Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines

Saraubon, Kobkiat; Wiriyanuruknakon, Nuttapong; Tangthirasunun, Natdanai

doi:10.3991/ijim.v15i11.20753

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…This helped us to accurately identify the eight activities where some of them are difficult to distinguish between for the similarities among them. Different traditional and Deep learning algorithms have been applied to our dataset as found useful in several machine learning related works [39][40][41][42]. Among them, Bi-directional LSTM gives optimal accuracy.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

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Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

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“…There has been interestingly few studies on the use of machine learning and deep learning models with LoRa RSS datasets in the literature [9][10]. There are four separate data sets based on different RF factors and bandwidths.…”

Section: Training Datamentioning

confidence: 99%

ANN-based LoRaWAN Channel Propagation Model

Habaebi

Rofi

Islam

et al. 2022

Int. J. Interact. Mob. Technol.

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LoRaWAN wireless communication channels are often impacted by noise and interference over long-range causing loss of a received signal. One of the main drawbacks of using existing propagation models is less accurate as these models in designing the communication link are tailored to simplify the estimation. In this paper, an artificial intelligent real time path loss model is proposed. It is capable of processing complex variables over a short period of time. Providing it with enough data, the model is able to learn channel behavior and predict the path loss accurately. Results of the model are benchmarked against classical statistical curve fitting models where RMSE values are also compared and indicating that the artificial intelligent model has better accurate prediction.

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“…It showed that LSTM-MVR Model had improved the operation of forecasting air pollution when compared to LSTM. Furthermore, another study forecasted air pollution based on machine learning techniques [12], it suggested two models: MLP and LSTM models. It confirmed that the LSTM model worked better than the MLP model.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Air Pollution Forecasting Using Deep Learning

et al. 2021

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Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human’s health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared.

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Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines

Cited by 5 publications

References 14 publications

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

ANN-based LoRaWAN Channel Propagation Model

Air Pollution Forecasting Using Deep Learning

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