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Anomaly Detection of Rolling Element Bearing (REB) using LSTM
Mareska Pratiwi Maharani,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
The long short-term memory (LSTM) technique is proposed in this research as a deep learning-based bearing defect detection algorithm. Event detection systems in industrial sensors are one example of a wide range of applications for autonomous anomaly detection in data mining. The most serious issue with such a scenario is that the cause of the abnormality is unknown. As a result, typical machine learning approaches are ineffective in solving this type of problem. To differentiate vibration abnormalities from sensor values, this article employs an LSTM model. This simulation seeks to identify whether or not the bearings need to be changed as soon as possible.
Real-time Wireless Sensor Fault Detection Scheme in Industrial Internet of Things
Mareska Pratiwi Maharani,Jae Min Lee,Dong-Seong Kim 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Fog computing and IoT have recently been used in a variety of fields, including transportation, education, healthcare, and the industrial industry. Through fog computing and the implementation of IoT devices, it can reduce the complexity in various scenarios including anomaly detection framework. One example of an application for autonomous anomaly detection in data mining is sensors fault detection systems in industrial internet of things (IIoT). Therefore, it requires a fog-computing-based simulator and deep learning algorithms applied to solve this challenge. This paper performed deep learning approaches and simulated on top of iFogSim as simulator for the network’s reliability.
Malicious Detection for Edge Computing-based in Industrial Internet of Things
Mareska Pratiwi Maharani,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
The networks and technologies have been greatly developed over time in widely referred to as Industry 4.0 or Industrial Internet of Things (IIoT). Thus, the collected and sensed IIoT data must therefore be uninterrupted continuously, particularly for large factories, in order to effectively incorporate the Internet of Things technology into the industry. In this paper, malicious detection for edge computing-based by comparing between two machine learning algorithms, K-Nearest Neighbors (K-NN) and Random Forest, is proposed.
Fast Sensor Data Recovery using Multi-Directional LSTM for Industrial Wireless Sensor
Adinda Riztia Putri,Mareska Pratiwi Maharani,Ade Pitra Hermawan,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
Missing sensor values are common in Wireless Sensor Network due to its unpredictable and fragile nature. While missing sensor values may lead to several serious problems in the industrial environment where the sensors are deployed, it is inevitable at some points. Fast sensor data recovery using multi-directional LSTM is introduced to address this problem where it can handle missing sensor values and replace them with the predicted values from the previous sensor data. Our proposed method shows a promising result of low prediction loss while also achieve fast computation time of RMSE by 0.11, MAE by 0.07, MAPE by 0.01, and computing time by 0.341 ms.
Real-time Data Recovery using Multi-directional LSTM in Wireless Sensor Networks
Ade Pitra Hermawan,Mareska Pratiwi Maharani,Dong-Seong Kim(김동성),Jae-Min Lee(이재민) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
In this paper, an algorithm to recover missing data from the sensor in real-time is proposed. Since missing sensor data is a crucial issue in Industrial Internet of Things (IIoT), we employ two different long short-term memory (LSTM) algorithms to handle this issue. The unidirectional LSTM constantly estimates the upcoming data by learning from the previous information, while bidirectional LSTM utilize both past and future information to estimate the missing data. When the system does not receive the data from the sensor devices, the algorithms fill in the missing data based on the predicted value automatically. According to the simulation results, the proposed scheme significantly surpasses the previous works in terms of loss value.
LSTM-Based Human Fall Detection using Thermal Array Sensor
Adinda Riztia Putri,Goodness Oluchi Anyanwu,Mareska Pratiwi Maharani,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Accidental fall may lead to numerous serious and deadly injures. Existing fall detection systems mostly use cameras and are considered a privacy-intrusive approach. Thermal array sensors are considered a privacy-friendly device that does not raises discomforts for users. In this study, we simulate a fall detection system using a thermal array sensor with three different algorithms: CNN, LSTM, and CNN-LSTM. Our result shows that LSTM has the best accuracy among other algorithms by 99.96%.