Parallel-sequential adder-subtractor with the highest digits forward on neurons

The article deals with the development of a parallel-sequential adder-subtractor that performs arithmetic operations of addition and subtraction of binary numbers in the format with a fixed comma with the highest digits forward. The result of performing arithmetic operations is the sum and difference of binary numbers in the direct code of eight digits. The sum and difference of numbers is calculated on neuropositive elements, the transfer to the highest digits when summing and the loan from the highest digits when subtracting is determined by the majority elements. The algorithm for adding numbers in direct codes allows you to get the result in direct code. The signed digits of numbers determine which operation should be performed on numbers using the sum modulo two operation. If the characters are the same, the result will be zero. Otherwise, the result will be one. After that, the addition or subtraction operation is selected. Summation is performed if the numbers have the same signs, the result is assigned the sign of the first number. Subtraction is performed if the numbers have different signs, the result is assigned the sign of a larger modulo number. The adder-subtracter senior digits forward on neurons contains: block input, block comparatii, the block parallel-serial addersubtracter, the unit registers a larger number, the unit of determining the transfer and loan, a unit registers a smaller number of unit registers a result, the control unit, majority, threshold and neural elements. The device can be used as an arithmetic co-processor in a computer system. It significantly speeds up calculations of both simple arithmetic operations and results of various mathematical functions.

Neuroclusterization method of stabilometric data

Stabilometry is a modern method for assessing the functional state of a person by the ability to maintain a stable balance of an upright posture. Technically, the implementation of the stabilometry method consists in measuring, with the help of specialized devices, the values that make up the support reaction, with the subsequent determination, according to these measurements, of the coordinates of the center of body pressure on the support. The nature of the migrations of the center of pressure during the stabilometric study is a source of information about the features of the processes of postural regulation. At the same time, up to the present time, there is a problem of the correct interpretation of the results of stabilometry. The adequacy of the conclusions is largely determined by the human factor, i.e. qualification of a specialist analyzing stabilometry data. Thus, in our opinion, the task of objectifying the assessment of stabilometry results is urgent. The aim of this work is to study the possibility of applying the neurocluster method using self-organizing neural networks to objectify the analysis of stabilometry data. The authors proposed a technique for analyzing the structure of individual and group stabilometric data by clustering them using selforganizing Kohonen neural maps with Euclidean metrics. Neuroclusterization of stabilometric data allows in automatic mode (without human intervention) to identify the type of group of subjects corresponding to the norm or pathology, various types of pathologies, as well as individual biometric characteristics of the subjects. The subsequent analysis of the individual characteristics of the data of the subjects, grouped in this way, makes it possible to detect deviations indicating the presence of abnormalities or the formation of various pathological conditions, which can be useful for the early diagnosis of diseases.

Organizational theory of the distribution of the elements of consciousness formed by information and technical immunity of a system of martial arts of the East

The article deals with the martial arts of the East (MAE) in relation to the organizational system of A.A. Bogdanov, from the point of view of their life stability. It is necessary for the definition of MAE as an art, not a physical education and sports. It contributed to the identification of technical immunity and a strict hierarchy of these systems. It allowed us to identify MAE as a system that promotes the development of the talent of individual, forming a creative personality. It became possible to solve the problem of consciousness through a conscious choice of the desired element of the system, depending on the range of situations. The considered MAE became a similarity of living system and an analogy of human brain. Considering an information-hierarchical structure, it was given the name “supraorganizational”, because the process of its “reproduction” and the significance of this process for the system were discovered. The identification of MAE as a living creative structure capable of interacting with space, in order to acquire and transfer properties to influence the recipient, made it possible to define them as the basis for understanding the art, particularly fine art. Technical immunity and technical homeostasis were able to justify the emergence of the immunological android as a node in the technology of living systems between the individual and the artificial intelligence.

Neural network processing of radar scanning data array

This article deals with the digital processing of a matrix radar image. The information received from the radar scanner needs to be transformed to enable visual perception. The article describes the main methods of digital processing of matrix data, presents the images transformed by them. The aim of the article was the development of a radar data processing algorithm that identifies the contours and edges of examined objects. The authors propose an algorithm for isolating the geometric structure of the scanned area. The difference between the processing method and the known analogues is based on the nature of the change in the values of the array being processed and consists in the double operation of extracting the gradient of the distribution of values. The software implementation of the algorithm is made in C++ using methods from an open library of computer vision. The efficiency of the algorithm was estimated based on comparison with the algorithms for determining edges based on linear filtering and neural networks. The results of the work can be used to create software for mobile short-range radar devices. Imaging from object boundaries and their edges provides spatial perception of the image by the operator, and free areas are available for rendering additional information. This solution allows you to combine scanning devices and thereby increase the information value of the result.

Radical schemes for the implementation of section structures of the RADICAL language when modeling complex purposeful system

Complex artificial purposeful systems and their software and hardware are characterized not only by achievements, but also disadvantages leading to significant losses. Modern automation tools do not fully cope with the existing problems. To solve the problems of complex systems, new effective tools are needed, new modeling technology. This technology should cover all significant aspects of the problem area. It seems that such technology should be based on radical modeling and the universal language of radical schemes RADICAL. A radical is a system characterized by both active (working) and passive states. Being connected with each other radicals form schemes of radicals. These schemes are constructions of the RADICAL language. In the aggregate, these schemes realize radical environment – radical model of united problem area of complex system. That is, the problem area is represented by a single global scheme of radicals. The work with such a scheme is carried out using the universal language of radical schemes RADICAL, applicable to the problem area of any complex system by constructing sections of the RADICAL language, expressed by the schemes of radicals. The purpose of the work is to consider the use of radical schemes for the implementation of the structures of sections (sequences of sections) of the RADICAL language when modeling complex system. The results of the work are descriptions of some typical schemes of radicals intended for the implementation of the section structures of the RADICAL language when modeling complex purposeful systems. Something significant sequences of sections are considered. The practice of using of structures of cross-sections of the media of radicals, expressed by the schemes of radicals, indicates the expediency of they use for radical modeling of problem areas of complex purposeful systems, for the development and modification of software and information support of such systems.

