There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The article examines the behavior of an intellectual agent in a competitive environment. An OODA loop is selected to model the behavior. The interaction of the stages of the OODA loop (observation, orientation, decision-making, action) with the ontology of the tasks and the domain within which this agent functions is considered.

Method of Ontology Use in OODA

In the given article is functional logistic model of commercial content processing as the content life cycle stage in electronic commerce systems proposed The model of commercial content processing describes the information resources forming in electronic content commerce systems and automation technology simplifies the commercial content management In the given article the main problems of electronic content commerce and functional services of commercial content processing are analyzed The proposed model gives an opportunity to create an instrument of information resources processing in electronic commerce systems and to implement the subsystem of commercial content formation, management and support

Set-theoretic models and unified methods of information resources processing in e-business systems

The optimal decision-making task based on the Markovian learning methods is investigated. The stochastic and deterministic learning methods are described. The decision-making problem is formulated. The problem of Markovian learning of an agent making optimal decisions in a deterministic environment was solved on the example of finding the shortest path in the cell space. The mathematical formulation of the decision - making problem with deterministic and stochastic strategies based on recurrent estimation of criterion functions of utility of states and efficiency of options of actions of the agent was provided. The evaluation of criterion functions values takes place in real time on the basis of reinforced Q-learning and does not require a model of the environment, which is important for practical applications of decision-making in conditions of uncertainty. The algorithmic and software tools for the decision making modelling in uncertainty conditions are developed. The computer simulation results of decision-making process in cellular space are discussed and presented.

Markovian Learning Methods in Decision-Making Systems

/pdf/development-of-intelligent-system-for-visual-passenger-flows-17kjhjffop.pdf

Development of Intelligent System for Visual Passenger Flows Simulation of Public Transport in Smart City Based on Neural Network.

The article deals with the modified paradigm of the bee colony for tourist routes by solving combinatorial problems on graphs: selection in the graph of an independent subset of vertices, finding the maximum pairing in the graph, coloring the graph, highlighting the click in the graph. Based on the analysis of the behavioral model of bee colony self-organization, methods and mechanisms of formation of corresponding representations of solutions of the considered combinatorial problems on graphs are developed. Methods of search space forming are considered. The position in the search space is presented as an ordered list. The key operation of the bee algorithm is the study of perspective positions and their surroundings in the search space. The paper proposes a method of decision edges forming with an adjustable degree of similarity and closeness between them. Three approaches are proposed to determine the number of foraging agents that are sent to the vicinity of each base position.

/pdf/tourist-itineraries-plan-design-based-on-the-behavior-of-bee-6eadfusfws.pdf

Victoria Vysotska

Papers

Method of Ontology Use in OODA

Set-theoretic models and unified methods of information resources processing in e-business systems

Markovian Learning Methods in Decision-Making Systems

Development of Intelligent System for Visual Passenger Flows Simulation of Public Transport in Smart City Based on Neural Network.

Tourist Itineraries Plan Design Based on the Behavior of Bee Colonies.