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Aleksandr Yu. Mishin

Bio: Aleksandr Yu. Mishin is an academic researcher from Financial University under the Government of the Russian Federation. The author has contributed to research in topics: Production (economics) & Business. The author has co-authored 2 publications.

Papers
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Journal ArticleDOI
TL;DR: In this article , a distributed multi-agent information control system for the operation of torpedo ladle cars is proposed and described, where the results for detecting burnout zones of a lining by the standard system and newly developed system are presented.
Abstract: The paper presents that during the operation of torpedo ladle cars in metallurgical production, problems periodically arise with ensuring the safety of their use. The authors have highlighted the relevance and necessity of the solution to the problem of diagnosing the lining state of ladle cars to ensure their safe functioning. To solve the problem of diagnosing the lining state of ladle cars for the maritime industry, an algorithm for detecting burnout zones of a lining based on a neural network has been developed. The authors propose and describe a distributed multi-agent information control system for the operation of torpedo ladle cars. The results for detecting burnout zones of a lining by the standard system and newly developed system are presented. To automate assessing the lining state of the ladle car and support in making decisions regarding operation mode of the ladle cars, the software has been developed.

8 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: In this paper, the authors propose to improve the effectiveness of state strategic planning by increasing the level of validity of the planned and forecast calculations, which is the most expedient area of using the above mechanisms of its development.
Abstract: The purpose of this work is to prepare proposals to improve the effectiveness of state strategic planning by increasing the level of validity of the planned and forecast calculations. In modern conditions, the main important directions for ensuring the further development of domestic strategic planning are target management and production regulation. In this work, the following issues have been resolved: 1) As a result of the analysis of methodological approaches to strategic planning in the USSR and Russia, the initial methodological premises were identified and the most expedient area of using the above mechanisms of its development was substantiated; 2) Based on the study and scientific generalization of domestic and foreign research in the field of long-term planning, methodological and methodological foundations for creating its regulatory framework have been developed, including principles, procedures, composition and structure; 3) Proposals have been substantiated for the creation in the Russian Federation of a special ministry - strategic development.
Journal ArticleDOI
01 Sep 2020
TL;DR: In this article, a comparative analysis of specific features, advantages and disadvantages of capital depreciation accrual methods, indices of average annual manufacturing capacity actual employment in the context of the most important types of output industrial products is presented.
Abstract: The purpose of this paper is to prepare suggestions on the use of capital depreciation methods as the key tool of industrial enterprise and organization financial soundness improvement that provides the gained profit increase, and thus the capital position.The paper solves the following issues: 1) In the result of comparative analysis of specific features, advantages and disadvantages of depreciation accrual methods, indices of average annual manufacturing capacity actual employment in the context of the most important types of output industrial products it was concluded that the majority of such methods (except the method for depreciation accrual in proportion to the gross output) doesn’t consider the actual load of process equipment; 2) the methodological approach to modification of average annual manufacturing capacity actual employment was substantiated by introducing the calculation formula and the coefficient of average annual manufacturing capacity actual employment; 3) calculations on annual 2016 and 2017 returns were carried out for several economic activities in order to assess product cost reduction (growth of profits) due to the proposed methodological approach.

Cited by
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Journal ArticleDOI
TL;DR: In this paper , a marine diesel engine exhaust temperature prediction model is constructed, combining the feature extraction ability of CNN and the time series data prediction ability of the bidirectional gated recurrent unit (BiGRU), and the results show that the model can accurately determine the fault early warning of the diesel engine and provide a new reference for the health management of intelligent marine equipment.
Abstract: The normal operation of the marine diesel engine is of great significance to ensure the normal navigation of the ship. Predicting its operation state and judging whether the diesel engine is in the abnormal state in advance can guarantee the safe navigation of the vessel. In this paper, combining the feature extraction ability of the convolutional neural network (CNN) and the time series data prediction ability of the bidirectional gated recurrent unit (BiGRU), a marine diesel engine exhaust temperature prediction model is constructed. The results show that the mean square error (MSE) of the prediction model is 0.1156, the average absolute error (MAE) is 0.2501, and the average absolute percentage error (MAPE) is 0.0005336. Then, according to the residual distribution between the predicted value and the actual value of the model output and the standard deviation of the residual calculated by using the sliding window, we set the alarm threshold, where the upper limit of residual error is 1 and the lower limit is 1. The upper limit of the standard deviation is 0.604. Finally, we used the data set under abnormal conditions for experimental verification. The results show that the method can accurately determine the fault early warning of the marine diesel engine and provides a new reference for the health management of intelligent marine equipment.

