• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.487-492
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• 2004

This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can't deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. In general, the modeling method of CDES has been studied many times, but that of FDES hasn't been nearly studied by qualitative character and scarcity of applicated system. This paper models traffic system with FDES's character in FTTPN and designs a traffic signal controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.449-453
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• 2006

This paper wishes to present way that can take advantage of parts information of image drawing for efficient maintenance management of facilities efficiently. Information for parts that compose facilities to facilities design drawing has been expressed, and legend character has been written to divide each parts. This paper applies Artificial Intelligence techniques for legend character cognition of image drawing. Finally, apply artificial intelligence techniques to drawing management system to evaluate efficiency of method that propose in this paper that see.

• Proceedings of the KIEE Conference
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• 2007.11a
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• pp.163-164
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• 2007

In this study, we developed intelligent spacer built in the internal type UHF PD sensors. 3-Dimensional electro-magnetic simulations were performed to analyze electric-field distribution of the single-phase GIS and three-phase GIS. After considering the spacer's specification, Sensor structures were designed and analyzed using the 3-D EM Simulator. As a result of the simulation the internal type UHF PD sensors were built in. Performance of the sensor built into real scale GIS spacer was measured in terms of return loss and detected Max voltage. And we identified a character of the intelligent spacer by using 5pC partial discharge ceil.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.15-20
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• 2006

This paper describes research on intelligent game character through performance enhancements of physics engine in computer games. The algorithm that recognizes the physics situation uses momentum back-propagation neural networks. Also, we present an experiment and its results, integration methods that display optimum performance based on the physics situation. In this experiment on integration methods, the Euler method was shown to produce the best results in terms of fps in a simulation environment with collision detection. Simulation with collision detection was shown similar fps for all three methods and the Runge-kutta method was shown the greatest accuracy. In the experiment on physics situation recognition, a physics situation recognition algorithm where the number of input layers (number of physical parameters) and output layers (destruction value for the master car) is fixed has shown the best performance when the number of hidden layers is 3 and the learning count number is 30,000. Since we tested with rigid bodies only, we are currently studying efficient physics situation recognition for soft body objects.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.449-456
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• 2006

This paper proposes intelligent characters for fighting action games to which energy points are applied for more realistic implementation than those of previous researches. The intelligent characters decide their actions in consideration of their energy level as well as a current action, the step of the action, the distance, and past actions of opponent characters that were used in existing intelligent ones. We used two types of energy, HP(Health Point) and MP(Mana Point) that were frequently employed in recent on-line games. We experimented with proposed intelligent characters to investigate whether the intelligent characters learn proper actions and cope with opponent characters in consideration of their energy levels. Experimental results showed that the intelligent characters reacted with the best actions to obtain high score if their energy is sufficient, Otherwise, they did the actions to(that?) recharge their energy. From this observation, we could conclude that the proposed intelligent characters worked well and did effective actions in consideration of the their energy.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.777-784
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• 2017

In the architectural space, the user's behavior is the most important factor in evaluating the quality of architecture. Normally, the evaluation of user behavioral performance was carried out after a building was completed. Recently, interest in and efforts at pre-simulation based on information technology are accelerating. However, since existing user simulation technology is concerned mainly with simply escaping from a large space, it is impossible to simulate the behavior of multiple users in an architectural space. The present study strives to develop a human-figured intelligent agent for advanced user simulation based on ontology. The main purpose of the study is to employ the intelligent behaviors of a NPC(Non-player Character) to infer the ontology of both spatial and user information. In this paper, we intend to integrate ontology inference technology into the virtual space. And also, this study suggest the ontology visualization technology which illustrate the ontology-based information and their change in the spatial information.

• Journal of the Korea Computer Industry Society
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• v.7 no.3
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• pp.163-170
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• 2006

This paper proposes intelligent characters for fighting action games to which energy concepts are applied for more realistic implementation than those of previous researches. The intelligent characters decide their actions in consideration of their energy level as well as a current action, the step of the action, the distance, and past actions of opponent characters that were used in existing intelligent ones. We used two types of energy, HP(Health Point) and MP(Mana Point), that were frequently employed in recent on-line games. We experimented with proposed intelligent characters to investigate whether the intelligent characters loam proper actions and cope with opponent characters in consideration of their energy levels. Experimental results showed that the intelligent characters reacted to the best actions to obtain high score if their energy is sufficient, otherwise they did the actions to recharge their energy. From this observation, we could conclude that the proposed intelligent characters worked well and did effective actions in consideration to the their energy.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.15-25
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• 2002

This paper proposes a vehicle license plate recognition system, which has relatively a simple structure and is highly tolerant of noise, by using the DCT(Discrete Cosine Transform) coefficients extracted from the character region of a license plate and the LVQ(Learning Vector Quantization) neural network. The image of a license plate is taken from a captured vehicle image based on RGB color information, and the character region is derived by the histogram of the license plate and the relative position of individual characters in the plate. The feature vector obtained by the DCT of extracted character region is utilized as an input to the LVQ neural classifier fur the recognition process. In the experiment, 109 vehicle images captured under various types of circumstances were tested with the proposed method, and the relatively high extraction rate of license plates and recognition rate were achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5522-5540
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• 2018

Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.