Intelligent train control for cooperative train formation: A deep reinforcement learning approach

Zhang, Danyang; Zhao, Junhui; Zhang, Yang; Zhang, Qingmiao

doi:10.1177/09596518211064799

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…When the quality coefficient is about 0.85, the minimum values of a1-a6 are 0.3, 0.3, 0.2, 0.2, 0.3, and 0.3, respectively. When the quality coefficient is about 0.47, the minimum values of a1-a6 are 0.1, 0.1, 0.1, 0.1, 0.1, and 0.1, respectively [21]. At the same time, the degree of satisfaction of the six quality characteristics of a1-a6 is as follows.…”

Section: Optimization Control Model For Construction Engineering Mana...mentioning

confidence: 94%

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Zhou,

Guo

2024

SCPE

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Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.

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Section: Optimization Control Model For Construction Engineering Mana...mentioning

confidence: 94%

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Zhou,

Guo

2024

SCPE

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“…After testing, this algorithm smoothes the acceleration curve. It can effectively complete train control tasks in multi train tracking scenarios [12]. Shihab S A M and Wei P proposed a strategy formulation method based on DRL for developing optimal seat inventory control strategies.…”

Section: Related Workmentioning

confidence: 99%

Construction of an Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithms

Chen

2023

IJACSA

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In recent years, intelligent robots have been widely used in fields such as express transportation, industrial automation, and healthcare, bringing great convenience to people's lives. As one of the core technologies of intelligent robots, path planning technology has become a research highlight in the field of robotics. To achieve path planning in unknown environments, a path planning algorithm based on an improved dual depth Q-network is proposed. In both simple and complex grid environments, the planned path inflection points for the improved dual depth Q-network is 4 and 9, respectively, with path lengths of 27.21m and 28.63m, respectively. Both are less than double depth Q network and adaptive Ant colony optimization algorithms. The average reward values of the improved dual depth Q network in simple and complex environments are 1.12 and 1.02, respectively, which are higher than those of the dual depth Q network. In a random environment, the lowest probability of the improved dual depth Q network successfully reaching the destination without colliding with obstacles is 95.1%, which is higher than the other two algorithms. In the Gazebo environment, when the number of iterations reaches 2000, the average cumulative reward value is positive. The average cumulative reward value in the range of iterations from 3500 to 4000 and iterations from 4000 to 4500 exceeds 500. The average cumulative reward value of the dual depth Q network is only positive within the two intervals of iterations 2500-3000 and 3000-3500. The average cumulative reward value does not exceed 100. According to the findings, the path planning ability of the improved dual depth Q network is better than that of the dual depth Q network and the adaptive Ant colony optimization algorithms.

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Research on Intelligent Inspection Method of Multi-robot Joint Inspection of Power Equipment under New Energy Access

Qian,

Zhang,

Deng

et al. 2023

2023 13th International Conference on Power and Energy Systems (ICPES)

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Intelligent train control for cooperative train formation: A deep reinforcement learning approach

Cited by 4 publications

References 40 publications

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Construction of an Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithms

Research on Intelligent Inspection Method of Multi-robot Joint Inspection of Power Equipment under New Energy Access

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