Recurrent Neural Network-based Path Planning for an Excavator Arm under Varying Environment

Vu, Nga Thi-Thuy; Tran, Ngoc Nguyen; Nguyen, Nam Hoai

doi:10.48084/etasr.4125

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…These gates alleviate the problem of disappearing gradients that can occur in LSTMs [26]. This is a similar approach to [27] on the detection and recognition of danger signs, to [28] on prediction, and to [29] on the planning of routes using RNNs. The LSTM is guided and uses the historical context maintained by the forget gate.…”

Section: A the Bidirectional Long-short-term Memory (Bltsm) Model Of ...mentioning

confidence: 99%

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

Aldhahri

2023

Eng. Technol. Appl. Sci. Res.

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The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance.

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Section: A the Bidirectional Long-short-term Memory (Bltsm) Model Of ...mentioning

confidence: 99%

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

Aldhahri

2023

Eng. Technol. Appl. Sci. Res.

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“…The amount of information taken from history that will be stored is determined by the Input Gate. The amount of information that can be forgotten from previous moments is determined by the Forget Gate, while the Output Gates are responsible for controlling the internal memory unit, which is responsible for producing the required quantity of information for the subsequent cycle [15].…”

Section: B Lstmmentioning

confidence: 99%

Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture

Ahmed,

Haji,

Ahmed

et al. 2023

Eng. Technol. Appl. Sci. Res.

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The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.

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“…The above algorithm solves the truck transportation scheduling problem in open-pit mines to some extent, but it has the problems of low convergence accuracy and slow convergence speed. Gap theory is a theory put forward in highway unmanned driving to coordinate and efficiently pass through intersections [16]. Local vehicle running state, rational use of vehicle clearance [17], to achieve cross traffic between different traffic flows, rather than the inefficient way of stopping and giving way to cross traffic.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

Zhang

Lin

et al. 2023

IEEE Access

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Aiming at the transportation scheduling problem of unmanned trucks in open-pit mines, an optimization model for transportation scheduling of unmanned trucks in open-pit mines is established, taking the minimum sum of fuel cost, fixed start-up cost, fault maintenance cost and network base station construction and maintenance cost as the objective function, and taking mining amount, crushing amount in crushing field, truck number and truck transportation work as the bundle reduction conditions. By adjusting the position of the encapsulated individual and the optimal encapsulated individual through the parameter position adaptive updating mechanism, the search range of the solution space can be enlarged, so that the algorithm can jump out of the local optimum. This paper analyzes the influence of vehicle clearance on coordinated traffic at intersections under clearance theory, and puts forward the minimum vehicle clearance to meet coordinated traffic. Generate trajectories with behavioral semantics through hierarchical online selection of primitives; Based on the model predictive control algorithm, the motion control module fully considers the execution ability, tracking deviation and control stability of the platform drive motor, and updates the weight coefficient of the control objective function in real time with the help of the semantic attributes of the behavioral motion primitives given by the motion planning, so as to reduce the lateral error, heading error and speed error of the trajectory tracking. Finally, a real vehicle is verified by a medium-sized hybrid unmanned crawler platform, which proves that the proposed intersection coordinated traffic model is feasible in theory, reflecting that the traffic density is a factor that affects the transportation efficiency. Under the condition of meeting the production requirements, reduce the intersection conflict and improve the traffic capacity of the intersection by adopting large-load unmanned mine trucks as much as possible, reducing the number of unmanned mine trucks and the interference between equipment.

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Recurrent Neural Network-based Path Planning for an Excavator Arm under Varying Environment

Cited by 9 publications

References 25 publications

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture

Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

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