Authors

Computer-aided system for joint investment funds evaluation using back-propagation neural nets

The authors of the article have developed software that helps analyze and forecast profitability for joint investment funds (JIF) using back-propagation neural nets. The authors presented moving window algorithm for time series construction for neural nets training. The results show that neural nets might be used for correct forecasting of JIF profitability.

Constructing integrated model for risk management of metallurgical enterprise

A method of risk management in the metallurgical enterprise is proposed. Activities specific features are considered and risk management process is decomposed in such a way that every task is mathematically described and allocated to one of the steps originated from the decomposition.

Choosing a supplier in supply chain with a fuzzy logic algorithm

Logical-statistical algorithm for the identification of through pores and its application to the analysis of the nonmaterial structure

Simulation multiagent fuzzy logic model for industrial companies marketing decision making under uncertainty

Heuristic computational tools the integrated logistic support of life cycle of pipeline systems of manufacturing enterprises

Information-measuring system geographically remote locations in the gas transport sector

On the 60th anniversary of Yury B. Rubin

Computer-aided analysis and optimization of structural and parametrical reliability of complex gas supply pipeline systems

Program complex for life cycle support of pipeline systems of petrochemical companies

Preliminary assessment of the pragmatic value of information in the classifiсation problem based on deep neural networks

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data. Read more...

Adaptive‑multi‑index‑cluster algorithm for comprehensive assessment of the impact of chemical pollution on forests using satellite photographs

An original adaptive-index-clustering algorithm is proposed: “Managed vegetation index”. An original adaptive-multi-index-cluster algorithm for comprehensive assessment of the impact of chemical pollution on forests using satellite photographs is proposed, which is distinguished by the use of an adaptive procedure for the formation of pixel clusters displaying a plurality of spectral channels of a photographic image of each type of vegetation state of a forest stand in the zones of chemical pollution of forest tracts, as well as using the procedure for calculating the weighted average values of complex vegetation indices for each zone of chemical pollution, which allows, based on the values of complex vegetation indices, to determine various biological, phytological and physico-chemical states of forest areas.It should be noted that in order to solve the complex problem of constructing complex indices linked to ecological zones, it is proposed to use the simple idea of increasing the quality of modeling and forecasting by expanding the amount of information. The proposed problem can be solved using a statistical analysis of data on the distribution of pixels whose belonging to ecological zones is known in advance. The development of the algorithm is based on the following prerequisites: (1) using a linear combination of individual classical vegetation indices of the state of forest areas, it is possible to create a new specialized complex vegetation index that makes it possible to identify ecological zones in forest areas according to the levels of impact on forests of chemical pollution of industrial enterprises; (2) the possibility of using specialized complex vegetation indices in the form of weighted average linear combinations of classical vegetation indices. Specialized complex vegetation indices of adaptive selection of weight coefficients are capable of displaying various biological, physicochemical and ecological characteristics of the state of forests based on clustering of satellite image pixels. The proposed algorithm makes it possible to calculate, as a result of clustering, more accurate estimates of the total areas of ecological zones of forest tracts, which can be used as a basis for assessing the degree of ecological degradation of forest tracts and environmental damage. Read more...