Optimization of the precision gear grinding operation based on integrated information system

In accordance with the principles of hierarchical management, a comprehensive two-level management system is presented for the development and manufacturing of products for the stages of pre-production (the upper level of the management hierarchy) and for the actual production stage (the lower level of the management hierarchy). At the stage of pre-production, the gear grinding operation design on the “MAAG” type machines was carried out. For this purpose, a technique for optimizing the gear grinding parameters for a two dish-wheel rolling scheme has been developed, a mathematical optimization model containing an objective function with restrictions imposed on it has been created. The objective function is the gear grinding machine time, which depends on the operation parameters (gear grinding stock allowance, cutting modes, grinding wheel specification, part material) and the design features of the gears being ground (module, diameter, number of teeth, radius of curvature of the involutes). The article shows that at the stage of pre-production, the gear grinding optimization is a method of operation design. At the stage of actual production, a closed-loop automatic control system with feedback on the deviation of the adjustable value (gear grinding power) automatically supports the numerical power values that were found at the operation design stage, taking into account ensuring defect-free high-performance gear grinding (minimum number of working strokes and maximum longitudinal feeds). At this stage, i.e. when a robust longitudinal feed automatic control system is operating, the optimization carried out at the previous stage (pre-production) sets the functioning algorithm for the adaptive system with corresponding control algorithm. Thus, at the production stage (when the gear grinding machine is running), the operation optimization is a control method. Therefore, it is shown that with two-level control, the gear grinding operation optimization performs a dual function. On the one hand, it is a design method (at the pre-production stage), and on the other – a management method (at the actual production stage). With this approach, i.e. with the integration of production and its preparation based on a single two-level management, the efficiency of a single integrated design and production automation system is significantly higher due to general (unified) optimization, rather than partial one.

Method for increasing the dynamic characteristics of thermoelectric coolers

The influence of the efficiency of the initial thermoelectric materials on the dynamics of the functioning of the thermoelectric cooling device for various characteristic current modes of operation in the range of operating temperature drops and heat load at a given geometry of thermoelement legs is considered. The parameters of thermoelectric materials of thermoelements are conventionally divided into three groups: used for batch production, laboratory research and maximum values. The criterion for choosing the operating mode of the thermoelectric cooler takes into account the mutual influence and weight of each of the limiting factors. Since the design conditions can be very diverse, simultaneously varying several limiting factors (constructive, energy and reliability), you can choose the most rational mode of operation. The analysis was carried out for typical current modes of operation of thermoelectric coolers: maximum cooling capacity, maximum cooling capacity at a given current, maximum coefficient of performance, minimum failure rate. It is shown that with an increase in the efficiency of the initial thermoelectric materials, the time for reaching the stationary operating mode of the thermoelectric cooler, the required number of thermoelements, and the maximum temperature difference increase. A method is proposed for reducing the time constant of thermoelectric coolers due to the revealed relationship between the efficiency of thermoelectric materials and the dynamic characteristics of thermoelements. It is shown that an increase in the dynamic characteristics of thermoelectric coolers is achieved without changing the design documentation, manufacturing technology and additional climatic and mechanical testing of products.

Control of thermal regime of thermoelectric coolers in uniform temperature field

The comparative analysis of means of control of a thermal mode at minimization of a complex of the basic parameters in various combinations with indicators of reliability and dynamics of functioning of one-stage thermoelectric cooler is resulted. The study was conducted for the operating range of temperature differences, standard heat load and different geometry of the branches of thermocouples. According to the results of research to minimize the sets of basic parameters in interaction with the indicators of reliability and dynamics of work, a number of current modes of operation have been developed. The developed mathematical models for the optimal operating current from the relative temperature difference and heat transfer of the radiator for the proposed operating modes are analyzed. The results of calculations of the main parameters, reliability indicators, and time of transition to stationary mode of operation for different current modes of operation in the range of temperature differences for different geometry of branches of thermoelements are given. The extremes of dependences of the cooling coefficient, heat dissipation capacity of the radiator, the amount of energy consumed on the relative operating current are determined, which is essential for the implementation of the control function. The possibility of choosing the current mode of operation for optimal control of the thermal regime of single-stage thermoelectric devices manufactured by the same technology, taking into account mass, size, energy, reliability and dynamic characteristics. The developed method of optimal regulation of the thermal regime of a single-stage thermoelectric cooler based on minimizing the set of basic parameters allows finding and choosing compromise solutions taking into account the importance of each of the limiting factors.

