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Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.

The study focuses on the choice of prevention schemes of the appearance and recurrence of gangrene of the lower extremities, depending on any of the degrees of confidence that the patient will have gangrene or will experience its relapse. The degree of confidence is determined using the fuzzy logic of decision making on the relevant membership functions. For each of the selected classes, an appropriate prevention scheme has been developed, the effectiveness of which was tested using the theory of measuring latent variables and the synthesis of mathematical models of their choice depending on the degree of risk of the occurrence and recurrence of lower extremities gangrene. Model statistical tests showed that compared with traditional prevention schemes the use of the proposed models can increase the rate of positive results in the absence of lower extremities gangrene and reduce the possibility of relapse (recurrent changes by 42%, risk of amputation by 35%).

Urologists and nephrologists attribute pyelonephritis of pregnant women to the category of complicated upper urinary tract infections that threaten the development of a severe purulent-septic process. The frequency of pyelonephritis in pregnant women ranges from 12.2 to 33.8%. In this research, laboratory indicators of the state of immunity and lipid peroxidation using fuzzy decision logic are used to improve the quality of differential diagnosis of serous and purulent pyelonephritis in pregnant women. A space of informative indicators was formed that characterize the state of immune changes, making it possible to carry out the differential diagnosis of pyelonephritis forms in pregnant women with high accuracy. Results of the operation of the obtained decision rules in the control sample showed that the diagnostic efficiency of the proposed method reaches 93%, which is acceptable for use in medical practice.

Several researchers studied the health impacts of electromagnetic fields in work environment. However, the previous research focuses on the statistical analysis of past exposure. There are no studies that addressed prediction of health symptoms. Prediction and early diagnosis of occupational diseases of electric power workers with acceptable accuracy is needed. The objective of this study is to develop a data driven mathematical model for predicting and diagnosis of occupational diseases in workers in electric power industry. The complex nature of disease occurrence due to electromagnetic radiation is appropriate for the fuzzy rules set by medical experts which are analyzed and validated to produce hybrid fuzzy decision rules. The selected group of medical experts suggested using hormonal disorders, endocrine diseases, coffee abuse, chronic diseases of the internal organs, allergic diseases, cervical osteochondrosis, severe course of infectious diseases, intoxication, injury. The developed hybrid fuzzy logic model predicts high risk of developing nervous system diseases. The prediction accuracy exceeded 0.88, which is acceptable for supporting tool.

Working at several electric power facilities exposes workers to hazardous factors are risk of electrical shock and electromagnetic waves of industrial frequency of 50 Hz) and less intense fields of the radio frequency range, noise and vibration levels, harmful chemicals like burning gases and fumes in the air, and psychological stress due to the on-call emergency state. In this work, we develop hybrid fuzzy decision rules is a promising tool that combines clinical knowledge with artificial intelligence. The developed model allows diagnosis of nervous system diseases at early stage. The selected decision rules took into account the environmental situation and individual health risk factors. This provides confidence in the prediction decisions of contracting nervous diseases shows the high accuracy of more than 0.85. The prediction of early stages reached minimum accuracy of worse than 0.92, which makes it a valuable tool to support physicians’ diagnoses. The developed model is valuable for health treatment decision making.

The problems of synthesis of hybrid fuzzy decision rules of ergonomic level evaluation of vehicles and its influence to the drivers’ health condition has been reviewed in current article Our investigations show that the confidence in correctness of decision making in prediction of such diseases as osteochondrosis of the lumbar spine and prostitutes located in the level 0.8, and the early diagnosis of these diseases is carried out with confidence 0.9.

Many theories of reflexology use ancient concepts which do not coincide with the modern medical terminology of anatomy, physiology and biophysics. This substantially reduces the trust of physicians in reflexology methods. During this research, several mathematical models for the interaction of the internal and biological active points of meridian structures have been proposed. The analysis of these models allows the specification of a list of gastric diseases for which reflex diagnostics and reflex therapy methods are most effective and also allows increasing the effectiveness of these procedures. It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained using fuzzy logic decision making.