3 citations

Journal ArticleDOI
TL;DR: In this article , the results of the application of a ranking method for identifying factors that influence vibration in marine diesel engines are presented to determine the most significant ones, with the clearance between the piston and the cylinder liner being the most important.
Abstract: The article discusses the method and results of processing statistical data from an experimental study of vibrations in marine diesel engines caused by the operation of cylinder-piston groups. The results of the application of a ranking method for identifying factors that influence vibration in marine diesel engines are presented to determine the most significant ones. A series of experiments were conducted according to special plans to actively implement the random balance method. This helped to establish the correctness of selecting the most significant factors from a variety of factors that influence the process under study. The article presents a mathematical model that enables the calculation of current values and prediction of changes in the most significant indicators, with the clearance between the piston and the cylinder liner being the most important.

3 citations

Journal ArticleDOI
TL;DR: In this paper , the authors used field expeditions data to map the water bodies of the Kerch Peninsula and used the Keras framework to build the architecture of a deep neural network, which achieved a model prediction accuracy of 96% when solving the problem of extracting the area of the water surface using remote sensing data.
Abstract: Water bodies on the Earth’s surface are an important part of the hydrological cycle. The water resources of the Kerch Peninsula at this moment can be described as a network with temporary streams and small rivers that dry up in summer. Partially, they are often used in fisheries. But since permanent field monitoring is quite financially and resource-intensive, it becomes necessary to find a way for the automated remote monitoring of water bodies using remote sensing data. In this work, we used remote sensing data obtained using the Sentinel-2 satellite in the period from 2017 to 2022 during the days of field expeditions to map the water bodies of the Kerch Peninsula. As a training data set for surface water prediction, field expeditions data were used. The area for test data collection is located near Lake Tobechikskoye, where there are five water bodies. The Keras framework, written in Python, was used to build the architecture of a deep neural network. The architecture of the neural network consisted of one flattened and four dense layers fully connected. As a result, it achieved a model prediction accuracy of 96% when solving the problem of extracting the area of the water surface using remote sensing data. The obtained model showed quite good results in the task of identifying water bodies using remote sensing data, which will make it possible to fully use this technology in the future both in hydrological studies and in the design and forecasting of fisheries.

1 citations

Journal ArticleDOI
TL;DR: In this article , a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data was developed for the Azov Sea estuaries.
Abstract: The Azov Sea estuaries play an important role in the reproduction of semi-anadromous fish species. Spawning efficiency is closely connected with overgrowing of those species spawning grounds; thus, the objective of the water vegetation research has vital fisheries importance. Thus, the main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS. For the classification of Azov Sea estuaries, a random forest algorithm was used. The obtained results showed that in 2020 the areas occupied by reeds reached 0.37 km2, while in 2021, they increased to 0.51 km2. There was a high level of Phragmites australis growth in the Soleniy and Chumyanniy firths. The rapid growth of Phragmites australis in the period of 2020–2021, where the area covered by the reed doubled, is primarily attributed to eutrophication. This is due to the nutrient enrichment from agricultural lands located in the northern part of the research area near Novonekrasovskiy village. Additionally, changes in water flows and hydrological conditions can also contribute to the favorable growth of the reed. This can result in a high growth rate of Phragmites australis, which can reach up to 2 m per year and can propagate both through vegetative and sexual means, leading to the formation of large and dense clusters.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the authors used neural networks for the analysis of the lifetime of the teeming ladle, which is similar to our work in this paper. But their use is suitable for processing complex problems where the dependencies between individual quantities are not exactly known.
Abstract: When describing the behaviour and modelling of real systems, which are characterized by considerable complexity, great difficulty, and often the impossibility of their formal mathematical description, and whose operational monitoring and measurement are difficult, conventional analytical–statistical models run into the limits of their use. The application of these models leads to necessary simplifications, which cause insufficient adequacy of the resulting mathematical description. In such cases, it is appropriate for modelling to use the methods brought by a new scientific discipline—artificial intelligence. Artificial intelligence provides very promising tools for describing and controlling complex systems. The method of neural networks was chosen for the analysis of the lifetime of the teeming ladle. Artificial neural networks are mathematical models that approximate non-linear functions of an arbitrary waveform. The advantage of neural networks is their ability to generalize the dependencies between individual quantities by learning the presented patterns. This property of a neural network is referred to as generalization. Their use is suitable for processing complex problems where the dependencies between individual quantities are not exactly known.