Journal archive

This work is devoted to the study of pricing issues for obtaining maximum profit when selling consumer goods at a constant purchase price. The said goods come in from either manufacturers or warehouses where the retail companies buy the goods in order to sell them directly to the consumers. The dependence of the selling rate per unit of time on the level of the added price in relation to the purchase price of the item is established by the means of sales price variation. The object of the research is the specific case of a linear approximation of said dependence, which is usually actualized in the event of either more elastic or less elastic demand for goods, when they are sold through Internet platforms. The proposed approach to determining prices of all the goods which are being sold for maximizing the total profit from the sales of all consumer goods or maximizing the total revenue throughout the whole period of sales time, based on the search of extremum points of the profit and revenue functions for each item of goods remains valid in the case of more complex approximations by quadratic and cubic functions of demand function. The type of the function of maximum value added revenue and the type of the function of maximum profit can be both found per unit of time depending on the variable level of the added price included into the sales price of the item. The type of maximum revenue function can be found per unit of time depending on the sales price of the item. The extremum points of the found functions are being determined. The theorems have been proved, that the extremum points which are being determined appear to be the maximum points of the researched functions for each item of goods, when the maximum profit or the maximum revenues are reached by selling goods to consumers. All common variables of said functions are found by summing up these functions among the multitude of goods on the interval of the whole sales time. The received data is used for the practical implementation of an effective sales strategy that ensures maximum profits for companies specializing in direct sales to consumers of the purchased goods. An applied methodicalэф approach to the sales of goods which ensures maximum profit from the sales in the field of elastic demand approximated by a linear function and under the condition of a constant purchase price for goods is proposed and theoretically substantiated.

In IIoT (Industrial Internet of Things) systems designed for enterprise management in real time, it is required to perform operational and intelligent processing of Big Data and issue a control signal to the actuators in a predictable time (on the order of several milliseconds). The high speeds of Big Data continuously generated by sensors of the industrial Internet of Things system make it difficult to obtain a control effect at a predictable time. The purpose of the study is to develop the architecture of a complex of IIoT systems to obtain a control effect at a predictable time in real time. The central issue of the task is the high-speed processing of structured data at the place of their occurrence to solve the contradiction between a large number of continuously generated necessary data and the need to process them at a predictable time. The decomposition of the IIoT system into separate IIoT systems according to the structures of the data used by them, followed by synthesis into a single complex of enterprise IIoT systems, is applied. The developed architecture of the IIoT system complex makes it possible to effectively implement distributed management of production processes in a predictable time, perform operational and intelligent processing of huge amounts of data of various formats continuously generated by industrial facilities. The complex of IIoT systems consists of separate systems of the industrial Internet of Things, each of which has its own structure of transmitted data and is implemented on the basis of a multi-level bus, which provides a high data transfer rate in a structured form, the ability to attach to the bus any IIoT device and any program used, including the Big Data system to identify hidden patterns in the work of the enterprise. The proposed solution of the architecture of the IIoT system complex based on intelligent sensors and touchsensors allows for effective management of enterprise equipment and technological process operations in real time with the immediate use of the new patterns found in the continuously incoming new data. The solution can be used by developers of industrial Internet of Things systems for the effective launch and implementation of projects, for the development and commissioning of IIoT systems.

The article presents the results of a study of the problem of structural synthesis of a vision system and its parametric identification using a new method based on the mathematical apparatus of the theory of modified descriptive image algebras. The theory of modified descriptive image algebras is a mathematical apparatus that allows one to formally describe the processing and analysis of images. In this mathematical apparatus, it is possible to describe the mathematical model of the measurement function of the technical vision system for the selected attribute of the observed object. To develop mathematical models, procedural and parametric transformations of images are used. Any mathematical model in the theory of modified descriptive image algebras has at least one variational parameter. In the course of parametric identification, it is required to calculate their values. This problem is multimodal and always has at least one solution. Numerical methods are usually used to solve the optimization problem. The article describes the algorithm for constructing a mathematical model for measuring the area using procedural and parametric transformations. The parametric identification problem is solved in the form of a nonlinear optimization problem. The visualization of the objective function has been carried out and recommendations for choosing the values of its variational parameters have been formulated. The collection of statistical data was carried out and a histogram was constructed, on the basis of which the distribution law for the measured value is selected. The statistical task of testing the hypothesis with the selected law of distribution of the general population according to the Pearson criterion is solved for a given level of significance. For the unknown parameters of the chosen distribution law, the estimation of confidence intervals was carried out. The materials of the article are applied in nature and have practical value. Using the proposed approach, it is possible to develop a measurement function for any feature of the observed object on a series of images.

The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented.

The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes based on the results of modeling heat and mass transfer processes occurring in various zones of the roasting conveyor machine. The impossibility of carrying out expensive full-scale experiments leads to the need to use computer simulation methods. Nonlinearity, large dimension of the search space, high computational complexity make it difficult to use traditional deterministic search methods. Under these conditions, the stochastic methods that deliberately introduce an element of randomness into the search algorithm show good results. Today, population algorithms based on modeling the collective behavior of living organisms and characterized by the ability to simultaneously process several options have become widespread. To solve the optimization problem, it is proposed to use a modified Cuckoo search algorithm (by introducing fuzzy elements), which provides a comprehensive account of a huge number of parameters set for each vacuum chamber of the roasting conveyor machine. The control of the chemical-energy-technological system for the processing of apatite-nepheline ores waste, taking into account the obtained data and based on the existing neural network model of the high-temperature process, will make it possible to minimize the amount of return and provide energy-saving conditions for the operation of roasting units.

