• 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%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.111-114
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• 1997

Usually, Evolutionary Algorithms are considered more efficient for optimal system design, However, the performance of the system is determined by fitness function and system environment. In this paper, in order to overcome the limitation of the performance by this factor, we propose a co-evolutionary method that two populations constantly interact and coevolve. In this paper, we apply coevolution to neural network's evolving. So, one population is composed of the structure of neural networks and other population is composed of training patterns. The structure of neural networks evolve to optimal structure and, at the same time, training patterns coevolve to feature patterns. This method prevent the system from the limitation of the performance by random design of neural network structure and inadequate selection of training patterns. In this time neural networks are trained by evolution strategies that are able to apply to the unsupervised learning. And in the coding of neural networks, we propose the method to maintain nonredundancy and character preservingness that are essential factor of genetic coding. We show the validity and the effectiveness of the proposed scheme by applying it to the visual servoing of RV-M2 robot manipulators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.648-651
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• 2003

In this paper, we propose the recognition system of a license plate using SOM algorithm. The recognition of license plate was investigated by means of the SOM algorithm. The morphological information of horizontal and vertical edges was used to extract a plate region from a car image. In addition, the 4-direction contour tracking algorithm was applied to extract the specific area, which includes characters from an extracted plate area. Therefore, we proposed how to extract license plate region using morphological information and how to recognize the character string using SOM algorithm. In this paper, 50 car images were tested. The extraction rate obtained by the proposed extraction method showed better results than that from the color information of RGB and HSI, respectively. And the license plate recognition using SOM algorithm was very efficient.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.326-329
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• 2003

Controller that is designed in this paper is form that apply PID controller about Fuzzy algorithm. Fuzzy Controller that using this paper is can speak that compensation style fuzzy controller as form to solidify action of PID controller for plant. This is not form that autotuning the each PID coefficient. We Apply and examined the response character to AGC(Automatic Generation Control) system using designed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.31-34
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• 2003

현재 구현되어 있는 한국어 철자 교정 시스템은 문장의 문법 정보나 연어 관계로부터 문장의 오류를 처리하는 방식을 쓰고 있다. 본 논문에서는, 홑문장에서 의미소 사이의 관계를 이용하여 오타 문자를 수정하고 오타에 의한 의미적인 오류가 있을 때에는 의미에 해당하는 적절한 단어를 대체하여 제공하는 시스템을 제안한다. 단어의 뜻에 따라 체언은 의미 트리를 형성하고, 서술어는 주어 및 목적어의 체언과 의미 관계를 정의한다. 오류가 포함된 문장에서, 의미 관계를 비교, 분석하여 주어 및 목적어의 체언이 틀렸을 경우에는 서술어로부터, 서술어가 틀렸을 경우에는 주어 및 목적어의 체언으로부터, 수식어가 틀렸을 경우에는 체언 또는 서술어로부터 정의된 상호 의미 관계를 이용하여 한 문자에 대한 오타를 수정하고 오타에 의한 의미적 오류가 발견될 때에는 상기와 같은 철자 교정 방법을 적용하였다.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.65-77
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• 2004

This paper presents a priori and the local font classification method. The font classification uses ascenders, descenders, and serifs extracted from a word image. The gradient features of those sub-images are extracted, and used as an input to a neural network classifier to produce font classification results. The font classification determines 2 font styles (upright or slant), 3 font groups (serif sans-serif or typewriter), and 7-font names (Postscript fonts such as Avant Garde, Helvetica, Bookman, New Century Schoolbook, Palatine, Times, and Courier). The proposed a priori and local font classification method allows an OCR system consisting of various font-specific character segmentation tools and various mono-font character recognizers. Experiments have shown font classification accuracies reach high performance levels of about 95.4 percent even with severely touching characters. The technique developed for tile selected 7 fonts in this paper can be applied to any other fonts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.435-436
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• 2021

본 논문에서는 RPA(Robotic Process Automation) Tool 개발 과정 중 OCR기법을 활용한 영수증 인식 후 가계부 생성에 관한 자동화 처리 과정을 기술한다. 개발된 RPA 툴은 AI분야에 사용될 데이터의 데이터 전처리 기능을 제공하고 그 외에 반복적으로 사용되는 기능들의 자동화를 제공한다. 그 중 영수증을 이용하여 가계부 작성을 자동으로 처리해주는 기능은 반복적이고 시간이 많이 소요되는 작업으로 이 기능을 활용하면 작업의 수행시간을 단축하고 효율적인 관리가 가능하다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.12-22
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• 2010

The variable message sign(VMS) is a facility to smoothen traffic flows and enable safe passing by providing real-time necessary information on roads, weather, transportation, and traffic regulations. The VMS also solves a feeling of uneasiness and gives a sense of psychological security by providing information to drivers. However, the VMS has a strong character of being a non-market product but a public product, so it has not normally been evaluated for its value. This research has evaluated a value of satisfaction level for traffic information users, using a contingent valuation method(CVM). As a result of evaluating the value of satisfaction level for users through division into an urban roadway and an urban highway for the cities where an intelligent transportation system(ITS) has been established, the urban highway had a value of 96.7 won/system and the urban roadway had a value of 76.3 won/system.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.