Patent Landscape Reports (PLR) have long been used in business, science and RD among countries, it is USA and China. However, patents of high technological relevance and/or commercial value may appear in small tech companies, or as a “by-product” of other corporate activities. The research results are of interest to researchers and practitioners involved in the development and implementation of 5G technologies.
The control of room temperature and humidity is important for ensuring of the necessary indoor human comfort for optimal work capacity and effective rest. The plant nonlinearity and the variables coupling require intelligent control techniques in order to satisfy the high performance demands. The present paper suggests a procedure for the design of a simple for industrial implementation fuzzy logic controller on the principle of parallel distributed compensation (PDC) that consists of linear local decoupling two-variable controllers. It is based on a Takagi-Sugeno-Kang (TSK) plant model, derived from experimentally obtained plant step responses using expert knowledge and parameter optimisation via genetic algorithms. The design is applied for the control of the temperature and the relative humidity of a laboratory air-conditioning system. The PDC system outperforms an existing Mamdani two-variable control system with adaptive properties in shorter settling time, higher robustness and reduced overshoot, estimated from simulations.
The COVID-19 pandemic situation keeps on ruining and affecting the wellbeing and prosperity of the worldwide population and due to this situation, the doctors around the world are working restlessly, as the coronavirus is increasing exponentially and the situation for testing has become quite a problematic and with restricted testing units, it’s impossible for every patient to be tested with available facilities. Effective screening of infected patients through chest X-ray images is a critical step in combating COVID-19. With the help of deep learning techniques, it is possible to train various radiology images and detect COVID-19. The dataset used in our research work is gathered from different sources and a specific new dataset is generated. The proposed methodology implemented is beneficial to the medical practitioner for the diagnosis of coronavirus infected patients where predictions can be done automated using deep learning. The deep learning algorithms that are used to predict the COVID with the help of chest X-ray images are evaluated for their prediction based on performance metrics such as accuracy, precision, Recall, and F1-score. In this work, the proposed model has used deep learning techniques for COVID-19 prediction and the results have shown superior performance in prediction of COVID-19. © 2021, International Institute for General Systems Studies. All rights reserved.
This paper describes models, methods and software tool for testability analysis of aviation systems. It comprises analytical and programing aspects of calculations of main testability, reliability and availability indices. It presents general description of the software, XML schema of input data and technique of their mapping to the database structure. The procedure for generating the initial data for testability analysis based on the line replaceable units failure modes report is described. Fault tree model for analysis of the built-in test conformity is suggested. Markov models have been created for analyzing reliability and availability, taking into account the features of the built-in test and the specifics of the aircraft operation. An approach to the construction of trends in the operative availability of aviation systems in the inter-maintenance interval is proposed.
EMG data processing and muscle activity recognition has become the most popular method for upper limb prosthetics. The high sensitivity of EMG sensors with respect to external disturbances and other factors prevent from accurate muscle activity recognition. The aim of the paper is to investigate robustness of window recognition method with respect to muscle fatigue and perspiration of the forearm skin. The current experiment was carried out using Arduino nano microcontroller connected to EMG sensors. The subject under study is a healthy man of 26 years old with an average build. The subject was asked to do physical exercises, thereby loading the muscles of the fingers of the hand to achieve partial or complete fatigue and perspiration. During the whole process, EMG sensors have installed on the subject and transmitted the signal to the computer using Arduino. All signal processing is done directly on the computer with a pre-recorded signal. Experimental results have been shown that with the appearance of external factors during prosthesis operation recognition accuracy may degrade to unsatisfactory. False positives occur with perspiration of skin surface and complete muscle fatigue. An algorithm for automatic self-correction of the boundaries of motion detection zones has been introduced. Instead of identification of causes that leads to performance degradation, we use correction scheduling started by timer. Experimental results have shown that proposed automatic adaptive correction is effective. Despite higher recognition delay, proposed auto-tuning method provides satisfactory muscle activity identification and feature extraction in real-time.
This paper considers the problems of adopting digital technologies in decision-making. Two types of digital decision technologies are described, namely, the direct (or traditional) technology with human decision-making based on computer-aided advisor systems and also the inverse technology with independent computer decision-making in which a human merely monitors the decision process. These technologies are compared with each other, and some applications are discussed.
