The method of self-testing of thermocouples in situ

Shu, Chang; Кочан, Орест; Kochan, Volodymyr; Vasylkiv, Nadiia

doi:10.1109/idaacs.2015.7341408

Cited by 1 publication

(1 citation statement)

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“…The IIoT is a collection of many smart devices that process data from the environment and are located in different areas of production. The IIoT assumes that the use of data collection and analysis algorithms makes the industrial production system more automatically controlled, i.e., it can make decisions on troubleshooting, dynamic allocation of resources, emergency calls to staff, need for calibration [33], etc. This approach allows speeding up the data processing and increasing the productivity of industrial production.…”

Section: Features Of Iiot and Smart Grid Systemsmentioning

confidence: 99%

Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

et al. 2023

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The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.

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Section: Features Of Iiot and Smart Grid Systemsmentioning

confidence: 99%