• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.191-202
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• 2005

The Semantic Web Services is a next-generation Web technology that supports Web services, based on the semantic Web technologies. Until now, the researches on semantic Web services may be foiled on the semantic Web document management and the inference engine to efficiently process the semantic Queries. However, in order to realize the principle semantic Web environment it is necessary to provide a semantic query interface though which users and/or agents can efficiently request semantic information. In this regard, we propose the Semantic Query Adapter(SQA) to provide a high query transparency with users, especially when querying about a complex semantic information. We first design the procedural user query interface based on a graphic view, by analyzing DAML-S Profile documents. And then, we builds a module which a user input query transforms its corresponding RDQL. We also propose the multiple semantic query generating procedure as a new method to solve the disjunctive query problem of the RDQL primitive.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• Journal of Korean Association for Spatial Structures
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• v.23 no.2
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• pp.29-36
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• 2023

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

• The KIPS Transactions:PartC
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• v.9C no.2
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• pp.293-296
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• 2002

UP Link is One of the commercial product which converts HTML to HDML convertor in order to show the internet www contents in the mobile environments. When UP browser accesses HTML pages, the agent in the UP Link controls the converter to change the HTML to the HDML, I-Mode, which is developed by NTT-Docomo of Japan, has many contents through the long and stable commercial service. Micro Explorer, which is developed by Stinger project, also has many additional function. In this paper, we designed and implemented WAP convertor which can accept C-HTML contents and mHTML contents. C-HTML format by I-Mode is a subset of HTML format, mHTML format by ME is similar to C-HTML, So the content provides can easily develop C-HTML contents compared with WAP and the other case. Since C-HTML, mHTML and WML are used under the mobile environment, the limited transmission capacity of one page is also similar. In order to make a match table. After that, we apply conversion algorithm on it. If we can not find matched element, we arrange some tags which only can be supported by WML to display in the best shape. By the result, we can convert over 90% contents.