This paper concerns robust synchronization and parameter identification for nonlinear gyroscope systems. Gyros are widely utilized in navigational applications where synchronization plays a vital role. A system of nonlinear dynamical equations with some parameters presents a model of gyro systems. The parameters of gyro can vary in time, which can lead to desynchronization of the gyro systems. In this paper, we assume that a gyro system has bounded time-varying unknown parameters and the synchronization problem is considered in two situations. First, the synchronization of two gyroscopes with identical dynamical model and, second, the synchronization of a gyroscope with the Rossler system. The Lyapunov stability theory with control terms is employed to cope with the problem. Also, the identification of time-varying unknown parameters is the side goal of the paper. The proposed scheme synchronizes chaotic nonlinear systems in both situations appropriately. In addition, the slave parameters converge to the nominal values of master parameters despite uncertainty. Simulation results illustrate the superiority of the proposed method.
This paper investigates the matter of stability criteria for linear time delay systems with distributed delay. Firstly, a relaxed double integral inequality is established to estimate the double integral terms appearing within the derivative of Lyapunov-Krasovskii functionals (LKFs) with a triple integral term. Unlike the recently introduced Jensen's inequalities, Wirtinger based integral inequalities, refined Jensen's inequalities and therefore the auxiliary function based integral inequalities the proposed relaxed integral inequality provides large feasible solution region and fewer conservative results. Secondly, by constructing an augmented Lyapunov-Krasovskii functional with a triple integral term, the robust stability criteria for linear time delay systems with distributed delay are given in terms of linear matrix inequalities (LMIs), which may be easily computed by the LMI toolbox of MATLAB. Finally, two numerical examples are performed to indicate the effectiveness of the proposed criterion.
In the modern global economy, the system of functional cooperative relations that lead to the formation of hybrid structures – production networks has become actively spread. However, the strength and scale of cooperation are not uniform. Using the example of Russia, the authors consider the effectiveness of the production network of cooperation between small and medium-sized enterprises with large companies. We tested a number of hypotheses based on common ideas about the effects of cooperation. Empirical results make it possible to clarify the mechanism of formation and features of interfirm production chains in Russia. The “anchor” role of large enterprises with state participation as centers of cooperation formation is noted. In the course of the study, 14 enterprises were selected, distributed across key sectors of the Russian economy. Statistical and correlation analysis methods were used to evaluate the effects of cooperation. The results showed that the orders placed by large manufacturing enterprises with small and medium-sized enterprises increased over the period of 2015–2019. “Anchor” enterprises, as a rule, reduce the production localization degree. However, this does not have a significant impact on improving the profitability of their activities, and also does not depend on the share of state participation. Besides, placing orders with small and medium-sized enterprises does not allow them to reduce the number of employees. Many of the expected internalities that are characteristic of cooperative relations in developed countries are not reflected in the specifics of the Russian economy, or their manifestation is limited. The Russian experience clearly demonstrates the weakness of cooperative partnership, although with positive trends of change. There is a need to further improve the mechanisms for supporting small and medium-sized enterprises in the production sector, aimed at creating sustainable networks. The proposed approach can be applied to assess inter-firm production chains in other countries. A comparative study will determine the strength of the formation of production networks across countries, which will expand the understanding of the economic processes of networkization.
Coronavirus Disease 2019 (COVID-19) is a high death rate respiratory condition that requires easy-to-reach markers for prediction. The electrocardiograph (ECG) alterations that may occur after COVID-19 hospitalization have not been fully studied yet. COVID-19 also affects heart function, which can be seen on an ECG. As a result, ECG can be used to detect virus-infected individuals. The database consists of ECG images. In this scenario, a convolution neural network (CNN) is utilized to classify COVID-19 ECG. The model is made up of eight layers, including a convolution layer, a max-pooling layer and a dense layer. The ECG image is fed into a CNN model, which classifies the COVID-19 ECG. The model provides us with 98.11% accuracy, 98.6% sensitivity and 96.40% specificity. Although 100.00% of the categorization of normal images and COVID-19 ECGs were not accurately determined by the proposed CNN model, this is the first CNN model to categorize ECG images into normal and COVID-19 classes from the ECG database and provide additional diagnostic to medical experts. © 2021 ASSA.
This paper is devoted to the study of modular inequality for general Hardy–Copson type operator restricted on the cone of monotone functions from weighted Orlicz space with general weight.
The paper considers a periodic differential inclusion with an asymptotically stable set. The uniform character of convergence of solutions to an asymptotically stable set is established. An exponential estimate is obtained for solutions of a periodic differential inclusion homogeneous in state vector. Examples of control systems leading to consideration of periodic differential inclusions are given. These results can find applications in the stability analysis of control systems with periodic parameters, in particular, servomechanisms whose elements operate on AC, control systems with pulse amplitude modulation, and systems used to solve problems related to investigating vibrations of milling machines.
