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RISS 인기검색어
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- 저자 : Seng Dewen,Tang Meixia,Wu Hao,Fang Xujian,Xu Haitao (검색결과 3 건)
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Multiple Constrained Dynamic Path Optimization based on Improved Ant Colony Algorithm
Seng Dewen,Tang Meixia,Wu Hao,Fang Xujian,Xu Haitao 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.6
Vehicle navigation system can effectively alleviate traffic congestion, reduce pollution, and reduce travel cost and other problems. As is known to all, the traditional ones are all just static path planning with problems of not only weak effectiveness but also lack of standard optimal path options. They usually provide only one path which represents the shortest time or shortest distance, and ignore the actual demands of the dirivers. Based on traffic data of the past, the upcoming traffic flows can be estimated. With the help of the improved ant colony algorithm, the dynamic optimal path planning results will meet the need of the travelers according with multiple actual constraints.
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Visual Analysis of Deep Q-network
( Dewen Seng ),( Jiaming Zhang ),( Xiaoying Shi ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.3
In recent years, deep reinforcement learning (DRL) models are enjoying great interest as their success in a variety of challenging tasks. Deep Q-Network (DQN) is a widely used deep reinforcement learning model, which trains an intelligent agent that executes optimal actions while interacting with an environment. This model is well known for its ability to surpass skilled human players across many Atari 2600 games. Although DQN has achieved excellent performance in practice, there lacks a clear understanding of why the model works. In this paper, we present a visual analytics system for understanding deep Q-network in a non-blind matter. Based on the stored data generated from the training and testing process, four coordinated views are designed to expose the internal execution mechanism of DQN from different perspectives. We report the system performance and demonstrate its effectiveness through two case studies. By using our system, users can learn the relationship between states and Q-values, the function of convolutional layers, the strategies learned by DQN and the rationality of decisions made by the agent.
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Process Backtracking and Reconstruction based on Task Chain Model
Jing Chen,Dewen Seng,Xujian Fang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.4
Fundamental information of the design unit is described using the correlation between the nodes in the task chain. Data transfer between design units is formulated as fundamental data transfer, design rule transfer and path scheme transfer, respectively. The design process is stored with the node as the unit by using the algorithm for decomposing correlated nodes. The reconstruction method is employed to eliminate the redundant nodes that exist in various previous design processes, alleviating the degree of coupling. The performance of the proposed scheme is verified by applying it to the development of the low-voltage appliance.
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