Electronic educational complex for training specialists in the field of technical systems management

Verkhova, Galina V.; Akimov, S. V.

doi:10.1109/ctsys.2017.8109479

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…The proposed model is universal and can be used both in marker and nonmarked Augmented Reality technology, implemented with the help of smartphones and tablets, virtual reality helmets and enlarged reality glasses. In addition, it can be generalized to a system of mixed reality, which provides a deeper degree of integration of virtual reality into physical reality [13][14][15].…”

Section: The Architecture Of the Generalized Model Of Additional Realitymentioning

confidence: 99%

Generalized Model of the Additional Reality System

Verkhova¹,

Akimov²,

Kotelnikov³

2019

JPIT

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The article presents a generalized model of augmented reality system. It is shown how this model can be used to create modular systems that support the technology of augmented reality. Unification methods in the field of augmented and mixed reality are proposed. The role of complemented and mixed reality technology in the framework of the unified cyber environment of the information society is demonstrated.

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Section: The Architecture Of the Generalized Model Of Additional Realitymentioning

confidence: 99%

Generalized Model of the Additional Reality System

Verkhova¹,

Akimov²,

Kotelnikov³

2019

JPIT

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“…The work is based on: o Inspection of acting systems -Ukraine, Russia [2,[12][13][14][15][16][17], and others;…”

Section: A Justification Of the Research Conceptmentioning

confidence: 99%

“…Computer training has evolved from "episodic use of technical tools for training and knowledge control", as well as from systems such as "Dean's office" or "Department" (50s -90s of the 20th century) to the development of unified information environments of universities, for example [2,3]. Moreover, both stationary computers and mobile devices of the educational process participants are accessing the universities information environments and controlling the relevant processes [4,5].…”

Section: Introductionmentioning

confidence: 99%

University information environment: the case study

Lavrov

Paderno

Volosiuk

et al. 2019

2019: Proceedings of 2019 XVIII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the

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The paper considers the issues of digitalized control of the university. The causes for the low efficiency of university digitalization development are revealed. Among the main issues are patchwork digitalization and ignoring the principles of modern quality management systems. The concept of designing an effective university information environment based on the philosophy of universal quality management, the methodology of integrated information systems, the principles of an active operator, adaptability and the human-system approach are described. The paper presents the university management information system tested at 20 universities in Russia and Ukraine. The system is useful not only to the university, but also to enterprises, since (1) it provides a higher quality of training, allowing creating "training paths" with production specifics taken into account, and (2) guarantees access to information about a future specialist.

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“…Talent training quality (TTQ), which determines the employment rate, has always been a social concern. Therefore, most colleges have recently shifted their focus from improving the employment rate of talents to the methods of talent training [1][2][3][4]. Traditionally, the TTQ is evaluated against survey data on the expertise and career development of previous graduates.…”

Section: Introductionmentioning

confidence: 99%

Construction and Optimization of Talent Training Quality Based on Data Mining

Xu¹

2020

ISI

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Thanks to the rapid development of information technology, it is now possible to mine out and fully utilize the valuable information from the big data on talent training quality (TTQ), using artificial intelligence and big data analysis. This paper develops a TTQ prediction model based on random forest (RF)-artificial neural network (ANN), aiming to improve and optimize the TTQ evaluation index system (EIS) in the big data environment, and realize efficient and accurate prediction of TTQ. Firstly, a scientific TTQ EIS was built up, including four primary indies (background evaluation, process evaluation, input evaluation, and result evaluation), and a number of secondary and tertiary indices. Through association matching, a data reconstruction model was constructed for the key evaluation indices of TTQ, the features of the data structure were analyzed, and the key features of each index were extracted and optimized intelligently. Finally, the backpropagation neural network (BPNN) was integrated with the RF algorithm into our prediction model. Experimental results demonstrate the accuracy and effectiveness of the proposed TTQ prediction model. The research findings provide a reference for applying the RF-ANN prediction model in other fields.

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Electronic educational complex for training specialists in the field of technical systems management

Cited by 11 publications

References 4 publications

Generalized Model of the Additional Reality System

Generalized Model of the Additional Reality System

University information environment: the case study

Construction and Optimization of Talent Training Quality Based on Data Mining

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