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№4
Publication date:
09 September 2024
Journal articles №3 2016
31. Forecasting optimal duration of a beer main fermentation process using the kalman filter [№3 за 2016 год]Authors: Niyonsaba T. (nitherence@mail.ru) - ver State Technical University; Pavlov V.A. (nitherence@mail.ru) - ver State Technical University (Associate Professor), Ph.D;
Abstract: One of the most important processes of beer production is the main process of fermentation. In this process, the wort transforms into beer. The quality of beer depends on the dynamics of wort parameters. The main fermentation process continues for 10 days and requires high costs. Therefore, the main purpose of this article is to forecast the optimal duration of the beer main fermentation process and provide its optimal control. The Kalman filter can provide optimal control of the main fermentation process. It also estimates state variables of the main fermentation process taking into account the characteristics of random effects at the object’s input and filters measuring noise. The initial values of raw materials and control parameters of the beer main fermentation process can be determined based on the predicted completion time of the main fermentation process.
Keywords: extract, alcohol, time, temperature, kalman filter, wort, main fermentation
Visitors: 5288
32. The architecture of the system for osteoporotic fracture diagnostics and risk assessment [№3 за 2016 год]
Authors: Dmitriev G.A. (kirsanich@mail.ru) - Tver State Technical University, Ph.D; Al-Fakih Ali Saleh Ali (alfakih.ali@mail.ru) - Tver State Technical University;
Abstract: The use of information technologies in medicine for diadnosis of various diseases needs improvements in data storage and processing. To assess the osteoporotic fracture risk the specialists use a computational model based on the Bayesian inference scheme. A prediction task is considered as a classification task, i.e. a task of finding the posterior probability of patient belonging to one of two original classification classes. The factor that determines the possibility of osteoporotic fracture is a multidimensional random variable. To evaluate its performance it is required to store and process large information volumes. The complex includes informational and computational components. The computational component contains methods of data mining aimed at detecting patterns and trends, as well as at identifying existing relationships in multidimensional arrays of clinical data. The informational component contains sample data models in the form of multidimensional cubes that are formed on the base of OLAP-technologies and contingency tables. Information and computer components are combined into a single system model. Data output of the information component is used as input for the computer component, which is used to display the statements under conditions of uncertainty and incomplete information. It is based on probability calculation methods and Bayesian networks. The article examines the use of Microsoft SQL Server Analysis Services as a platform to create and analyze multivariate models based on Data Mining technology.
Keywords: information system, medical diagnostics, osteoporosis, bayesian networks
Visitors: 9940
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