour of copepod species in the Al Massira reservoir, Morocco

To improve our knowledge of nocturnal and diurnal migratory activity in copepods and in order to assess the effect of abiotic parameters on this activity, we have undertaken at the level of the Al Massira dam reservoir (dam located on the wadi Oum Erbia, in Morocco) the monitoring of the migration of the main species, during a 24-hour nycthemeral cycle. To be done; the measurement and sampling step is carried out every 4 hours; at different depths of the prospected station. The temperature, pH, dissolved oxygen and chlorophyll a measurements are taken. The systematic position of the main species of copepods and their density are determined. Most of the results obtained show that the main species of copepods inventoried within the reservoir are Neolovenula alluaudi and Acanthocyclops robustus. The nauplii of these two species present the maximum of densities at a depth of -5 m at 12 h; i.e. 70400 individual / m3 (ind / m3). The development stages of N. alluaudi are preferentially concentrated at -2 m at midnight, with 1900 ind / m3, while at 12 h and 16 h this density is less than 500 ind / m3. The maximum density for the stages of A. robustus is noted at midnight on the surface (at -5 m), i.e. 3400 ind / m3 for C1-2 and 3200 ind / m3 for stages C3-4-5 at -2 m at the same time. Therefore, the migratory behavior of these species seems to depend on the temperature, the concentration of dissolved oxygen and the variation of the food during the different phases of the cycle.