Advances in Systems Science and Applications

Frequency Domain Identification of the Quadcopter Attitude Dynamics

Vadim Alexandrov — 2023-10-12

The paper presents the identification of a linearized model of the quadcopter attitude dynamics. The attitude is described by the Euler angles of roll, pitch and yaw. Closed loop identification is performed, when the quadcopter control system operates providing flight stability. The experimental flight data, when the sequence of the sine waves is fed as required value for each angle separately, are used. The transfer functions from the controls, which provide the torques related to the body frame axes, to the Euler angles are obtained via the finite frequency identification procedure. Moreover, components of the rotational dynamics are considered in detail. The unknown parameters of the transfer functions are found by optimization procedure using the experimentally obtained values of the frequency response for the set of test frequencies. The difference in the parameters values of the fixed linearized model structure, identified for two operating points, shows that the tilt angle dynamics is sufficiently nonlinear.

Newton Method vs. Semismooth Newton Method for Singular Solutions of Nonlinear Complementarity Problems

Alexey Izmailov — 2023-10-12

Among the most successful techniques for solving nonlinear complementarity problems is the one consisting of reformulation of the problem in question as a system of nonlinear equations, by means of the so-called complementarity functions. Different complementarity functions lead to nonlinear systems with different smoothness and regularity properties, thus allowing for application of different classed of numerical methods. In this paper we compare the Newton method for the smooth reformulation with the semismooth Newton method for the reformulation relying on the nonsmooth Fischer--Burmeister complementarity function, with a special emphasis on the cases when the solution in question violates the strict complementarity condition.

Deep-Learning-Based Tracing for Satellite Telemetry

Ilya Nachevsky — 2023-10-12

The mechanical section of the telemetry data from the scientific satellite “Lomonosov”, (such as yaw, pitch, and roll angles of the spacecraft’s main axes, along with its programmed and measured velocities) is pre-processed (alignment, reflection, and binarization) and used for anomaly behavior propagation. The main goal of this study is to estimate possible abnormal behavior of the system in the future and to help to restore normal behavior during a limited communication session with a spacecraft. The system model uses a recurrent architecture approach, namely tracing methodology, considering time shifts in the target data sequence. A deep learning strategy is used to model the abnormal behavior using the onboard collected mechanical information as inputs. The results are compared with the onboard anomaly detection system (ARO) data. The reproduction of the obtained information shows better performance compared to traditional estimation techniques, using binary cross-entropy and receiver operating characteristic curve (ROCAUC) as comparison criterion. Future model modifications, which can improve its quality, are discussed at end of the study.

Feedback Design in Linear Control Problems as an Optimization Problem

Mikhail Khlebnikov — 2023-10-12

We provide and discuss a new approach to the design of linear control systems based on the optimization viewpoint. Three basic classes of control problems are analyzed: a) static state and output feedback for linear quadratic regulator problem; b) rejection of nonrandom bounded exogenous disturbances via static linear feedback; c) the same rejection via dynamic output feedback using an observer. These three problems are considered as optimization ones with feedback gains as matrix variables. The iterative algorithms for its solution are formulated in a uniform way, and the explicit expressions for gradients of the cost functions are provided. The gradient method exhibits its efficiency for test examples, including double pendulum.

On a Random Topological Characteristic for Inclusions with Nonlinear Fredholm Operators: Application to Some Classes of Feedback Control Systems

Valeri Obukhovskii — 2023-10-12

We define and study an oriented random coincidence index for a pair consisting of a nonlinear zero index Fredholm operator $f$ and a nonconvex - valued random multivalued map $G$ which is fundamentally restrictible with respect to $f.$ It is shown how this characteristic can be used for the justification of the existence of random coincidence points. We present an application of developed results to the existence of a random solution
for a control system whose dynamics is governed by an implicit integro-differential equation and the feedback is realized by a random differential inclusion.

COVID-19 Spread Modeling Incorporating Suggestive Optimal Control Strategies under Uncertainty

P.K. Santra — 2023-10-12

In the present paper, we have provided a five-compartmental epidemic model in an interval environment to analyze the spread of COVID-19 infection in India. The proposed model divides the entire population of India into five classes. They are susceptible, exposed, asymptomatic, symptomatic, and recovered classes. Under some suppositions, the crisp model is constructed and converted to an imprecise model by the interval number. We introduced a parametric functional form of an interval number to study the imprecise epidemiological model. The main objective of this study is to develop an epidemiological model in an imprecise environment and to try to understand the dynamics of the epidemic model of COVID-19 infection spread in India. We also presented the COVID-19 model with two controls to effectively control COVID-19 disease in India. Finally, a numerical simulation is carried out considering that the model parameters are imprecise. The numerical results show that our proposed imprecise model is reliable from a practical point of view.

Global Stability Analysis of Malaria Model with Prophylactic Treatment

Saheed Ajao — 2023-10-12

In this paper, we present a mathematical model of the interaction between the human population and the vector (mosquito) population to study the stability of a malaria model in the presence of prophylactic treatment. The graph-theoretic method was used to obtain the basic reproduction number (R₀). We obtained the disease-free equilibrium for the model which is locally and globally asymptotically stable when the basic reproduction number is less than unity. Moreover, we showed that there exists a unique endemic equilibrium whenever R₀> 1, and the Lyapunov function was used to establish that the endemic equilibrium is globally asymptotically stable whenever R₀> 1. The simulations show that the presence of prophylactic treatment reduces the population of infectious individuals. Further numerical simulations carried out conformed with the analytic results.

