Towards Smart Transportation System

Li, Guofu; Cao, Ning; Zhu, Pengjia; Zhang, Yanwu; Zhang, Ying-Ying; Li, Lei; Li, Qingyuan; Yu, Zhang

doi:10.4018/joeuc.20210501.oa3

Cited by 22 publications

(8 citation statements)

References 38 publications

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“…Traditional machine learning enables computers to learn from experience, that is, to continuously improve machine learning algorithms by training data [15]. However, the algorithm relies on human to set specific features.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Logo Design System of Network Media Art Based on Convolutional Neural Network

Yang

2022

Mobile Information Systems

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Nowadays, we are in an era of rapid development of Internet technology and unlimited expansion of information dissemination. While the application of new media and digital multimedia has become more popular, it has also brought Earth shaking changes to our life. In order to solve the problem that the traditional static visual image has been difficult to meet people’s needs, a network media art dynamic logo design system based on convolutional neural network is proposed. Firstly, the software and hardware platform related to accelerator development is introduced, the advanced integrated design and calculation IP core are determined as the FPGA hardware accelerator, and the design objectives and requirements of the accelerator system are analyzed. The overall architecture of the accelerator system is designed. 76% of designers believe that the dynamic logo has promoted the corporate image. Then, the function and architecture of IP core are designed based on advanced synthesis, the code structure is standardized, the function is divided, and the operation acceleration is further optimized by using the instruction set of HLS. Finally, the design is integrated by Vivado HLS and Vivado IDE software. The experiment shows that the accelerator system has low power consumption and high resource utilization.

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Section: Methodsmentioning

confidence: 99%

Dynamic Logo Design System of Network Media Art Based on Convolutional Neural Network

Yang

2022

Mobile Information Systems

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“…In general, most authors propose a solution based on deep-RL approaches (e.g., [154,155,220]), whereby especially older publications are limited to regression analyses for demand prediction combined with optimization approaches (e.g., [137,207]). In dependence of the actual sharing type as well as sharing mode, the formulation of the relocation optimization models differ, so that for dockless SMS, it is the state-of-the-art approach in literature and practice to use grid-based environments to obtain a spatial discretization of the operating area into zones (see, e.g., [144,154,155,213,214,215,220]) -the counterpart of stations in docked SMS (see, e.g., [174,218,219]). Regarding time, the considered time frame is discretized into periods for both dockless and docked SMS.…”

Section: ) Repositioningmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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“…Overview of Reinforcement Learning. Reinforcement learning is an important machine learning method that has been at the forefront of intelligent control and artificial intelligence research in recent years [12]. Among various learning methods, reinforcement learning has the ability to adapt to complex systems and self-training.…”

Section: Overview Of Reinforcement Learning and Uav Communicationmentioning

confidence: 99%

Optimization Design of Multi-UAV Communication Network Based on Reinforcement Learning

Cao

2022

Wireless Communications and Mobile Computing

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In recent years, due to the application of high-definition video codec technology, high-precision satellite navigation technology, mobile base station positioning technology, and broadband technology, the performance of UAVs has been greatly improved. In the military field, drones have become an important weapon alongside missiles on the battlefield. In the future, military drones will perform strategic missions such as battlefield reconnaissance and long-range destruction. Outside the military field, DJI’s Zenmuse series drones are used for filming, MG series drones are used for pesticide spraying, and Beijing Zhonghangzhi unmanned helicopters are used for geological surveys, precise inspection of power lines, and maritime law enforcement. With the continuous improvement of technical specifications, UAV communication technology requires further research and development. This paper has conducted research experiments on the optimization of multi-UAV communication network based on reinforcement learning. The experimental data show that it is marked as the AoI value corresponding to the completion of a certain self-task. It can be seen that the final AoI of the communication trajectory of reinforcement learning is 115, and the AoI greedy strategy finally obtains AoI of 140 seconds, achieving about 18% of the total AoI reduce, which effectively improve the performance of the system. From the above data, the research of reinforcement learning method has great benefits for the development of UAV communication.

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Towards Smart Transportation System

Cited by 22 publications

References 38 publications

Dynamic Logo Design System of Network Media Art Based on Convolutional Neural Network

Dynamic Logo Design System of Network Media Art Based on Convolutional Neural Network

A Systematic Literature Review on Machine Learning in Shared Mobility

Optimization Design of Multi-UAV Communication Network Based on Reinforcement Learning

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