Modelling the efficiency of power system with reserve capacity from variable renewable sources of energy

The paper considers modeling the efficiency of power system with integration large share of variable renewable sources of energy with the account of climate conditions of Ukraine. The proposed methodology with its position between system planning and dispatch simulation contributes to the field of hybrid energy system models. The idea behind the method allows high spatial and temporal resolution as well as the inclusion of the technical details of the power system and its dispatch. The novelty of this method is the usage of a parametric approach is chosen to analyze different variable renewable sources of energy scenarios, precisely every possible its share and mix. This provides insights on the systematic effects of different resource mixes and may serve as a new approach to the analysis of future power system development. The additional novelty aspect allows the optimization of the design of the technical details of the power system with large variable renewable sources shares to have continuous improvement of its energy efficiency. The energy balance model generator is well suited for the analysis of large share of variable renewable sources integration in the power system. The design of technical details of the power system with large variable renewable sources shares was optimized with the energy balance model. The results of numerical modelling demonstrated that at 80% variable renewable sources of energy share, the overproduction is reduced to 20%, down from over 100 % without grid extensions. With it, the necessary wind and solar capacity decreases. Consequently, the possible achievable variable renewable sources of energy share is increased, assuming the same technical potential. According to the results, a Ukrainian grid would allow to increase the possible variable renewable sources of energy share from 50% to 75%.

Informational aspects at model of power consumption by main drainage facilities of iron-ore mining enterprises

The work investigates into variable informational approaches to modeling power consumption by main drainage facilities of ore mining enterprises with underground mining method. Methodological recommendations for using the models are also designed. The research deals with general methodological approaches to model formation with both power consumption indices for drainage facilities and corresponding costs. Logistics of model formation is substantiated, namely, combination of classic multifactor regression modeling with modern digital modeling methods – automated control systems used for drainage facilities. Principles of building fuzzy logic controllers and algorithms of their functioning under multichannel control are determined in detail. The improved fuzzy logic-based variant is proposed and combined, with correlation analysis, to provide the basis for developing algorithms of the automated control systems of electric power consumption. There is an example of developing a “road map” for implementing a generalized algorithm for automated control systems power flows for two current cases – a selective tariff with limited daily contract-based power consumption and that with a variable tariff. It is established that application of the two-rate hourly tariff with its conditional distribution (Night/Peak) instead of the three-rate tariff (Night/Half-Peak/Peak) on a single-use basis leads to a thirteen percent increase of daily power costs with a single-channel control of the ore flow and a seven percent increase with two-channel control (ore flow and drainage simultaneously). The use of fuzzy logic controllers enables minimizing these losses.

Accelerating the learning process of a neural network by predicting the weight coefficient

