Vibration Prediction of Flying IoT Based on LSTM and GRU

Hong, Jun-Ki

doi:10.3390/electronics11071052

Cited by 7 publications

(3 citation statements)

References 36 publications

(33 reference statements)

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“…The aspects mentioned above have a significant impact on the operational safety of the drone. The increase in safety may also result from changes in the design [ 205 , 206 ], appropriate risk analysis tailored to the purpose of the drone [ 163 , 188 , 189 , 202 , 207 , 208 ], and the detection of unexpected behaviours and abnormal situations during the operation of the drone [ 209 , 210 , 211 , 212 , 213 ]. Just as important as the drone’s flight is its landing.…”

Section: Resultsmentioning

confidence: 99%

Risks of Drone Use in Light of Literature Studies

Tubis,

Poturaj,

Dereń

et al. 2024

Sensors

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This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia.

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Section: Resultsmentioning

confidence: 99%

Risks of Drone Use in Light of Literature Studies

Tubis,

Poturaj,

Dereń

et al. 2024

Sensors

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“…Several researchers have adopted neural networks because of their effectiveness in learning this complex internal relationship well. Gated recurrent unit (GRU) network, as a variant of the classical recurrent neural network LSTM, has a simpler structure with fewer parameters and faster convergence, which can speed up the iterative process of the model ( 13 ). At present, several scholars use LSTM or other neural network models to study the driving intention and trajectory of HDVs and determine the optimal combination of hyperparameters through experience or experiments ( 14 – 16 ).…”

Section: Problem Descriptionmentioning

confidence: 99%

Bayesian Global Optimization Gated Recurrent Unit Model for Human-Driven Vehicle Lane-Change Trajectory Prediction Considering Hyperparameter Optimization

Chen

Zhao

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To address the low accuracy and inefficiency of current lane-change trajectory prediction methods for human-driven vehicles, this study develops a neural network lane-change trajectory prediction model with hyperparametric optimization capability using Bayesian optimization and gated recurrent units to consider the effect of lane-change intention on vehicle lane-change behavior and to predict it. The proposed model was instantiated using trajectory data of 8,721 vehicles. The results show that the overall recognition accuracy of intention recognition under the optimal input is 93.54%, and the recognition accuracy of keeping straight, left lane-change and right lane-change is 95.59%, 91.72%, and 93.30%, respectively. The root mean square errors of the predicted and actual trajectories to the left and to the right under the optimal input are 0.2582 and 0.2957, respectively. This paper demonstrates that, for the intention recognition module, the low-dimensional input enables the model to obtain high prediction accuracy, while for the trajectory prediction module, the high-latitude input enables the model to obtain a low prediction error. The developed trajectory prediction model can be used to assist in driving decision-making, path planning, and so forth.

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“…This model combines sequence prediction with GRU and GRU-Seq2Seq to address the gradient disappearance problem, outperforming back-propagation (BP) and LSTM-Seq2Seq models. Hong [35] applied LSTM and GRU models to forecast vibrations based on time series motor data, comparing their accuracy and simulation runtime efficiency. This research indicates that GRU forecasts vibrations faster than LSTM.…”

Section: Related Workmentioning

confidence: 99%

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Lee,

Hong

2023

Electronics

Self Cite

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We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.

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Vibration Prediction of Flying IoT Based on LSTM and GRU

Cited by 7 publications

References 36 publications

Risks of Drone Use in Light of Literature Studies

Risks of Drone Use in Light of Literature Studies

Bayesian Global Optimization Gated Recurrent Unit Model for Human-Driven Vehicle Lane-Change Trajectory Prediction Considering Hyperparameter Optimization

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

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