Designing Recurrent Neural Networks for Monitoring Embedded Devices

Bahnsen, Fin Hendrik; Kaiser, Jan; Fey, Görschwin

doi:10.1109/ets50041.2021.9465460

Cited by 2 publications

(1 citation statement)

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“…This research work determines the use of ANN-based approach to detect the faults on raw data streaming that comes from the monitoring systems in order to minimize the need of engineer curated data streams for adapting in different devices. Reference [ 6 ] developed a lossless compression mechanism for home appliances based on hardware data compression. The serial interface with hardware acceleration was designed to sample infrared (IR) and very high-frequency signals.…”

Section: Related Workmentioning

confidence: 99%

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Ramalingam

Mehbodniya

Webber

et al. 2022

Computational Intelligence and Neuroscience

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Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

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Section: Related Workmentioning

confidence: 99%