Analysis of Wireless Network Technology for High Reliability Aircraft Networks

Ahn, Seung-Pyo; Kim, Da‐Hye; Lee, Jae-Min; Kim, Dong-Seong

doi:10.7840/kics.2016.41.12.1933

Cited by 1 publication

(2 citation statements)

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“…However, it suffers from several problems, including difficulties in checking system operation and remote-monitoring-based control, cost-intensive CCTV operation for visual monitoring, and the high complexity of criteria for assessing abnormalities in sensing values. Besides such technical and financial problems, the application of advanced technologies to real-world local farms suffers from additional basic limitations [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Faults detected based on sensing values have been assessed corresponding to individual and multiple data within a given space. Particularly, a moving-average-based assessment technique was used to study time series, and anomalous data were assessed based on a geospatial understanding of the sensing values [3,4]. One study pointed out the problem of poor generalizability of the methods used for sensor outlier assessment based on the ambiguity of data forms and models [8].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe,

Lee

2023

Agriculture

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Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

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Section: Introductionmentioning

confidence: 99%