An assessment of the algorithm difference measure in a multivariate system for production plans scheduling

In the process of designing a production plan, one of the important steps is scheduling the execution of technological operations. The schedule can be created either manually or by using software. If the schedule is compiled by software, then several schedule generation algorithms are used to eliminate possible errors. A set of such algorithms is called a "batch". It is advisable that only different algorithms should be included in the batch. This is necessary to eliminate errors of the same type. Therefore, the search for clones of algorithms in the batch is an urgent production task. To solve it a diversity metric of algorithms was developed in the course of this work. Such a metric numerically (as a percentage) determines how much the algorithms differ. This metric is based on the properties of the algorithm execution. Algorithm traces are constructed in the N-dimensional space using the obtained points. The coordinates of the trace points are the values with which the algorithm works at each step of its execution or each of the control points of the algorithm execution. An experiment was performed to confirm the correctness of this metric. Within this experiment, the trace properties of three sorting algorithms were calculated. Based on the properties obtained, indicators were determined for comparing algorithms in the metric space. The experiment confirmed the effectiveness of using the diversity metric to find clones in the algorithms batch. The scope of this metric is not limited to clone searches. It can be used as an independent indicator of software quality.

A method for the synthesis of neural regulators for linear objects

The article proposes a method for the synthesis of a neural controller for closed-loop systems with linear objects. The scientific novelty of the proposed method lies in the fact that the neural controller, to the input of which the object state vector is fed, must be trained to stabilize in one of the possible desired values, and to ensure regulation in other desired values. For objects with an inaccessible state vector, it is possible to use the estimation vector of the object state vector. It is proposed to proportionally decrease/increase the signal of the state vector and increase/decrease the control signal formed by the neural regulator. Also, other advantages of the proposed method include: 1) the absence of the need for training on several desired values, which greatly simplifies and accelerates the training of the neural network, and also eliminates control errors in the range of values for which the neural controller was not trained; 2) the possibility of learning from an initially unstable state of a closed-loop system. The proposed method for the synthesis of a neural controller for a closed-loop system with a linear object was tested on the example of the synthesis of a controller for an object 1/s 3, which is unstable. A neural network is used as a regulator, which is proposed to be trained using one of the reinforcement learning methods (in the article, the Deterministic Policy Gradient method allowed us to obtain the best results). The resulting graphs of transient processes allow us to conclude about its successful application. The article ends with conclusions and considerations about further lines of research, which include the quality of the transient process and the possibility of adjusting it by changing the reward function, which will allow setting the graphs of transient processes.

Non-parametric identification algorithms for complex engineering systems

In a practice, it often happens that complex engineering systems consist of several interconnected different-type simpler subsystems. An adequate model formulation for every subsystem is impractical due to the complexity of physical processes proceeding in the subsystem. In such cases, a non-detailed black-box model is commonly used. For stationary linear systems (or subsystems), the connection between an input and an output of the black-box is defined by the Volterra integral equation of the first kind with an undetermined difference kernel also known as an impulse response in the automatic control theory. It is necessary to evaluate the unknown impulse response to use the black-box model .This statement is a non-parametric identification problem. For complex systems, the problem needs to be solved both for a whole system and for every isolated subsystem that makes identification substantially complex. Formally, impulse response evaluation is a solution of the integral equation of the first kind for its kernel over registered noise-contaminated discrete input and output values. This problem is ill-posed because of possible solution instability regarding measurement noises in initial data. To find a unique stable solution regularizing algorithms are used, but specific input and output signals in impulse response identification experiments do not allow applying computational methods of these algorithms (system of linear equations or discrete Fourier transformation). In this paper, the authors propose two specific-considering identification algorithms for complex engineering systems. In these algorithms, smoothing cubic splines are used for stable calculation of first derivatives of identified system signals. The results of the complex “Heater-Blower-Room” system identification prove the efficiency of algorithms proposed.