The purpose of this study is to analyze and implement the acceleration of the neural network learning process by predicting the weight coefficients. The relevance of accelerating the learning of neural networks is touched upon, as well as the possibility of using prediction models in a wide range of tasks where it is necessary to build fast classifiers. When data is received from the array of sensors of a chemical unit in real time, it is necessary to be able to predict changes and change the operating parameters. After assessment, this should be done as quickly as possible in order to promptly change the current structure and state of the resulting substances.. Work on speeding up classifiers usually focuses on speeding up the applied classifier. The calculation of the predicted values of the weight coefficients is carried out using the calculation of the value using the known prediction models. The possibility of the combined use of prediction models and optimization models was tested to accelerate the learning process of a neural network. The scientific novelty of the study lies in the effectiveness analysis of prediction models use in training neural networks. For the experimental evaluation of the effectiveness of prediction models use, the classification problem was chosen. To solve the experimental problem, the type of neural network “multilayer perceptron” was chosen. The experiment is divided into several stages: initial training of the neural network without a model, and then using prediction models; initial training of a neural network without an optimization method, and then using optimization methods; initial training of the neural network using combinations of prediction models and optimization methods; measuring the relative error of using prediction models, optimization methods and combined use. Models such as “Seasonal Linear Regression”, “Simple Moving Average”, and “Jump” were used in the experiment. The “Jump” model was proposed and developed based on the results of observing the dependence of changes in the values of the weighting coefficient on the epoch. Methods such as “Adagrad”, “Adadelta”, “Adam” were chosen for training neural and subsequent verification of the combined use of prediction models with optimization methods. As a result of the study, the effectiveness of the use of prediction models in predicting the weight coefficients of a neural network has been revealed. The idea is proposed and models are used that can significantly reduce the training time of a neural network. The idea of using prediction models is that the model of the change in the weight coefficient from the epoch is a time series, which in turn tends to a certain value. As a result of the study, it was found that it is possible to combine prediction models and optimization models. Also, prediction models do not interfere with optimization models, since they do not affect the formula of the training itself, as a result of which it is possible to achieve rapid training of the neural network. In the practical part of the work, two known prediction models and the proposed developed model were used. As a result of the experiment, operating conditions were determined using prediction models.

Ways of development of processes of standardization and informatization of the field of healthcare

This article is devoted to the analysis of standardization of informatization of medical care projects. In particular, the task of improving the quality and accessibility of medical services on the basis of a scientifically based methodology of portfolio-oriented management is set. To solve this problem, subtasks have been identified, such as the development of methods for estimating the cost of projects in the field of medical services; the creation of a model for financing healthcare based on the introduction of a system of compulsory state medical insurance; the development of a quality management system for healthcare projects, which includes models and methods for planning, ensuring and controlling the quality of medical services; the development of the foundations of state programs for standardization and informatization of healthcare as the basis of a quality management system. Improving the quality of healthcare is recognized as the main goal of reforming the industry at the present stage. The quality of healthcare is defined as the totality of the results of prevention, diagnosis and treatment of diseases determined by the established requirements based on the achievements of medical science and practice. In the work, based on the analysis of the best world experience, it is shown that the use of the principles of standardization in healthcare provides a high level of medical care, regardless of the patient's place of residence, the level of knowledge of a particular doctor or recommendations of a particular scientific school. The paper proposes the main approaches, methods and components of the standardization system. Using the practical experience of private medical centers an algorithm for creating an electronic medical record, its main functions and possible areas of use are proposed. It is shown that the main difficulty in implementing the standardization of medical care is that this work at the level of public and private clinics is controlled not only by the principle of general standardization and modification, but also by a specific project goal for each medical industry, which should be achieved in a comprehensive and balanced manner.

IMPROVING THE DESIGNING METHOD OF THERMAL NETWORKS: SERIAL CONNECTION OF STREAMS

The paper presents an approach to the design of technical systems, the elements of which are interconnected and carry out an internal exchange of energy. The above analysis showed that for heat-exchange equipment when combining devices into systems, only iterative methods are currently used, a representative of which is Pinch analysis. A limitation of the iterative approach is the impossibility of obtaining an exact solution to such problems, which can only be achieved by analytical methods, which also make it possible to reveal some effects in systems that are practically unavailable for numerical solution. This indicates the absence of a rigorous proof of the existence of a solution and a problem in the construction of approximate solutions, due to the need to involve complementary hypotheses. The topological representation of the system modules allows us to consider the architecture as a network, which contributes to the analysis of the connections between the constituent elements and the identification of their mutual influence. Highlighted the typical connections of network elements such as serial, parallel, contour, which allows to unify the principles of building connections in the system. As an optimality criterion, the NTU parameter was chosen, which includes the heat exchange surface and is usually used when searching for a solution for heat exchangers of moving objects. An analytical solution to the problem of flow distribution and energy exchange efficiency in a system of two series-connected heat exchangers is obtained. His analysis showed that the formulation of the design problem based on the definition of matrix elements in relation to determinants allows not only to meet the requirements for the system, but also to determine the design parameters of its elements that satisfy their extreme characteristics

