Ilya Александрович Hodashinsky

A hierarchical structure for quadcopter control is proposed for maritime search and rescue missions under strong winds and prolonged disturbances. Adaptive integral sliding mode control with an extended state observer is used in the outer position control loop to compensate for control errors under the influence of cumulative disturbances. Sliding control with an extended state observer is used in the inner attitude control loop to ensure high performance and stability. Cumulative disturbances are directly estimated using the extended state observer and compensated in the control law, which improves the noise immunity of the system. The 3S Optimizer metaheuristic algorithm is used to develop a technique for selecting controller parameters, taking into account Lyapunov stability conditions and the physical constraints of the quadcopter. Simulation results showed that the proposed structure provides higher trajectory tracking accuracy and better disturbance tolerance compared to the control structures considered under identical operating conditions.

Unmanned aerial vehicles are currently widely used in maritime search and rescue missions to survey waters and locate victims. The success of the missions largely depends on the effectiveness of the search strategy. The article proposes a deterministic method for delimiting search areas and planning flight trajectories for a group of unmanned aerial vehicles during maritime search operations. An unmanned aerial vehicle detects a target if it is within the coverage area of the remote sensing equipment installed on the vehicle. The coverage area is specified as a circle of a certain radius. The problem of planning trajectories for complete coverage of the search area is solved. A trajectory of minimum length is considered effective. Two search strategies are considered: without taking into account and taking into account restrictions on energy resources. The problem of complete coverage of the search area divided into sectors is solved. Each sector is assigned to one device, which searches for a target in its sector according to a given algorithm. A geometric model of the search trajectory is presented. Four algorithms implementing the two specified search strategies are presented: 1) an algorithm for deploying a group of devices from the central point of the search area without taking into account energy resource constraints; 2) an algorithm for deploying a group of devices from the approach point to the search area without taking into account energy resource constraints; 3) an algorithm for deploying a group of devices from the central point of the search area taking into account energy resource constraints; 4) an algorithm for deploying a group of devices from the approach point to the search area taking into account energy resource constraints. The algorithms are tested.

Phishing attacks are among the most common cyberattacks aimed at stealing user personal information. Machine learning methods have successfully been used t

Constructing interpretable machine learning models that are able to explain the result of the inference process is an important factor to increase the level of confidence and trust in the machine learning models and their results, especially in medicine. Fuzzy classifiers are interpretable in nature because they consist of IF-THEN rules and use fuzzy terms used in colloquial speech. For the construction of fuzzy classifiers, the authors use a three-stage construction method consisting of a structure generation stage, a feature selection stage, and a rule parameter optimization stage. This paper focuses on the feature selection stage. At this stage, swallow swarm and leaping frogs binary metaheuristic algorithms are used for feature selection by wrapper method. Various implementations of binarization algorithms are proposed to convert the search space from continuous to discrete. Experiments on classifier construction and statistical evaluation of the effectiveness of the resulting models were performed, which showed a significant advantage in feature reduction compared to genetic fuzzy systems.

Classification problems with different types of variables can be difficult to solve. In this paper, we propose hybrid methods for extracting fuzzy classification rules from mixed data. A new frequency method was proposed to form the membership degree of fuzzy nominal values and compared with two other methods. The performance of the proposed methods is confirmed by a series of experiments on real-world mixed data sets. Empirical evaluations confirm the exceptional performance demonstrated by the proposed frequents method for processing mixed data.

A modified fuzzy min-max classifier is presented that differs from the original in the way that the hyperbox expansion operation is performed. The classifi

Fuzzy classifiers differ from other machine learning algorithms in their ability to interpret the inference process, which is especially important in high responsibility subject areas such as medicine. The membership functions of fuzzy terms and the rule base are easy to visualize, so it is not difficult for the user to understand why a particular result was obtained. However, the interpretability of the model suffers if the resulting model is highly complex, when the classifier has more than a dozen of high-length rules. Balancing accuracy and complexity in fuzzy classifiers is a nontrivial task. This article, the first in a series about constructing compact classifiers for medical data, addresses the problem of maximizing accuracy with as few rules as possible using metaheuristic algorithms. Using metaheuristics to optimize fuzzy rules allows a more accurate representation of the subject domain, which has a positive effect on classification accuracy. To increase the efficiency of population metaheuristics, it is important to use an appropriate method for a particular algorithm to form optimization starting points. The paper investigates the effect of using different population identification methods for two metaheuristics – the swallow swarm algorithm and the hybrid of the gravitational search algorithm and the shuffled leaping frogs algorithm.

In swarm algorithms, the need to measure population diversity arises in various contexts, such as in the adaptation of algorithm parameters, preventing the

The paper describes three stages in the construction of a fuzzy classifier. The first refers to the formation of fuzzy rules, the second stage is feature s

Coronavirus infection causes long-term post-Covid syndrome, which determines the need for medical rehabilitation. The use of modern machine learning technologies to predict the effectiveness of rehabilitation can personalize the process of providing assistance.Aim: To create a method for constructing a model to predict the effectiveness of patients rehabilitation who have suffered COVID-19.Material and Methods. The study included 64 patients admitted for inpatient rehabilitation after COVID-19. The average age was 56.92±9.29 years. To obtain information about the patients' health status, a physical examination, six-minute walk test (SHT), clinical and biochemical blood tests, and spirometry were performed in order to obtain information about the health status of the patients. The collected data were anonymized and transformed into a data set for the classification task, the output label of which was a binary attribute indicating the presence or absence of an improvement in the six-minute walk test result by at least 15 %. The proposed method for determining a consistent subset of features was tested by ReliefF, χ²-squared filters and the minimum redundancy and maximum information content algorithm; the binary genetic algorithm NSGA2 was used as a wrapper. The construction of preliminary machine learning models on the found subsets of features using the linear support vector machine was carried out.Results. In the process of testing the proposed method in the task of predicting the effectiveness of rehabilitation of patients after COVID-19, a subset of signs was identified that made it possible to achieve the maximum value of the concordance coefficient. The found set includes the following characteristics: gender, concomitant diseases, shortness of breath, cough, complaints about the gastrointestinal tract, six-minute walk test result, D-dimer level, assessment of shortness of breath according to the Borg scale.Conclusion. The proposed method allowed identifying the signs that are of the greatest importance for predicting the effectiveness of patients’ rehabilitation who have had COVID-19.