Hydrogen energy is able to solve the problem of the dependence of modern industries on fossil fuels and significantly reduce the amount of harmful emissions. One of the ways to produce hydrogen is high-temperature water-steam electrolysis. Increasing the temperature of the steam involved in electrolysis makes the process more efficient. The key problem is the use of a reliable heat energy source capable of reaching high temperatures. High-temperature gas-cooled reactors with a gaseous coolant and a graphite moderator provide a solution to the problem of heating the electrolyte. Part of the heat energy is used for producing electrical energy required for electrolysis. Modern electrolyzers built as arrays of tubular or planar electrolytic cells with a nuclear energy source make it possible to produce hydrogen by decomposing water molecules, and the working temperature control leads to a decrease in the Nernst potential. The operation of such facilities is complicated by the need to determine the optimal parameters of the electrolysis cell, the steam flow rate, and the operating current density. To reduce the costs associated with the process optimization, it is proposed to use a low-temperature electrolysis system controlled by a spiking neural network. The results confirm the effectiveness of intelligent technologies that implement adaptive control of hybrid modeling processes in order to organize the most feasible hydrogen production in a specific process, the parameters of which can be modified depending on the specific use of the reactor thermal energy. In addition, the results of the study confirm the feasibility of using a combined functional structure made on the basis of spiking neurons to correct the parameters of the developed electrolytic system. The proposed simulation strategy can significantly reduce the consumption of computational resources in comparison with models based only on neural network prediction methods.

ПРОГРАММНЫЙ КОМПЛЕКС ОЦЕНКИ ЗАПАСА РЕАКТИВНОСТИ РЕАКТОРА ВВР-ЦИ.П.Белявцев, С

The WWR-c reactor reactivity margin can be calculated using a precision reactor model. The precision model based on the Monte Carlo method (Kolesov et al. 2011) is not well suited for operational calculations. The article describes the work on creating a software package for preliminary evaluations of the WWR-c reactor reactivity margin. The research has confirmed the possibility of using an artificial neural network to approximate the reactivity margin based on the reactor core condition. Computational experiments were conducted on training the artificial neural network using the precision model data and real reactor measured data. According to the results of the computational experiments, the maximum relative approximation error ∆k/k for fuel burnup was 3.13 and 3.56%, respectively. The mean computation time was 100 ms. The computational experiments showed it possible to construct the artificial neural network architecture. This architecture became the basis for building a software package for evaluating the WWR-c reactor reactivity margin – REST API based web-application – which has a convenient user interface for entering the core configuration. It is also possible to replenish the training sample with new measurements and train the artificial neuron network once again. The reactivity margin evaluation software is ready to be tested by the WWR-c reactor personnel and to be used as a component of the automated reactor refueling system. With minor modifications, the software package can be used for reactors of other types.

Two artificial neural networks approximating the criticality margin of the WWR-c reactor (based on model and calculated data) were created and trained. The resulting neural networks realize the correct approximation, have high accuracy, and also high speed of operation. Using the obtained artificial neural networks can be applied to accelerate the preliminary calculations of the state of the reactor.

A hardware-software complex of in-depth studies of hexapod walking robot including the hexapod mockup and its simulator is described. Possible applications of the complex for the study of statically stable hexapod gaits and obstacle clearance algorithms are considered. An important feature of the simulator is the ability to work in real time that allows you to use it in the control loop.

Reactors with heavy water coolants and moderators have been used extensively in today's power industry. Monitoring of the moderator condition plays an important role in ensuring normal operation of a power plant. A cellular neural network, the architecture of which has been adapted for hardware implementation, is proposed for use in a system for prediction of the heavy water moderator temperature. A reactor model composed in accordance with the CANDU Darlington heavy water reactor design was used to form the training sample collection and to control correct operation of the neural network structure. The sample components for the adjustment and configuration of the network topology include key parameters that characterize the energy generation process in the core. The paper considers the feasibility of the temperature prediction only for the calandria's central cross-section. To solve this problem, the cellular neural network architecture has been designed, and major parts of the digital computational element and methods for their implementation based on an FPLD have also been developed. The method is described for organizing an optical coupling between individual neural modules within the network, which enables not only the restructuring of the topology in the training process, but also the assignment of priorities for the propagation of the information signals of neurons depending on the activity in a situation analysis at the neural network structure inlet. Asynchronous activation of cells was used based on an oscillating fractal network, the basis for which was a modified ring oscillator. The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated. A comparative analysis of the model behavior and the results of the neural network operation have shown that the use of the neural network approach is effective in safety systems of power plants.

