Methods of cognitive-graphical representation of information for effective monitoring of complex technical systems

Емельянова, Юлия Геннадиевна; Фраленко, Виталий Петрович

doi:10.25209/2079-3316-2018-9-4-117-158

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Strictly accurate mapping of characteristic and general signs of object classes is a challenged issue when visualizing solutions in multidimensional continuous spaces [20][21][22][23][24][25][26][27]. It is required to analyze N w (N w -1)/2 slices to unambiguously identify a class based on an OLAP cube.…”

Section: Visualization and Interpretation Of Classesmentioning

confidence: 99%

Interpretation of a trained neural network based on genetic algorithms

Pimenov

2020

ICS

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Introduction: Artificial intelligence development strategy involves the use of deep machine learning algorithms in order to solve various problems. Neural network models trained on specific data sets are difficult to interpret, which is due to the “black box” approach when knowledge is formed as a set of interneuronal connection weights. Purpose: Development of a discrete knowledge model which explicitly represents information processing patterns encoded by connections between neurons. Methods: Adaptive quantization of a feature space using a genetic algorithm, and construction of a discrete model for a multidimensional OLAP cube with binary measures. Results: A genetic algorithm extracts a discrete knowledge carrier from a trained neural network. An individual's chromosome encodes a combination of values of all quantization levels for the measurable object properties. The head gene group defines the feature space structure, while the other genes are responsible for setting up the quantization of a multidimensional space, where each gene is responsible for one quantization threshold for a given variable. A discrete model of a multidimensional OLAP cube with binary measures explicitly represents the relationships between combinations of object feature values and classes. Practical relevance: For neural network prediction models based on a training sample, genetic algorithms make it possible to find the effective value of the feature space volume for the combinations of input feature values not represented in the training sample whose volume is usually limited. The proposed discrete model builds unique images of each class based on rectangular maps which use a mesh structure of gradations. The maps reflect the most significant integral indicators of classes that determine the location and size of a class in a multidimensional space. Based on a convolution of the constructed class images, a complete system of production decision rules is recorded for the preset feature gradations.

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Section: Visualization and Interpretation Of Classesmentioning

confidence: 99%

Interpretation of a trained neural network based on genetic algorithms

Pimenov

2020

ICS

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Hybrid Network Structures and Their Use in Diagnosing Complex Technical Systems

Yakimov¹,

Maltsev²

2021

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An approach to the technical diagnostics of complex technical systems based on the results of telemetry information processing by an external monitoring and diagnostics system using hybrid network structures is proposed. The principle of constructing diagnostic complexes of complex technical systems is considered, which ensures the automation of the technical diagnostics process and is based on the use of models in the form of hybrid network structures for processing telemetric information, including multilayer neural networks and discrete Bayesian networks with stochastic learning. A model of changes in the parameters of complex technical systems technical state based on multilayer neural networks has been developed, which makes it possible to form a probabilistic assessment of attributing the current situation of complex technical system functioning to the set of functions considered situations according to individual telemetry parameters, and multilevel hierarchical model of complex technical systems technical diagnostics based on a discrete Bayesian network with stochastic learning, which allows aggregating the information received from neural network models and recognizing the current situation of complex technical system functioning. In the conditions of functioning emergencies of the complex technical system, according to the results of processing telemetric information, faulty functional units are localized and an explanation of the cause of the emergency is formed. The stages of complex technical systems technical diagnostics implementation using the proposed hybrid network structures in the processing of telemetric information are detailed. An example of using the developed approach to solving problems of spacecraft onboard system technical diagnostics is presented. The advantages of the proposed approach to the technical diagnostics of complex technical systems in comparison with the traditional approach based on analysis of telemetry parameters values belonging to the given tolerances are shown.

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Support of the Decision-Making Process using the Unified Graphic Visualization of Activity (UGVA) Notation

Uglev

2023

HoBMSTU.SIE

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The paper considers the problem of graphic support of the decision-making processes when working with a variety of complex multi-parameter objects that require comparison. It notes complexity of the visualization process, when it is necessary to visualize the object in dynamics with an emphasis on its activity and combine the categories of past, present and future (the afferent synthesis model according to Anokhin P.K.). Prerequisites and genesis of the Unified Graphic Visualization of Activity (UGVA) method appearance are described, as it the development of the well-known "Chernoff Faces" approach. Stages of implementing the methodology to form anthropomorphic images for visualization of the complex multi-parameter objects in the UGVA are described. Examples of the sets of images are provided, where curricula, student performance, project passports and employees’ activities in the workplace are considered as the objects for comparison. Methodological generalizations are proposed allowing a systematic approach to selection of option in visualizing the anthropomorphic images taking into account specifics of the information decomposition on the data axes and the types of symmetry. A coding system for various options of images in the UGVA is given that uses a combination of letters of the Latin and Greek alphabets

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Methods of cognitive-graphical representation of information for effective monitoring of complex technical systems

Cited by 5 publications

References 12 publications

Interpretation of a trained neural network based on genetic algorithms

Interpretation of a trained neural network based on genetic algorithms

Hybrid Network Structures and Their Use in Diagnosing Complex Technical Systems

Support of the Decision-Making Process using the Unified Graphic Visualization of Activity (UGVA) Notation

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