The interest of this paper is to examine the controllability and observability of a control system in the configuration state-space of an uncertain optimal control system. The control system is designed based on the realization of capital asset values where a special case of asset management is modelled and optimized. Thus some necessary and sufficient conditions of the controllability and observability of the deterministic systems and the corresponding uncertain systems for the case of the uncertain optimal control system with application in capital asset management are considered.
The paper considers a method for correcting thermographic images. Mathematical processing of thermograms is based on the analytical continuation of the stationary temperature distribution as a harmonic function from the surface of the object under study to the heat sources. The continuation is performed by solving an ill posed mixed problem for the Laplace equation in a cylindrical region of rectangular cross-section. The cylindrical area is bounded by an arbitrary surface and plane. The Cauchy conditions are set on the surface-the boundary values of the desired function and its normal derivative. Inhomogeneous conditions of the first kind are set on the side faces of the cylinder. The problem is the inverse of the corresponding mixed problem for the Poisson equation. In this paper, an approximate solution of the problem is obtained that is stable with respect to the error in the Cauchy data and inhomogeneity in the boundary conditions. In the course of constructing an approximate solution, the problem is reduced to the Fredholm integral equation of the first kind, which is solved using the minimum smoothing functional principle. The convergence of the approximate solution of the problem is proved when the regularization parameter is matched to the error in the data.
Currently, due to the successful use of neural network technologies for analyzing data of various formats, the range of problems that can be solved using mathematical modeling methods has significantly expanded. The paper deals with the topical task of analyzing speech perception by social media data and assessing the level of conflictogenity, social approval / stress of the residents regarding urban planning projects. The purpose of the paper is to develop and show on a practical example the effectiveness of the integration of neural network and mathematical models for solving such tasks. Mathematical models are built using the methods of mathematical statistics and topological data analysis.
The problem of an optimal PP/TP (path/trajectory planning) evasion of a mobile vehicle from the detection is considered. The objective is to minimize the risk of a moving vehicle being detected by a static sensor when moving between two specified points on the plane. Detection is based on the primary acoustic field emitted by a vehicle with an inhomogeneous radiation pattern. An algorithm for finding a two-link optimal trajectory is proposed. The optimal trajectory and the law of speed of a mobile vehicle, as well as the value of the criterion, are found.
In this paper we study the controllability problem in a Banach space for various classes of functional inclusions with causal operators with an infinite delay, and impulse effects. Basing on the topological degree theory for condensing multimaps, we prove a global theorem on the existence of trajectories for systems governed by functional inclusions. As an application, we obtain generalizations of existence theorems for the controllability problem for a semilinear first order functional differential inclusions of this type and a semilinear functional differential inclusions of a fractional order 0 < q < 1.
We consider the model of the production side of the Russian economy. This model is derived as the solution of the nonlinear dynamic optimization problem of the macroeconomic agent we call Producer. This agent maximizes his discounted profit flow under technologic, demographic and financial constraints. We use the method of relaxation of complementary slackness conditions to transform the model to the more regular one and evaluate its parameters on the Russian macroeconomic data. We show that this model can successfully replicate the large set of Russian macroeconomic indicators such as gross domestic product, loans of producers, volume of fixed assets etc.
Algebraic methods of processing data obtained by control and measurement systems to achieve angular superresolution are presented. The efficiency of using the methods in the formation of approximate images of objects at low signal-to-noise ratios is shown. The results of numerical experiments demonstrate the possibility of obtaining images with a resolution exceeding the Rayleigh criterion by 3-10 times. The robustness of the solutions obtained by the methods of algebraic exceeds many well-known approaches. The relative simplicity of the presented methods allows the use of inexpensive computing devices and perform real-time measurement processing.
An eco-epidemiological model representing the interactions between prey and predator populations affected by a disease in an ecosystem is presented. The model is governed by a five-dimensional nonlinear system of ordinary differential equations coupling both ecological and epidemiological features of interacting populations. The well-posedness of the model is established with respect to positivity and boundedness of solutions. Conditions for asymptotic stability of different equilibrium points are extensively investigated to determine the existence and coexistence of prey and predator species using local linearization and Lyapunov functions techniques. Additionally, the analysis of the model is extended to assess the effects of three time-dependent control functions, such as disease prevention, treatment and alternative resource for predator, on the population dynamics of the prey-predator coexistence in the system.