This study investigated the method of semantic image analysis by using a set of neuron-like detectors of foreground objects. This method is intended to find different types of foreground objects and to determine properties of these objects. As a result of semantic analysis the semantic descriptor of the image is created. The descriptor is a set of foreground objects of the image and a set of properties for each object. The distance between images is defined as distance between their semantic descriptors. Using the concept of distance between images, "semantically similarity" between images or videos is defined.

Detection of moving objects in a video stream received from a moving camera is difficult computer vision task, because the motion of the camera blends with the motion of the objects in the scene. In order to tackle this problem, we propose a method based on optical flow calculation and Delaunay triangulation. Given a sequence of frames, firstly, we extract the corner feature points using ORB algorithm and compute optical flow vectors at the extracted feature points. Secondly, we separate the optical flow vectors using K-Means clustering method. Third, we classify each cluster into camera and object motion using its mean scatter value. Finally, we represent the moving object using Delaunay triangulation.

In this work the authors present a thorough experimental study of a practical realization of a complex analog signal transmission system using dynamic chaos. It is demonstrated that the chaotic synchronous response could be used as a basis for the design of secure communication channels. The results presented in this work confirm the possibility of secure wireless communications in RF band, while they allow the authors to analyze in detail the restrictions and problems connected with the quality of synchronization of the transmitter and the receiver of the wireless communication systems. The effect of the perturbing factors on the transmission quality is investigated theoretically. It is shown that the main reason of the transmission’s quality degradation is the chaotic response desynchronization associated with the phenomenon of “on-off” intermittency. It is found that under the effect of the perturbing factors, the level of information signal fed to the transmitter must be increased in order to obtain qualitative information transmission. However, in order to provide secure communication, one must decrease the information signal level. A compromise on these contradictory requirements provides an improvement of the quality of the synchronous chaotic response in the receiver.

Performance of wireless communication scheme with ultrawideband chaotic signals in the multipath channel is evaluated. We consider channel models based on multipath channel models elaborated by IEEE 802.15 working group for high bitrate (IEEE 802.15.3a channel model) and low bitrate (IEEE 802.15.4a channel model) ultrawideband wireless communication systems. Path loss and waveform of chaotic radio pulses propagating over multipath channel are experimentally measured as characteristics determining the achievable distance range between transmitter and receiver and achievable bitrates in the presence of additive Gaussian noise.

The problem of indoor multipath propagation of ultrawideband signals is considered for direct chaotic communications systems. The values of the guard interval between the information symbols, which provides the quality of reception at a level of P err ∼ 10−3 to 10−5, are estimated theoretically. The estimation procedure is based on the models of the multipath channels of wireless area networks that have been proposed by the IEEE workgroup.

Basic ideas and concepts underlying the technology of ultrawideband wireless direct-chaos communications are presented. The factors of crucial importance for the development of this communications platform are considered. The models for the origin of chaos in different frequency bands are discussed. Effects of multipath propagation on ultrawideband chaotic signals are analyzed. The results of the experimental study of direct-chaos transmitters/receivers are presented.

Изложены основные идеи и принципы технологии сверхширокополосной беспроводной прямохаотической связи. Рассмотрена совокупность вопросов, оказывающих решающее влияние на развитие этой коммуникационной платформы. Обсуждаются модели источников хаоса в различных частотных диапазонах. Проведен анализ влияния многолучевого распространения сверхширокополосных хаотических сигналов. Приведены результаты экспериментального исследования прямохаотических приемопередатчиков.

The models of indoor multipath propagation of wideband and ultrawideband (UWB) signals are considered. Application of these models is recommended by an IEEE working group. In the framework of one of the models corresponding to the line-of-sight conditions, the algorithms for reception of UWB chaotic signals in the presence of reflection from multiple surfaces (echo signals) are proposed and analyzed. The efficiency of reception techniques is estimated from the viewpoint of the error probabilities. Limitations caused by the specific properties of echo signals are revealed, and the methods for improvement of reception quality are discussed.

Phase synchronization of a receiver to consecutive data batches either with or without an ultrawideband chaotic pulse at the beginning is considered in application to the problem of indoor communication. The probability that a received and a reference sequence are synchronized with an accuracy to one sampling interval At is estimated without using the conventional assumption of error smallness that validates the use of the Gaussian error distribution. In this work, the probability of synchronization error is estimated as the probability of erroneous discrimination between two possible variants (the zero error and an error by one sampling interval). This approach was first applied by V.A. Kotel'nikov for analyzing the erroneous estimates of a delay in deterministic signals with known phases at a high level of interference.