Safety factor profile control with reduced central solenoid flux consumption during plasma current ramp-up phase using a reinforcement learning technique

Minor and major disruptions, preceded by the growth of m = 2 and m = 3 modes, are observed during normal discharges on the STOR-1M tokamak, when the plasma safety factor on the plasma surface, q(a), becomes lower than 3 or with strong gas-puffing. It has been demonstrated that stable low-q discharges, with q(a) as low as 1.6 can be obtained and maintained for durations longer than the growth time for resistive tearing modes, if the plasma current is ramped up rapidly through a disruptive q region defined by 1.85 < q(a) < 2.1. The boundaries of the disruptive q region do not change when the maximum current ramp-up rate is varied between 0.5–2 kA μs−1.

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Reduction of poloidal magnetic flux consumption during plasma current ramp-up in DEMO relevant plasma regimes

Nuclear Fusion ◽

10.1088/0029-5515/57/1/016015 ◽

2016 ◽

Vol 57 (1) ◽

pp. 016015 ◽

Cited By ~ 2

Author(s):

T. Wakatsuki ◽

T. Suzuki ◽

N. Hayashi ◽

J. Shiraishi ◽

Y. Sakamoto ◽

...

Keyword(s):

Magnetic Flux ◽

Plasma Current ◽

Current Ramp ◽

Flux Consumption

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Computation offloading through mobile vehicles in IoT-edge-cloud network

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01848-5 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Jun Long ◽

Yueyi Luo ◽

Xiaoyu Zhu ◽

Entao Luo ◽

Mingfeng Huang

Keyword(s):

Reinforcement Learning ◽

Energy Consumption ◽

Low Cost ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Challenging Problem ◽

Learning Technique ◽

Cloud Network ◽

The City ◽

Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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Adaptive Resource Management Utilizing Reinforcement Learning Technique in Inter-Cloud Environments

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1055/1/012124 ◽

2021 ◽

Vol 1055 (1) ◽

pp. 012124

Author(s):

Vadla Pradeep Kumar ◽

Kolla Bhanu Prakash

Keyword(s):

Reinforcement Learning ◽

Resource Management ◽

Adaptive Resource Management ◽

Learning Technique ◽

Cloud Environments ◽

Adaptive Resource

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Reinforcement Learning Technique for Parameterization in Powertrain Controls

10.4271/2021-26-0045 ◽

2021 ◽

Author(s):

Gautham Sidharthan ◽

Vivek Venkobarao

Keyword(s):

Reinforcement Learning ◽

Learning Technique

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Applying a Deep Q Network for OpenAIs Car Racing Game

10.14293/s2199-1006.1.sor-.ppd7fvs.v1 ◽

2020 ◽

Author(s):

Ali Fakhry

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Transfer Learning ◽

State Of The Art ◽

Learning Techniques ◽

Car Racing ◽

Custom Made ◽

Learning Technique ◽

Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

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