Artificial Neural Networks in Classifying Telemetry Data for Compression Systems

Bogachev, I. V.; Levenets, A. V.; Chye, En Una

doi:10.15217/issn1684-8853.2016.3.2

Cited by 3 publications

(1 citation statement)

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“…Сегодня вопросам сжатия данных посвящено огромное число работ и информационных ресурсов, например, [2][3][4], большинство из которых посвящены сжатию мультимедийной информации. В то же время стали появляться работы, посвящённые сжатию информации с сенсорных устройств в информационноизмерительных системах [5][6][7], а также применению для обработки специальных данных нетрадиционных алгоритмов, не применяемых для сжатия изображений и звука [8][9][10].…”

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Research and development of data compression methods for technical monitoring systems

Yakunin

2023

Vestn. Dagest. gos. teh. univ., Teh. nauki

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Objective. The aim of the study is to develop methods for data compression in relation to monitoring systems.Method. The studies were carried out mainly experimentally, by processing data generated by the information-measuring system of the Altai State Technical University in the process of meteorological observations, temperature control and control of the consumption of such energy resources as hot and cold water, heat supply. However, when it was proposed to change their structure and presentation form to compact the stored data, the degree of compression was found theoretically.Result. The results of studies conducted with the participation of the author of the publication in the field of data processing, transmission and storage are presented. Methods for streaming data compression “on the fly” as it arrives, both lossy and lossless, as well as methods based on optimizing the structure of databases designed to store information collected during monitoring, are considered. The main attention is paid to the consideration of data compression created in the process of temperature monitoring, since it is the control and monitoring of temperature processes that is most common in various technical process control systems, heat metering systems both on the consumer side (housing and communal services) and on the side of the heat supply organization.Conclusion. In the vast majority of cases, lossless compression allows you to compress the information received from the monitoring system by more than 10 times, and lossy compression is much stronger, and without losing the pragmatic value of the stored information.

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