• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.123-132
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• 2021

In order for the underwater vehicle to perform various tasks, it is important to control the depth, course, and roll of the underwater vehicle. To design such a controller, it is necessary to construct a dynamic model of the underwater vehicle and select the appropriate hydrodynamic coefficients. For the controller design, since the dynamic model is linearized assuming a limited operating range, the control performance in the steady state is well satisfied, but the control performance in the transient state may be unstable. In this paper, in order to overcome the problems of the existing controller design, we propose a A2C(Advantage Actor-Critic) based roll controller for underwater vehicle with stable learning performance in a continuous space among reinforcement learning methods that can be learned through rewards for actions. The performance of the proposed A2C based roll controller is verified through simulation and compared with PID and Dueling DDQN based roll controllers.