UNORGANIZED AIR RELEASES WITH RADIOACTIVE AEROSOLS FROM THE NEW SAFE CONFINIMENT OF CHNPP INTO THE ENVIRONMENT

The New Safe Confinement (NSC) of the Chernobyl NPP, which isolates the destroyed reactor and the “Shelter Object” from the environment, is not airtight, so the problem is the lack of information on the flow of unorganized air with radioactive aerosols outside the NSC. This work presents computational model of the hydraulic state of the NSC, which allows to determine these flow rates through the leaks in the shells and building structures under the walls of the NSC. In addition to the developed model, the NSC hydraulic state model, created by neural network technology, was tested, which showed similar results and much higher computational performance, which allows its use for analysis and prediction of NSC`s hydraulic state in real time.

Satellite to Ground Station, Attenuation Prediction for 2.4-72GHz Using LTSM, An Artificial Recurrent Neural Network Technology

Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. Satellite communication services became recently attractive to mega-companies that foresee an excellent opportunity to connect disconnected remote regions, serve emerging machine-to-machine communication, Internet-of-things connectivity, and more. Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. In addition, when signals approach the ground station, it has to overcome buildings blocking the direct access to the ground station. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes LTSM, an artificial recurrent neural network technology that provides a time-dependent prediction of the expected attenuation level due to rain and fog and the signal strength that remained after crossing physical obstacles surrounding the ground station. The satellite transmitter is calibrated accordingly. The satellite outgoing signal strength is based on the predicted signal strength to ensure it will remain strong enough for the ground station to process it. The instant calibration eliminates the excess use of energy resulting in energy savings.

Modeling of Seasonal Variations of Thermal Energy Production by an Electric Power Company based on Neural Network Technology

The paper considers the problem of modeling the dependence of the value of thermal energy production by an electric power company on the air temperature using neural network technology. As an example of an electric power company producing thermal energy, the Public Joint-Stock Company (PJSC) Yakutskenergo. As consumers of thermal energy, organizations, enterprises and the population of the city of Yakutsk, are located at latitude 62 and characterized by a cold northern climate. The numerical experiments carried out in this paper have shown that the general trend of the temperature dependence of thermal energy production, observed empirically, is well described by a neural network

Evaluation Method of Reservoir Heterogeneity Based on Neural Network Technology

Reservoir is the underground storage and accumulation place of oil and natural gas. The accuracy of reservoir heterogeneity evaluation has great economic value for correctly guiding the production and development of oil and natural gas. The high-order neural network method is used to comprehensively evaluate the heterogeneity of the reservoir. This method was applied to the evaluation of reservoir heterogeneity in the PK area. The results show that the heterogeneity of sandy clastic flow sand bodies is the weakest, the sandy landslide sand bodies are medium, and the turbidity current sand bodies are strongest. The evaluation method of reservoir heterogeneity based on high-order neural network technology effectively solves the problem of inconsistent conclusions of single-parameter evaluation of heterogeneity in conventional methods, and can quantitatively characterize the degree of reservoir heterogeneity.

Application of Machine Learning Algorithm Based on Neural Network Technology

Neural networks have strong characteristics for processing data and information. At the same time, the current computer technology is also very advanced, and many kinds of very powerful information technologies have been developed under the promotion and promotion of modern science and technology. Therefore, relevant personnel will carry out advanced technology and neural network structure methods. Fusion, and then an artificial neural network was established on this basis. In a broad sense, machine learning refers to how to enable a machine to acquire relevant knowledge through autonomous learning, and the purpose is to enable the machine to have relevant skills similar to what people need to acquire knowledge. The research in this article aims to explore the machine learning algorithms based on neural network technology, and through literature research methods, case analysis methods, etc., to have an in-depth understanding of machine learning algorithms in neural network technology, and then through the analysis of machine learning algorithms in neural network technology The learning advantage and its influencing factors are designed based on the machine learning algorithm in the neural network and experimented. Experimental results show that LSTM performs well in replication tasks, and the performance of LSTM even far exceeds that of NTM, but the performance of LSTM in addition and multiplication tasks is much lower than that of NTM. Although the accuracy of NTM on the test set is higher than that of LSTM and RNN, the performance of the model is still relatively poor.

Organizing Interactions Between a Robotic Manipulator and a Technological Equipment Control Panel

Algorithms for the organization of standalone operation of a robotic manipulator have been considered in this Article. The controls on the control panel of a man-made equipment piece have been the manipulated object. A robotic manipulator fitted with a control system, and a machine vision system, have been the subsystems under study. The robotic manipulator interacts with the objects to switch on and off a toggle switch, turn a rotary switch, press on a pushbutton, etc. Manipulating the objects in question involves assessing of their positions and orientations, which is achieved through the machine vision system. Coordinated operation of the subsystems is required to plan and to implement control of the manipulator, while taking into account positions of the objects relative to each other. A configuration of interactions between the manipulator and the control panel, a method for organizing coordinated functioning of the machine vision system when manipulating controls, which includes assessing positions of the object of interest relative to the robotic manipulator, and an algorithm for planning control based on artificial neural network technology have been presented in this Article.

Recovering Information from Wireless Sensors in Hardware and Software Platforms

The work considers the task of information processing in a subsystem of the hardware-software platform of the simulator complex - a mobile system of simulating isolation breathing apparatuses. The problem of predicting values when data packets are lost during their wireless transmission has been revealed. To solve the problem, an algorithm for data processing based on neural network technology has been developed, which allows reducing the number of data packet losses by predicting the lost values. The experimental studies confirmed the adequacy and effectiveness of the proposed algorithm. The use of neural networks in solving the problems of information processing has improved the accuracy of this process.

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.