• Journal of the Korean Society of Environmental Restoration Technology
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• v.8 no.5
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• pp.47-55
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• 2005

This study was conducted to suggest revegetation character for environment factor of slope. Field test carried out for the man-made slope with three types(0.5cm no net, 3.0cm no net, 3.0cm net) revegetation methods in Hwaseong. Test revegetation plants were Festuca arundinacea, Lolium perenne, Lespedeza cyrtobotrya and Indigofera pseudo-tinctoria M. The result of this study can be summarized as follows; 1. The soil hardness, the soil acidity, and the soil humidity of three types(0.5cm no net, 3.0cm no net, 3.0cm net) revegetation methods were at a suitable value for plants growth. 2. All plant growth index(seedling number, ground coverage, plant height, plant weight, etc) of south slope were better than north slope. But plant growth index of net plots were similar to no net plots. 3. For washout investigation, washout quantity of north slope was plentifully measured from south slope, and 1 amount of rainfall will be big was visible appears plentifully.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.53-59
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• 2017

Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper- and lower-case characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

• Journal of Korea Game Society
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• v.15 no.6
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• pp.131-140
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• 2015

The character is one of most important interest-element in role playing game genres since it shows the individuality. In general cases, game players allocate points to talent clauses that they choose. However, it is not easy to provide the suitable character-growing to players in generic system since the cases are too simple and based on just humans choices. In this paper, we propose the character growing system based on the player type inference module. Growth morphology is determined by player's behavior or type. The determination is based on petri-net. Our experimental results and analysis show that our proposed approach is suitable for character-growing system.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.86-100
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• 2015

Through advances driven by the Internet and the Smart Revolution, the amount and types of data generated by users have increased and diversified respectively. There is now a new concept at the center of attention, which is Big Data for assessing enormous amount of data and enjoying new values therefrom. In particular, efforts are required to analyze narratives within video clips and to study how to visualize such narratives in order to search contents stored in the Big Data. As part of the research efforts, this paper analyzes dialogues exchanged among characters and offers an interface named "Character-net" developed for modelling narratives. The interface Character-net can extract characters by analyzing narrative videos and also model the relationships between characters, both in the automatic manner. This signifies a possibility of a tool that can visualize a narrative based on an approach different from those used in existing studies. However, its drawbacks have been observed in terms of limited applications and difficulty in grasping a narrative's features at a glace. It was assumed that Character-net could be improved with the introduction of information design. Against the backdrop, the paper first provides a brief explanation of visualization design found in the data information design area and investigates research cases focused on the visualization of narratives present in videos. Next, key ideas of Character-net and its technical differences from existing studies have been introduced, followed by methods suggested for its potential improvements with the help of design-side solutions.

• The Research Journal of the Costume Culture
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• v.8 no.5
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• pp.707-716
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• 2000

The purpose of this study was to explane the Net generation's characters appearing in the apparel brand naming for them. For this study, first I was trying to explane the New generation characters, second 45 apparel brands were selected by market research and questionaire survey was conducted on 53 the Net generation collage women of age 20 thru 21. Third, the apparel brands were classified into four types according to the characters reflecting in the apparel brand naming : First, the apparel brand type using the figures such as STORM=292513, 1492Miles. Second, the apparel brand type that two or more words are abbreviated into one word. Third, the apparel brand type containing more than one meaning in a brand naming or spelling the words as it pronunciate. Fourth, the apparel brand type using of slang. In conclusion, These types of the apparel brands were related to characters of the New generation, i.e., they who have grown in the advance of digital civilization are skilled in the communication through computer, internet and mobile phone, so that they are familiar with the figures, combined words, or abbreviated words etc. Also, they have individual, sensitivity character and seek after individuality, current fashion. They have also a tendency to accept various the sense of value, while they have a refusing tendency a custom or convention which the older generation has conformited.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3265-3276
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• 1996

The studies of document recongnition have been focused mainly on Korean documents. But most of documents composed of Korean and other characters. So, in this paper, we propose the document recognition system that can recognize the multi-size, multi font and mixed type characters. We have utilized a large scale network model, "CombNET" which consists of a 4 layered network with combstructure. And we propose recognition method that can recognize characters without discrimination of character type. The first layer constitutes a Kohonen's SOFM network which quantizes an input feature vector space into several sub-spaces and the following 2-4 layers constitutes BP network modules which classify input data in each sub-space into specified catagories. An experimental result demonstrated the usefulness of this approach with the recognition rates of 95.6% for the training data. For the mixed type character documents we obtained the recognition rates of 92.6% and recognition speed of 10.3 characters per second.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.131-138
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• 2013

In this paper, I propose and evaluate the method that classifies emotional type of characters with their emotional words. Emotional types are classified as three types such as positive, negative and neutral. They are selected by classification of emotional words that characters speak. I propose the method to extract emotional words based on WordNet, and to represent as emotional vector. WordNet is thesaurus of network structure connected by hypernym, hyponym, synonym, antonym, and so on. Emotion word is extracted by calculating its emotional distance to each emotional category. The number of emotional category is 30. Therefore, emotional vector has 30 levels. When all emotional vectors of some character are accumulated, her/his emotion of a movie can be represented as a emotional vector. Also, thirty emotional categories can be classified as three elements of positive, negative, and neutral. As a result, emotion of some character can be represented by values of three elements. The proposed method was evaluated for 12 characters of four movies. Result of evaluation showed the accuracy of 75%.

• Language and Information
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• v.7 no.2
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• pp.81-101
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• 2003

This paper introduces a computerized version of the Korean Standard Pronunciation Rules that can be used in speech engineering systems such as Korean speech synthesis and recognition systems. For this purpose, we build Petri net models for each item of the Standard Pronunciation Rules, and then integrate them into the sound conversion table. The reversion of the Korean Standard Pronunciation Rules regulates the way of matching sounds into grammatically correct written characters. This paper presents not only the sound conversion table but also the character conversion table obtained by reversely converting the sound conversion table. Malting use of these tables, we have implemented a Korean character into a sound system and a Korean sound into the character conversion system, and tested them with various data sets reflecting all the items of the Standard Pronunciation Rules to verify the soundness and completeness of our tables. The test results show that the tables improve the process speed in addition to the soundness and completeness.

• Cross-Cultural Studies
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• v.19
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• pp.233-250
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• 2010

This study aims to observe the features of Korean adjectives composed with symbolic base impling human character. Korean adjectives composed with symbolic base shows more delicate nuances than ordinary adjectives. For observing the feature of them we analyzed the 6000 symbolic words in 'Stanadard Korean Dictionary'. As a result,the symbolic base of adjectives is divided into the one that maintain the basic meaning of symbolic words and the other that do not maintain basic meaning of symbolic words. The base that maintain the basic meaning of symbolic words is divided into the one that has meaning of character and the other that do not has meaning of character. The base that do not maintain the basic meaning of symbolic words is divided into the one that can relate with '-hada' and the other that cannot relates with '-hada'. This study remains the problem in future to examine common points of symbolic base.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.