MODELS AND METHODS FOR DIAGNOSING ZERO-DAY THREATS IN CYBERSPACE

The article is devoted to the development of models and methods for detecting Zero-Day threats in cyberspace to improve the efficiency of detecting high-level malicious complexes that are using polymorphic mutators. The method for detecting samples by antivirus solutions using a public and local multiscanner is proposed. The method for diagnosing polymorphic malware using Yara rules is being developed. The multicomponent service that allows organizing a free malware analysis solution with a hybrid deployment architecture in public and private clouds is described. The cloud service for detecting malware based on open-source sandboxes and MAS, allowing horizontal scalability in hybrid clouds, and showing high capacity during malicious and non-malicious object processing is designed. The main task of the service is to collect artifacts after dynamic and static object analysis to detect zero-day threats. The effectiveness of the proposed solutions is shown. Scientific novelty and originality consist in the creation of the following methods: 1) detecting the sample by preinstalled antivirus solutions that allow static scanning in separate threads without requests restrictions for increasing the malware processing speed and restrict public access to confidential files; 2) diagnosing polymorphic malware using Yara rules, that allows detecting new modifications that are not detected by available solutions. The proposed hybrid system architecture allows to perform a retrospective search by families, tracking changes in destructive components, collect the malicious URLs database to block traffic to C&C servers, collect dropped and downloaded files, analyze phishing emails attachments, integrate with SIEM, IDS, IPS, antiphishing and Honeypot systems, improve the quality of the SOC analyst, decrease the incidents response times and block new threats that are not detected by available antivirus solutions. The practical significance of the results is in the cloud service development that combines MAS Sandbox and a modified distributed Cuckoo sandbox, which allows to respond to Zero-Day threats quickly, store a knowledge base for artifacts correlation between polymorphic malware samples, actively search for new malware samples and integrate with cyber protection hardware and software systems that support the Cuckoo API.

DEEP LEARNING TECHNOLOGY FOR VIDEOFRAME PROCESSING IN FACE SEGMENTATION ON MOBILE DEVICES

The aim of the research is to reduce the frame processing time for face segmentation on videos on mobile devices using deep learning technologies. The paper analyzes the advantages and disadvantages of existing segmentation methods, as well as their applicability to various tasks. The existing real-time realizations of face segmentation in the most popular mobile applications, which provide the functionality for adding visual effects to videos, were compared. As a result, it was determined that the classical segmentation methods do not have a suitable combination of accuracy and speed, and require manual tuning for a particular task, while the neural network-based segmentation methods determine the deep features automatically and have high accuracy with an acceptable speed. The method based on convolutional neural networks is chosen for use because, in addition to the advantages of other methods based on neural networks, it does not require such a significant amount of computing resources during its execution. A review of existing convolutional neural networks for segmentation was held, based on which the DeepLabV3+ network was chosen as having sufficiently high accuracy and being optimized for work on mobile devices. Modifications were made to the structure of the selected network to match the task of two classes segmentation and to speed up the work on devices with low performance. 8-bit quantization was applied to the values processed by the network for further acceleration. The network was adapted to the task of face segmentation by transfer learning performed on a set of face images from the COCO dataset. Based on the modified and additionally trained segmentation model, a mobile app was created to record video with real-time visual effects, which applies segmentation to separately add effects on two zones - the face (color filters, brightness adjustment, animated effects) and the background (blurring, hiding, replacement with another image). The time of frames processing in the application was tested on mobile devices with different technical characteristics. We analyzed the differences in testing results for segmentation using the obtained model and segmentation using the normalized cuts method. The comparison reveals a decrease of frame processing time on the majority of devices with a slight decrease of segmentation accuracy.