ISSN 1991-2927
 

ACP № 1 (63) 2021

Author: "Anton Alekseevich Romanov"

Anton Alekseevich Romanov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems of UlSTU; an author of articles in the field of intelligent systems of data storage and processing. e-mail: romanov73@gmail.comA.A. Romanov

Aleksei Aleksandrovich Filippov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems of UlSTU; an author of articles in the field of ontological modeling, intelligent systems of data storage and processing. e-mail: al.filippov@ulstu.ruA.A. Filippov

An approach to the contextual analysis of time series63_6.pdf

Forecasting methods despite their conventions and limitations are the evolution of descriptive analytics mechanisms. Any model of the real-world objects works only under conditions of restrictions and agreements. The same conclusion can be made for the forecasting process, that it is not possible to forecast future state of the researched objects for 100%. However, building the most accurate forecast under the given conditions is the key. Modern data mining methods are based on a variety of models. However, such models can’t define the components of researched objects and processes except those contained in their models. The context allows using additional domain knowledge in describing the behavior of objects and processes in the form of qualitative assessments of their state. The same dataset in different domains will have various models and analysis results. The article deals with an approach to the domain context formation based on the ontology for analyzing time series of industrial processes indicators. The logical representation of the ontology based on the ALCHI(D) descriptive logic is also considered. The article describes as well experimental results confirming the correctness and effectiveness of the approach proposed.

Time series, time series analyzing, context, domain, ontology.

2021_ 1

Sections: Information systems

Subjects: Information systems.



Anton Alekseevich Romanov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University (UlSTU); Associate Professor of the Department of Information Systems at UlSTU; an author of articles in the field of intelligent systems for data storage and processing. e-mail: romanov73@gmail.comA. A. Romanov

Aleksei Aleksandrovich Filippov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems at UlSTU; an author of articles in the field of ontological modeling, intelligent systems for data storage and processing. e-mail: al.filippov@ulstu.ruA. A. Filippov

Nadezhda Glebovna Yarushkina, Doctor of Sciences in Engineering, Professor; graduated from Ulyanovsk Polytechnic Institute; Interim Rector of Ulyanovsk State Technical University, Head of the Department of Information Systems at UlSTU; an author of more than 300 scientific papers in the field of soft computing, fuzzy logic, and hybrid systems. e-mail: jng@ulstu.ruN. G. Yarushkina

Vladimir Anatolyevich Maklaev, Candidate of Sciences in Engineering; graduated from Radioengineering Faculty of Ulyanovsk Polytechnic Institute; Director General of Federal Research-and-Production Center Joint-Stock Company ‘Research-and-Production Association ‘Mars’. The area of scientific interests relates to the computer- aided design systems. e-mail: mars@mv.ruV. A. Maklaev

The decision support module of information environment for technological support of production60_7.pdf

Making management decisions requires a specialist to have extensive knowledge of the problem area and the current state of the organization. There is a need for timely and urgent management decision support in the activity of any large organization. The decision support module (DSM) was created to solve that kind of problem in the task of balancing production capacities. The main task of the DSM is a linguistic summarization of the state of the main production indicators and the formation of recommendations that allow the decision-maker to develop a specific strategy for balancing production capacities. The DSM functionality is based on the knowledge base in the form of ontology with the set of expert SWRL-rules. This article describes methods for modeling and forecasting the dynamics of the main production indicators based on type- 2 fuzzy sets, and the approach to summarizing the time series of production indicators and forming recommendations for optimizing production based on knowledge engineering methods.

Data-driven decision making, type-2 fuzzy sets, time series forecasting, ontology, inference.

2020_ 2

Sections: Artificial intelligence

Subjects: Artificial intelligence.



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