Significant interest in the field of application of machine learning for the analysis of medical images stimulates the search for promising algorithms for solving routine diagnostic problems in cardiology. In relation to cardiovascular diseases, such a procedure is coronary angiography, which assesses the state of the vascular network and the presence of stenotic areas. This paper demonstrates an example of using modern models of neural networks: SSD MobileNet V2, SSD ResNet-50, Faster-RCNN Inception ResNet for localizing a single-vessel coronary artery lesion on a set of clinical data (3200 images). It is shown that the Faster-RCNN Inception ResNet V2 model was the most accurate in terms of the chosen metric mAP[0.5:0.95], reaching 0.9434 and 0.95 for the validation and test sets, respectively. However, the data processing speed was 0.363 seconds per frame, which corresponds to a speed of 2.8 frames/sec, which does not correspond to the speed of coronary angiography (15 frames/sec). Neural networks with a more “simple” architecture demonstrated an unsatisfactory quality of stenosis localization, expressed in a low characteristic mAP[0.5:0.95]. The results of this study demonstrate a key problem in the application of machine learning algorithms on graphic data – high accuracy, which may be acceptable for medical diagnostic procedures, is “decompensated” by long-term image analysis, as a result, the use of unmodified neural network architectures does not provide real-time data processing.

Machine learning methods are currently widely used to solve various production problems, the problems of defects diagnosing and predicting for items in mass production, in particular. One of the most important problems is defects diagnosing and predicting, basing on its solution the regulations for the technological processes parameters and raw materials used can be determined, that insures the minimum probability of defects and the highest possible quality of manufactured products. The solution of this urgent problem with the help of a neural network model is shown on the example of the technological process for manufacturing products from fine ore material. The proposed model is based on the neural network trained on the set of historical data including examples of manufacturing products with different sets of technological parameters and raw ore material. The predicted parameter is warping of the product in one of its sections. Designing and training of the proposed neural network structure allowed achieving the coefficient of determination R2 between the predicted and actual warpage values of 92%. The dependences for the warpage value on the most significant parameters of the technological process, including thermophysical and chemical power technological processes of raw materials processing were constructed by conducting computer experiments using the method of partial freezing for input parameters. Due to these dependencies, the regulations for the most significant parameters of the production process are determined, which ensures the product to be without violating the tolerance for the warpage value specified by the design documentation. Thus, a specific example shows the possibility of using neural network modeling to solve the problem of setting regulations for the production process parameters, which compliance ensures the minimum amount of rejects and, accordingly, a higher quality of a production batch.

Anomaly detection is an important task in various applications and areas of technology and production, such as structural defects, malicious intrusions into management and control systems, financial supervision and risk management, digital health screening, etc. The ever-increasing flows of diverse data and their structural complexity require the development of advanced approaches to their solution. In recent years, deep learning methods have achieved significant success in detecting anomalies, and unsupervised deep learning methods have become especially popular. Methods of anomaly detection by methods of deep learning without a teacher are investigated in the work on the example of a set of electrocardiograms containing normal ECG signals and ECG signals of people with various cardiovascular diseases (anomalies). To detect abnormal electrocardiograms, an autoencoder model has been developed in the form of a deep neural network with several fully connected layers. Also, to solve this problem, a method is proposed for selecting the threshold for separating abnormal ECG signals from normal ones, consisting in optimizing the ratio of performance indicators of the autoencoder model by methods. The paper presents a comparative analysis of the effectiveness of applying various machine learning models, such as the one class Support Vector Method, Isolation Forest, Random Forest and the presented autoencoder model to solving the problem of detecting abnormal ECG signals. For this purpose, metrics such as accuracy, recall, completeness, and f-score were used. His results showed that the proposed model surpassed the other models in solving the problem with accuracy = 98.8% precision = 95.75%, recall = 99.12%, f1-score = 98.75%.

By means of simulation computer modeling, an effective variant of constructing an identifier for the speed of an asynchronous motor of an electromechanical system of a sintering machine is analyzed. The mathematical and algorithmic basis of the adaptive speed identifier (ASI) of an induction motor with a squirrel-cage rotor (ACIM) is given. Using the developed mathematical description of ASI with a reference model and using the apparatus of Lyapunov functions, an adequate computer simulation model was created. Compared with the existing methods for constructing identifiers in sensorless asynchronous electric drives, the proposed version of the ASI allows taking into account the discrete nature of the supply voltage of the ACIM at the output of the frequency converter with pulse-width modulation (PWM) of the output voltage and changing a larger number of equivalent circuit parameters. The stability of the speed identification process is provided in a wide range, sufficient to stabilize the speed of the trolleys according to the requirements of the technological process of sintering machines. As a result, the accuracy of speed identification in static and dynamic modes of operation of the electric drive increases. Simulation confirmed the operability of the proposed version of the identifier, proposed options for setting the AIS components. Universal, important for practical application results have been obtained, which allow both to build a high-precision system for identifying the ACIM speed in general and to refine the setting of the coefficients of the proposed version of the identifier in particular. An important property of the developed version of the ASI is its operability without loss of accuracy at near-zero and zero speeds of rotation and close to the nominal load torque on the ACIM shaft. In this regard, the practical application of the developed version, in addition to the drive of the sintering machine, is also possible in high- precision positioning systems for electric drives for various purposes.