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

Approaches to motion control intellectualization of a mobile robot

The paper considers the principles of intelligent motion control of mobile robots using the example of omni-wheel modules. The proposed design solution uses components of movement intelligence in any direction, receiving commands from a human operator or above a standing automatic control device, consisting of an angle of movement direction and the required distance of movement. This paper presents an embodiment of using omni-wheels to move a mobile robot over a flat surface. Features of device and application of drive with three omni-wheels in comparison with differential drive are considered. Kinematics, basic principles of motion control formation, hardware and software complex for its implementation are described. There were revealed two alternative methods of organization of drive control in conditions of shortage of low-level hardware resources on the basis of 8-bit microcontroller, their advantages and disadvantages have been analyzed. Process support and materials have been presented that allows realizing the competitive advantages of development while minimizing the cost of components. Features of mobile robot travel route development have been mentioned on the example of competitive practice.

Hierarchical pyramidal subsampling in deep convolutional networks for visual pattern recognition

The main problem of known deep convolutional neural networks (CNN) is that they require a fixed-size input image. This requirement is “artificial” and can reduce recognition accuracy for images or its parts of arbitrary size/scale. The paper proposes a strategy of combining “hierarchical pyramidal subselection” to eliminate the above restriction. The structure of the neural network using the proposed combining strategy allows the generation of prediction regardless of the size/scale of the original image, and also improves the accuracy of recognition. Features of application of CNN for identification and recognition of defects of conducting pattern of printed circuit board blanks have been considered. Features of defects of conductive pattern of printed circuit board blanks have been briefly discussed. The invention proposes the use of artificial CNN, which have advantages in speed and accuracy in solving problems of object recognition on images relative to existing methods. The focus is on the architecture of CNN using hierarchical pyramidal subselection. Capabilities of application of CNN for recognition of defects of conducting pattern of printed circuit board blanks have been shown. Proposed method of hierarchical pyramidal subselection in deep convolutional networks has been implemented in software complex, which allows processing digital data of photographs of conducting pattern of printed circuit boards, in particular during their flaw detection, and can be used for localization of existing defects of conducting pattern. The conclusion draws the possibilities of using methods and means of image processing in flaw detection of radio-electronic equipment and instruments

Solving non-standard problems by a computer system

On the one hand, modern psychology presents a wide range of opinions from the complete denial of the possibility of an adequate theoretical description of the mental in mathematical terms to the recognition of the timeliness and even inevitability of such a description. On the other hand, many developers of traditional AI, i.e. systems based on rules, as well as systems based on deep learning networks of artificial neurons, and their various hybrids either use, most often subconsciously, the most primitive psychological concepts, or believe that they do not need psychological knowledge at all. Therefore, the problem consists of two interrelated parts. The first is whether it is possible to create algorithms of human thinking that are adequate to the facts known in psychology on the basis of the general theory of the psyche, which widely uses the mathematical apparatus. The second is whether it is possible to create a computer system based on these algorithms that can solve the most difficult (non-standard) problems in different fields of knowledge, using what most researchers refer to as "common sense". The goal of the article is to create a computer system capable of solving non-standard problems in natural Russian, using algorithms of human thinking and check its basic parameters. AlNikOr – computer system is created. AlNikOr can solve non-standard problems in natural Russian, using algorithms of human thinking. Its efficiency is shown by the example of solving a non-standard problem in physics. Computer systems based on AlNikOr can be used to solve real non-standard problems in various fields of science and technology.

System of automatic analysis of methods for recognizing the emotional state of a person

The introduction of methods and means of digital transformation of industry and the social sphere poses new challenges. One of these tasks is the management of active systems, in which simple registration and identification of the initiators of actions is not enough, but a deeper assessment of their state, including psychophysical and emotional, is required. The article is devoted to the analysis of methods and means of recognizing the emotional state of a person. Approaches to automated recognition of a person's emotional state based on primary, secondary and more complex features are analyzed. The main focus is on a comprehensive approach to recognition of a person's emotional state based on the analysis of visual and audio channels using neural networks and computer psyche algorithms. The target of the article is a formalization of methods and development of means to recognize a person's emotional state according to complex audiovisual criteria. Analyzed software for recognizing the emotional state of a person: FaceReaderNoldus, EmoDetect, FaceSecurity, Microsoft Oxford Project Emotion Recognition, eMotion Software, MMER_FEASy. Methods used to recognize the emotional state of a person by his face have been investigated, such as: the method of basic components, the Viola-Jones method, template comparison, the Hopfield neural network, the method based on the localization of key points on the face and the method based on texture information. Separately analyzed methods of recognizing the emotional state of a person from his speech. invention proposes a solution for multilevel recognition of emotional state of a person based on using algorithms of neural networks and computer psyche. Software for recognizing the emotional state of a person was analyzed: the developed approach can be used for various digital applications, ranging from the analysis of the psychophysiological and emotional state of a person - an operator (pilot, driver, etc.), to multimedia mobile applications for analyzing the emotional state of an interlocutor. The trend for remote work and self-employment opens up such areas of application of these applications as consulting psychologists, interviewing online, optimizing the human resources of companies and organizations. Prospects for using the latest versions of emotional analyzers are shown.