A great number of reflexologies use ancient concepts that do not coincide with modern medical terminology of anatomy, physiology, and biophysics, substantially reducing the trust of today’s physicians in this direction of their profession. Recently, several mathematical models of internal and biological active points of meridian structures interaction have been proposed. These models allowed specification of diseases for which reflex diagnostics and reflex therapy methods are most effective and also increased the efficacy of these procedures. Good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained using fuzzy logic decision making.

The work discusses aspects of decision rule synthesis for prediction and early diagnostics of stomach diseases. The distinguishing feature of heterogeneous fuzzy rules of decision making is the fact that they use information about the energetic condition of biologically active points and also features traditionally used in medical practice such as alcohol consumption, smoking tobacco, inheritance, etc. Use of different types of the original data allows us to provide diagnostic efficiency in decisions at the level 0.9 or greater, which makes it possible to recommend the research outcome for medical practice.

Setting exposure criteria to environmental pollutants is dependent on a range of interrelated variables, therefore it is complicated to understand and model exposure levels. Fuzzy logic is a convenient approach to integrate expert judgment and mathematical modelling in one predication model. Monitoring health state of population is useful tool to develop and calibrate parameters of fuzzy logic model. Analysis of the indistinct decisive rules making a basis of creation of knowledge bases of expert systems, solving problems of monitoring of a state of environment and health of humans on the basis of collectives of the hybrid mathematical models working with diverse structure of data in the absence of their exact analytical description are considered. Disease risk was analysed for populations living in two environmental conditions, one a polluted mining community (Zheleznogorsk city) and the other a cleaner environment community (Kursk city). Diseases screening for polluted area was conducted and analysed.

A brief description of methods and software for estimation of the ergonomics of biotechnical systems ergonomics is given. The study shows that the used indicators don't give sufficient accuracy for practical purposes because they don't take into account the impact of ergonomics on the functional status, the health of the people status, and the used technical systems owing to the fuzzy and latent (hidden) nature of the links between them. A method of synthesis of hybrid fuzzy decision rules groups is proposed for the analysis of the data structure by specially developed algorithm exploratory analysis based on fuzzy application. Mathematical modelling and software showed that the confidence in the decisions made by the selected class of problems exceeds the level of 0.85. This allows recommending the use of the obtained results in clinical practice.

The theoretical outcomes and experimental results of new color model designed for accelerated image processing together with implementations of designed model to software of image processing are presented in the study. This model, as it will be shown below, may be used in modern real time video processing applications. The developed model allows to increase a speed of image processing algorithms using accelerated model of conversion to new decorrelated color space. Experimental results the proposed method can get a better performance than other existing methods.

One of today's trends is continuously increasing the number and the power ratings of the non-linear loads connected to the electric power systems. These non-linear loads, such as power- electronic converters, are the main source of the harmonics that affect the performance of the impedance-type distance relays. It was found that, in the presence of harmonics, undesired operation of such type of distance protection will occur, as the distance relay can react improperly and sense faults at zones other than the desired ones and the selectivity of the distance relays will be violated as improper relays will react while those concerned will not. It has been also shown that the fault location and the fault resistance were affecting the impedance seen by the distance relay. The data obtained from the analysis of a three-phase-to-ground fault, in a sample four-bus practical system, was used to study the behavior of the distance relay and to estimate the impedance seen by it. An algorithm, that can be used to calculate the system quantities in the presence of a harmonic source, was suggested.

The aim of the study was to suggest optimal solution for the impact of harmonics on the performance of Capacitor-Run Motor (CRM), the most commonly used single-phase motor. Therefore, investigations were carried out over the entire slip from no-load condition up to the locked-rotor condition, for harmonics of various orders in the case of using Pulse-Width Modulation (PWM) speed control. The investigations gave the ability to optimize the performance of CRM by choosing the appropriate parameters of the PWM inverter. Mathematical model for CRM supplied from voltage source contained harmonics was presented. The work was conducted by using a digital computer simulation (MATLAB software).

We apply mathematical models for the interaction of the internal and biologically active points of meridian structures. Amongst the diseases for which reflex diagnostics are effective are those of the stomach disease. It is shown that use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of stomach diseases, depending on the reaction energy of biologically active points (acupuncture points). It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained by using fuzzy logic decision-making. Analysis of resistances lower and higher than the nominal values allowed us to specify the prognostic and diagnostic models.