Exploratory Data Analysis and Natural Language Processing Model for Analysis and Identification of the Dynamics of COVID-19 Vaccine Opinions on Small Datasets

Alexander Chkhartishvili — 2023-10-12

In this study, the successful implementation of an active learning algorithm on small-scale datasets is demonstrated. The study also examines the dynamics of public opinions on COVID-19 vaccinations using VK (social network) commentaries related to the COVID- 19 vaccine and masks for opinion evaluation. The proposed methodology includes several stages such as natural language processing, classification with active learning, exploratory data analysis, and opinion dynamics. Natural language processing is used for text preprocessing, tokenization, and feature extraction. A machine learning model with active learning is employed to identify opinions as positive, negative, or neutral/unknown. The model includes classical machine learning, machine learning and deep learning models. The results show that the highest classification accuracy is 69.1% and 73.1% without and with the active learning algorithm, respectively. The experimental results suggest that classifiers using active learning perform better than simple natural language processing classifiers on small-scale datasets.

Quality Control of a Real-Time Flight Experiment Using Neural Networks

Vasiliy Akhrameev — 2023-10-12

The paper presents some investigations on automatic processing of flight information. The aim of this investigation is to reduce the cost and timing required for aviation equipment testing. The algorithms are developed for recognizing various test modes as well as evaluating the correctness of their implementation. While developing the algorithms a multilayer perceptron and a Kohonen network were used as basics. The results of experiments are presented when applying these algorithms for real testing process.

Accurate Indoor Positioning System Based on Visible Light

Asmaa Hadjer Saboundji — 2023-10-12

Beside its use in high-rate wireless communications, visible light has recently emerged as an interesting positioning technology, which is referred to as Visible Light Positioning (VLP). Its main advantage is its high positioning accuracy. Like with other positioning technologies, many techniques may be used with VLP, such as Received Signal Strength (RSS), (Difference) Time Of Arrival ((D)TOA), Angle of Arrival (AOA), etc. In this paper, we investigate the use of the RSS technique for VLP. Two new methods are proposed and their performances are compared experimentally to those of three methods from the literature. Both of the proposed methods are based on polynomial fitting. The obtained results show that the second one has a positioning accuracy that is comparable to that of the best method among these three reference methods, while being simpler. It should be highlighted that with only standard low-cost commercial components, we could obtain a very high positioning accuracy, with a mean localization error in the order of 1 cm.

Butler Group Direct Decomposition Classification With Applications to Parallel Algorithms

Ekaterina Blagoveshchenskaya — 2023-10-12

The graphical approach to the classification problem of Butler group direct decompositions is used to preserve the indecomposability property of some rigid subgroups in all possible direct decompositions of the group itself. The group class under consideration as well as torsion-free abelian groups as a whole admits non-isomorphic direct decompositions. The proof of decomposition existence with predicted properties is one of the investigation streams. Until now the related results concerned only the ranks of indecomposable summands. Now the way of controlling the other properties of group decompositions is suggested. All the results in this direction are closely connected with the algorithm parallelization. The special feature of the results presented is that they give the method of constructing certain dependence graphs as the subgraphs of the algorithm graph to be given in a parallel form preserving the corresponding fragments. Such dependence subgraphs can define the data relations in parallel computations, which reflect various conditions of parallelism.

Semantic Image Segmentation Using a Hybrid Genetic–Cuckoo Search Algorithm

Alaa Abu Srhan — 2023-10-13

Image segmentation is the process of dividing a given image into a set of regions or categories. The goal of image segmentation is to change the image representation into a form that is substantially meaningful and easy to analyze. Metaheuristic optimization algorithms are widely used algorithms for many applications among them is image segmentation. Genetic algorithm (GA) and cuckoo search (CS) algorithm are among the most popular metaheuristic algorithms. In this paper, a hybrid CS and GA (CSGA) has been used to perform image segmentation and object detection, then compared with other popular algorithms for image segmentation which are fuzzy C-mean (FCM), K-means algorithms, and GA. Simulation results of the statistical measures of the performance corroborate that CSGA outperforms other compared methods.

On Bi-Laminar Neural Field Models of Electrical Activity in the Primary Visual Cortex

Evgenii Burlakov — 2023-10-30

We investigate the modelling framework for studying electrical activity in the primary visual cortex of the brain based on a bi-laminar neural field equation. The deep layer of the neural field models the orientation-independent electrical activity, whereas the orientation-dependent superficial layer captures the selectivity to spatially oriented stimuli of the orientation columns in the primary visual cortex. We verify the solvability of a Cauchy problem for the bi-laminar neural field equation with both sigmoidal and Heaviside-type neuronal activation. We also construct connections between the solutions that correspond to these types of neuronal activation, which justifies the use of the Heaviside-type neuronal activation functions that is crucial in the problems of computer simulations involving vast ensembles on neurons. We prove the possibility of a correct approximation of the bi-laminar neural field model with a two-layer neuronal network. We also highlight some perspectives opened by the results of the present research related to the studies of travelling waves of evoked electrical activity in the visual cortex as well as the neural activity control problems in the framework of the neurofeedback paradigm.