Detection of different type events in hybrid dynamical systems

Hybrid dynamical or simply hybrid systems (HS) are a modern apparatus for modeling discrete-continuous processes in different applications such as power engineering, aeronautics, manufacturing, economics, transport dispatching, etc. The key difference of HSs from classical dynamic systems is the presence of continuous mode switching events. Event times are defined by the zeroes of continuous event functions. If it is impossible to symbolically compute an event time. To do it one uses an event detection algorithm working together with a differential equation integration algorithm. Events in HSs are traditionally divided into state events and time events. Only explicit time events with event functions in the form of linear polynomials in time are usually considered in the literature. This paper addresses the class of implicit time events and lists their possible sources. Moreover, the traditional classification of events into unilateral, bilateral and accuracy critical events is expanded by adding difficult-to-detect events. These events are characterized by event functions crossing zero several times within one integration step. Not all algorithms can guarantee detecting events of this type. Heterogeneous HSs including processes of different physical nature are in general characterized by significantly stiff and high-dimensional modes usually defined in a form of differential-algebraic systems of equations with events of different types. The last feature limits the application of classical event detection algorithms oriented to a single event type. That is why the paper proposes the methodology of complex event detection consisting in using separate event detection algorithm for each event type. The joint work of several algorithms can ensure correct detection of events of different types and also may improve the efficiency. A complex event detection algorithm guaranteeing detection of all events is constructed for a particular HS. The complex algorithm demonstrates an average speed up of 17%.

Mathematical models for determining limit operating modes in electrical networks with distributed generation plants

Decentralization of electricity generation based on distributed generation plants is an important segment of the new technology platform for the power industry. On the basis of this approach, significant positive effects can be obtained, which consist in reducing financial costs of energy supply, increasing the uninterrupted power supply, improving the quality of electricity and stimulating the use of renewable energy sources. Effective use of distributed generation in electric power systems requires the development of methods and tools that provide coordinated management of normal, emergency and post-emergency modes. Of particular relevance is the problem of determining the limit operating modes of networks, at the nodal points of which relatively low power generators are connected. In some situations, for example, when using small hydraulic stations, groups of such generators can be located at significant distances for 6-10-20 kV distribution networks from consumption centers. In this case there will be a noticeable limitation of the regions of static aperiodic stability. The article presents the results of developments aimed at implementing methods for determining the limit operating modes by static aperiodic stability in networks with distributed generation plants. The proposed approach is based on the limit modes equations which provide the formation of effective algorithms for the operational finding of points belonging to the boundaries of stability regions. The results of the construction of the indicated areas for a 6 kV electric network with distributed generation plants based on low-power hydraulic stations are presented. Additionally, the transient processes in the studied electric power system were simulated in the Matlab system for various space points of the controlled mode parameters.

Design and research on a software and hardware complex for monitoring the condition of a pet

This article analyzes the current state and a potential use of pulseoximeters in veterinary medicine. Promising optical methods such as optical coherence tomography, pulseoximeter, and hyperspectral imaging have been clinically introduced into human medicine. But even though human and small animal medicine shares a personalized modern approach, biophotonics is still rarely used in veterinary medicine. Pulseoximeters are most often used when monitoring the condition of an animal during general anesthesia. Prospective optical devices for small animals, such as dogs and cats, should be reliable and resistant to damage (for example, due to bites or chewing), offering convenient and short measurements. The potential of using pulseoximeters for pet monitoring has yet to be explored. In this paper, we considered two methods of measurement, namely lumen oximetry and reflection oximetry. Based on the literature sources, we can conclude that the method of optical reflection oximetry has the same diagnostic value as the method of lumen oximetry and therefore can be used for veterinary pulseoximeters without losing the accuracy of pulse measurement and blood oxygenation. According to the results of the existing devices review, it was found that they mainly use the lumen oximetry method. This method is convenient for performing measurements in animals under anesthesia, but it is problematic for use on actively moving animals. The purpose of this work is to develop a new model of pulseoximeter for animals. A new type of the device based on an earlier unused method is proposed, and its advantages and disadvantages are described. Components for creation are proposed and a scheme based on these components is constructed.