Determination of psycho-emotional tension on humans is performed by means of psychological tests, the electrical characteristics of biologically active points connected with the psycho-emotional sphere, and on indicators characterizing the state of a subject’s visual attention. The level of such tension is determined as an aggregate of these three components with the rules of fuzzy logic. It is shown that the resulting method can be used as an informative feature for prediction and medical diagnosis of conditions such as stomach and heart disease. The use of dissimilar parameters characterizing different areas of psychology takes into account the systemic nature of mental phenomena such as psycho-emotional tension. Fuzzy logic decision-making can correctly account for uncertainties in a parameter under consideration, combined with incomplete understanding of its properties in practice. Additionally, such an assessment of psycho-emotional tension allows one to integrate this indicator into fuzzy output systems for a broad range of tasks in the fields of medical and psychological diagnosis, including vocational suitability.

We propose a biotech measurement scheme and software based on new features for the diagnosis of backbone osteochondrosis. Together with the traditional methods of diagnosis, the energy characteristics of biologically active points (acupuncture points, or APs) are used. This new software is based on mathematical models of the internal and biologically active points of meridian structures' interaction. The information from the APs is used in the solving rules based on fuzzy logic for decision-making, together with the factors of confidence of Shortliffe, the membership functions of Zadeh, and Kullback's informativeness measures. In terms of prognostic decision rules, clinical test shows that the quality of prediction using only the energy characteristics of APs is high, with high coefficient of confidence for the control sample as well. A check on control samples allows us to recommend the obtained results for use in medical practice as a part of corresponding systems of support for decision-making.

Al-Khedher, M. A., & Alshamasin, M. S. (2012, June). SCARA robot control using neural networks. In Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference on (Vol. 1, pp. 126-130). IEEE., A SCARA industrial robot model is identified based on a 4-axis structure using Lagrangian mechanics, also the dynamic model for the electromechanical actuator and motion transmission systems is identified. A conventional PD controller is implemented and compared to neural networks control system to achieve precise position control of SCARA manipulator. The performance of the modeled system is simulated using several desired tracking motion for each joint. Neural networks control method has shown a remarkable improvement of tracking capabilities for the SCARA robot over conventional PD controller. The proposed neural network controller has the potential to accurately control real-time manipulator applications.

Many theories of reflexology use ancient concepts which do not coincide with the modern medical terminology of anatomy, physiology and biophysics. This substantially reduces the trust of physicians in reflexology methods. During this research, several mathematical models for the interaction of the internal and biological active points of meridian structures have been proposed. The analysis of these models allows the specification of a list of heart diseases for which reflex diagnostics and reflex therapy methods are most effective and also allows increasing the effectiveness of these procedures. It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained using fuzzy logic decision making.

A biotech measurement software system for determining emotional tension on humans is determined by means of psychological tests, the electrical characteristics of biologically active points connected with the psycho-emotional sphere and on indicators characterising the state of the subject’s visual attention. Each of these three areas determines several factors for psycho-emotional tension. The level of tension is determined as an aggregate of these three components with the rules of fuzzy logic. The membership functions and solving rules for the psychological tests, for acupuncture points, and for the state of visual attention are constructed using fuzzy logic theory.

For many diseases, new methods and models for their prediction, diagnosis and analysis are sought. We apply mathematical models for the interaction of the internal and biologically active points of meridian structures and can thereby specify diseases for which reflex diagnostics and reflex therapy methods are the most effective; amongst these are diseases of the gastrointestinal tract. The use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of such diseases, depending on the reaction energy of biologically active points (acupuncture points).

A complete mathematical model of SCARA robot was developed and presented in this paper including servomotor dynamics and dynamics simulation. The equations of motion were derived by using Lagrangian mechanics. Direct current (DC) servomotor driving each robot joint was studied and modelled. SCARA robot was constructed to achieve drilling operation using solid dynamics (SD) software. The performance of the robot-actuator system was examined with solid dynamic simulation and verified with MATLAB/Simulink. The results of simulations were discussed. The facilities of the programmes (investigate, design, visualise, and test an object even if it does not exists) used for kinematic and dynamic simulation of robot systems were emphasised.