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Kamil Adipovich Masalimov

dblp:282/6010 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Deep learning architectures and training · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › recurrent neural network
LSTM		 0.412020
A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines · ASE 2020
Machine learning › Deep learning architectures and training
recurrent neural network		 0.412020
A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines · ASE 2020
Embedded and real-time systems
predictive maintenance		 0.412020
A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines · ASE 2020

Methods — techniques the papers use, named apart from their topics

LSTM neural network · 0.9
YearPublicationVenuePosition
2020 Diagnostic of the State of the Cutting Tool of Metal-Cutting Machines using Bidirectional Recurrent Neural Networks with a Long Short-Term Memory
abstract
The paper presents a method for diagnosing the condition of a cutting tool of metal-cutting machines, which allows determining the amount of tool wear according to information received from force and vibration sensors. To fix the dependence of the amount of wear on the values of vibration and cutting forces, it is proposed to use a diagnostic model based on bidirectional recurrent neural networks with a long short-term memory. The article presents and describes the architecture of such neural networks. We have developed several diagnostic models based on various neural network architectures and conducted a comparative analysis of the accuracy of the obtained models.
Kamil Adipovich Masalimov, Rustem Anvarovich Munasypov
 ICARCV1
2020 A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines
abstract
This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).
Kamil Adipovich Masalimov
 ASE1