An eco-driving algorithm for trains through distributing energy: A Q-Learning approach

Zhu, Qingyang; Su, Shuai; Tang, Tao; Li, Wentao; Zhang, Zixuan; Tian, Qinghao

doi:10.1016/j.isatra.2021.04.036

Cited by 70 publications

(16 citation statements)

References 32 publications

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“…For the convenience of discussion, we assume the mass of each train is 1. The longitudinal dynamics model of n following trains can be written as [9], [10]:…”

Section: Vcts Leader-following Modelmentioning

confidence: 99%

Adaptive Cruise Control of Virtual Coupled Trains Based on Sliding Mode

Wang

Cao

2022

J. Phys.: Conf. Ser.

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In this paper, the adaptive cruise control method for the virtually coupled train set (VCTS) in rail transit is proposed. First of all, a leader-following VCTS dynamics model is constructed by analyzing the dynamic evolution of VCTS in a real-world environment, taking into account uncertain parameters, unknown disturbances and controller input saturation. Next, to cope with the position and speed constraints, the position artificial potential field function and the speed barrier function are embedded into the designed sliding manifold. And a novel adaptive cruise control protocol is designed, which can simultaneously deal with uncertain resistance parameters and unknown disturbances while tracking the desired cruise speed and maintaining the desired distance. Based on Lyapunov stability theory, the proposed control protocol guarantees the ultimate boundedness of all subsystems. Finally, the simulation results verify the effectiveness of the theoretical analysis.

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“…For the convenience of discussion, we assume the mass of each train is 1. The longitudinal dynamics model of n following trains can be written as [9], [10]:…”

Section: Vcts Leader-following Modelmentioning

confidence: 99%

Adaptive Cruise Control of Virtual Coupled Trains Based on Sliding Mode

Wang

Cao

2022

J. Phys.: Conf. Ser.

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“…where X ijk represents k tasks of outgoing/returning/recycling assigned to the i th AGV under the j th order. e scheduling algorithm in the unmanned warehouse scenario was designed for the case where potential collisions caused by the AGV handling during task implementation were ignored and aimed to determine the shortest path from the starting point to the target point utilizing the exhaustive method and the Q-learning method [16]. is paper considered two AGV scenarios, i.e., single-and multi-target points.…”

Section: Modeling and Solution Findingmentioning

confidence: 99%

Research on Optimization of the AGV Shortest-Path Model and Obstacle Avoidance Planning in Dynamic Environments

Liu¹

2022

Mathematical Problems in Engineering

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This paper proposes a support vector machine (SVM)-based AGV scheduling strategy that enhances the scheduling efficiency of automated guided vehicles (AGVs) in intelligent factories. The developed scheme optimizes the task area division process to endow the AGVs with the ability to avoid obstacles in complex dynamic environments. Specifically, given the two AGV motion cases, i.e., towards a single target point and multiple target points, the optimal path was determined utilizing the exhaustive and the Q-learning methods, while path optimization was realized by utilizing different schemes. Based on the shortest path obtained, a nonlinear programming model with the shortest time as the objective was built, and the AGV’s turning path was proved to be optimal by the non-dominated sorting genetic algorithm (NSGA-II). Several simulation tests and calculation results validated the proposed method’s effectiveness, highlighting that the developed scheme is a rational solution to the obstacle congestion and deadlock problems. Moreover, the experimental results demonstrated the proposed method’s superiority in path planning accuracy and its ability to respond well in complex dynamic environments. Overall, this research provides a reference for developing and applying AGV cluster scheduling in real operational scenarios.

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“…The prediction speed is the time needs to predict cancer and the resource usage cost is simply the complexity of the ML model. The time and space complexity, time requires to execute an operation and the space requires to store data, are very important to determine the efficiency of any algorithm (Zhu et al, 2022) (Abdelsamea et al, 2019). In the following section, we have mentioned the prediction speed and complexity analysis.…”

Section: Speed and Complexity Analysismentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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An eco-driving algorithm for trains through distributing energy: A Q-Learning approach

Cited by 70 publications

References 32 publications

Adaptive Cruise Control of Virtual Coupled Trains Based on Sliding Mode

Adaptive Cruise Control of Virtual Coupled Trains Based on Sliding Mode

Research on Optimization of the AGV Shortest-Path Model and Obstacle Avoidance Planning in Dynamic Environments

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

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