Dichotomy. Dichotomy? Dichotomy! Basic provisions, problems of terminology and inspection analysis of the method of dichotomy

When creating modern systems of automatic control of various processes and objects operating in real time, very often one has to face the problem of solving various kinds of nonlinear scalar equations. Currently, there are a number of computational methods and algorithms for its solution, one of which is the dichotomy method. This method has a number of advantages in comparison with other known methods for solving nonlinear equations, but at present it has not found wide practical use. The main reason for its low popularity is the low rate of convergence of the sequence of approximate solutions and a large amount of computation required to obtain sufficiently accurate solutions. The purpose of the study is to consider in detail distinctive features of the dichotomy method and show the preference of its use in comparison with other known methods. We propose a modified version of the dichotomy method that allows one to obtain more rapidly converging sequences of approximate solutions to nonlinear scalar equations and requires significantly fewer computations required to obtain solutions with the desired accuracy. By solving a number of specific nonlinear equations, it is possible to illustrate the higher convergence rate of the sequence of approximate solutions calculated using the modified dichotomy method and, thereby, to substantiate the advantage of the new method for its use in creating various automatic control and regulation systems. Based on the results obtained a modification of the method for segment bisection is proposed. It has all the main advantages of the modified method. The research results can be used in the development of modern automatic control systems for various technological processes and objects.

Theoretical and empirical Lorenz functions, Gini indices, and their properties

The issues of assessing the fairness and efficiency of the distribution of the total income of society between different groups of the population have attracted attention of scientists for a long time. They became most relevant at the end of the 19th – beginning of the 20th centuries in connection with the intensive stratification of countries with various political and social systems caused by the intensive development of the economy, science and technology. The Lorenz function and the Lorenz curve, as well as the Gini index, are commonly used for theoretical research and applications in the economic and social sciences. These tools were originally introduced to describe and study the inequality in the incomes and wealth distribution among a given population. Nowadays they have found wide application in such fields as demography, insurance, healthcare, the risk and reliability theory, as well as in other areas of human activities. In this paper we present the properties of the Lorentz function and various representations of the Gini index, systematize the analytical results for uniform, exponential, power-law (types I and II) and lognormal distributions, as well as for the Pareto distribution (types I and II). Additionally, the issue of estimating inequality based on the Pietra index and its relationship with the Lorentz function was studied. Nonparametric estimates of the Lorentz function and the Gini index based on a sample from the corresponding distribution are considered. Strict consistency and asymptotic unbiasedness of these estimates are shown under certain conditions for the initial distribution with an increase in the sample size. On the basis of the method of linearization of estimates, the asymptotic normality of the empirical Lorentz function and the empirical Gini index is determined.

A neural network model for short-term forecasting of electricity generation by solar power plants

Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

Control flow graphs optimization in intermediate representations of the functional dataflow parallel programming language

Functional dataflow programming languages are intended for the development of architecture-independent parallel programs and support the control of computations based on data availability. Due to the fact that at present parallel computing systems are very widespread, and their programming in imperative languages is associated with portability problems, the development of architecturally independent parallel programming tools is an urgent task. When such a program is translated, intermediate representations are formed as the information graph and the corresponding control graph. During program execution, data readiness signals are transmitted along the arcs of the control graph. An explicit selection of the control graph allows us not only to change the computational control strategies and ensure the adaptation of the program to the architecture features, but also to apply specific methods for optimizing control dependencies. The paper proposes transformation methods that provide optimization of the control graph. When generating a control graph from an informational one, redundant arcs are introduced into it, the removal of which does not affect the result of the program, but leads to its more efficient execution. It is shown that in dataflow programs, in addition to control dependencies inherent in other programming languages, additional ones associated with the implementation features of deferred or conditional computations described by delayed lists arise. A formal description of redundant dependencies of various types is given, as well as an effective algorithm for their identification. The developed approach can be applied to such dataflow programming languages as PIFAGOR and Smile.