scholarly journals Diagnosing of a complex technical object in four-valued logic

2018 ◽  
Vol 67 (1) ◽  
pp. 33-42
Author(s):  
Stanisław Duer ◽  
Dariusz Bernatowicz ◽  
Paweł Wrzesień ◽  
Radosław Duer
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic System ◽  
Technical Diagnostics ◽  
Technical Object ◽  
Multiple Valued Logic ◽  
Logic Testing ◽  
Signal Features ◽  
Turbine Equipment ◽  
Theoretical Foundations

This paper presents the essence of an investigation of a complex technical object with the use of four-valued logic. To this end, an intelligent diagnostic system (DIAG 2) is described. A special feature of this system was its capability of inferring k at {k = 4, 3, 2}, in which case the logic {k = 4} is applied. An important part of this work was to present the theoretical foundations describing the essence of inference in the four-valued logic contemplated. It was also pointed out that the basis for classification of states in the multiple-valued logic of the diagnostic system (DIAG 2) was the permissible interval of changes in the values of diagnostic signal features. Four-valued logic testing was applied to a system of wind turbine equipment. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence

scholarly journals Examination of informativeness of diagnoses expressed with multiple-valued logic

2018 ◽  
Vol 67 (2) ◽  
pp. 169-178
Author(s):  
Stanisław Duer ◽  
Dariusz Bernatowicz ◽  
Paweł Wrzesień ◽  
Radosław Duer
Keyword(s):  
Artificial Intelligence ◽  
Wind Turbine ◽  
Diagnostic Test ◽  
Diagnostic System ◽  
Diagnostic Information ◽  
Technical Diagnostics ◽  
State Table ◽  
Object State ◽  
Multiple Valued Logic ◽  
Turbine Equipment

This paper presents the essence of an examination of informativeness in the diagnostic information outputs expressed with multiple-valued logic. The diagnostic test required for the examination was completed on wind turbine equipment. The examination included a constant set of determined diagnostic output values. The DIAG 2 diagnostic system was used for the examination and the diagnostic test. DIAG 2 is a smart diagnostic system capable of any inference k of the set {k = 2, 3, 4}. The examination results were expressed in an Object State Table, separately for each k-valued logic of inference tested. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence

scholarly journals Inference diagnostic state of the technical object in logic k-valuable

2017 ◽  
Vol 66 (1) ◽  
pp. 115-126
Author(s):  
Stanisław Duer
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Theoretical Basis ◽  
Diagnostic Reasoning ◽  
Significant Part ◽  
Technical Diagnostics ◽  
Technical Object ◽  
K Value ◽  
The Third

The article presents the problem of describing the theoretical basis for inference (decision-making) in the multi-valued logic. A significant part of the article concerns the descrip-tion of the basis for the development of the logic k-value, where k = 2, 3, 4. In the work, as a basis for the development of multivalent logics, the interval suitability for two-valued logic has been taken. The third chapter is an example of diagnosing the technical object in the logic of 4-valuable. Keywords: technical diagnostics, diagnostic reasoning, multivalent logic, artificial intelligence

scholarly journals Information with diagnostic technical object used to create experts’ knowledge base

2017 ◽  
Vol 66 (2) ◽  
pp. 91-106
Author(s):  
Radosław Duer ◽  
Stanisław Duer
Keyword(s):  
Diagnostic System ◽  
Knowledge Bases ◽  
Diagnostic Information ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Wind Power Plant ◽  
Technical Object ◽  
Pro Bono ◽  
Definition Of ◽  
Functional Diagnostic

The article presents the problem of determining diagnostic information for the purpose of testing a complex technical object. To this end, the essence of the development of the functional-diagnostic model was presented and described on the example of the wind power plant. Based on the developed model of the object, diagnostic information has been determined, which consists of two components: a set of basic elements and a set of diagnostic signals, which are worked out by the designated elements in the functional groups of the object. An important aspect in this paper is the shift in the pro bono definition of a set of diagnostic signals. Knowledge of the set of diagnostic signals and their nominal (master) signals is the basis for determining the range of changes in diagnostic signals that are the basis for the diagnosis of an object using the intelligent diagnostic system (DIAG 2) or using knowledge bases for expert systems. Keywords: technical diagnostics, diagnostic reasoning, multivalent logic, artificial intelligence

scholarly journals Diagnostics of low-capacity solar power station equipment with 2- and 3-valued logic

2018 ◽  
Vol 67 (3) ◽  
pp. 185-195
Author(s):  
Stanisław Duer ◽  
Paweł Wrzesień ◽  
Radosław Duer ◽  
Dariusz Bernatowicz
Keyword(s):  
Low Power ◽  
Solar Power ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Power Station ◽  
Research Issues ◽  
Multiple Valued Logic ◽  
Differential Distance ◽  
Solar Power Station

The paper outlines research issues relating to 2- and 3-valued logic diagnoses developed with the diagnostic system (DIA G 2) for the equipment installed at a low-capacity solar power station. The presentation is facilitated with an overview and technical description of the functional and diagnostic model of the low-power solar power station. A model of the low-power solar power station (the tested facility, a.k.a. the test object) was developed, from which a set of basic elements and a set of diagnostic outputs were determined and developed by the number of functional elements j of j. The work also provides a short description of the smart diagnostic system (DIA G 2) used for the tests shown herein. (DIA G 2) is a proprietary work. The diagnostic program of (DIA G 2) operates by comparing a set of actual diagnostic output vectors to their master vectors. The output of the comparison are elementary divergence metrics of the diagnostic output vectors determined by a neural network. The elementary divergence metrics include differential distance metrics which serve as the inputs for (DIA G 2) to deduct the state (condition) of the basic elements of the tested facility. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence.

CONTENT OF TEACHING THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN A SCHOOL INFORMATICS COURSE

2020 ◽  
pp. 3-10
Author(s):  
I. V. Levchenko
Keyword(s):  
Artificial Intelligence ◽  
General Education ◽  
Extracurricular Activities ◽  
Information Technologies ◽  
Learning Experience ◽  
9Th Grade ◽  
Modern Information ◽  
Further Development ◽  
Form Variant ◽  
Theoretical Foundations

The article considers the feasibility of integrating artificial intelligence technologies into school education and identifies a problem in identifying didactic elements in the field of artificial intelligence, which must be mastered in a school informatics course. The purpose of the article is to propose variant of the content of teaching the elements of artificial intelligence for the general education of schoolchildren as part of the curricular and extracurricular activities in informatics. An analysis of the psychological, pedagogical and scientific-methodical literature in the field of artificial intelligence made it possible to identify the appropriateness of teaching schoolchildren the elements of artificial intelligence in the framework of a comprehensive informatics course, as the theoretical foundations of modern information technologies. Summarizing and systematizing the learning experience of schoolchildren in the field of artificial intelligence made it possible to form variant of the content of teaching the elements of artificial intelligence, which can be implemented in a compulsory informatics course for 9th grade, as well as in elective classes. The results of the study are the theoretical basis for the further development of the components of the methodological system of teaching the elements of artificial intelligence in a school informatics course. The research materials may be useful to specialists in the field of teaching informatics and to informatics teachers.

scholarly journals Classification of artificial intelligence technologies to determine the civil liability

2021 ◽  
Vol 1794 (1) ◽  
pp. 012001
Author(s):  
A Alekseev ◽  
O Erakhtina ◽  
K Kondratyeva ◽  
T Nikitin
Keyword(s):  
Artificial Intelligence ◽  
Civil Liability

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

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