• Journal of the Korea Computer Graphics Society
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• v.21 no.5
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• pp.29-36
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• 2015

Human fingers are essential parts of the body that perform complex and detailed motion. Expression of natural finger motion is one of the most important issues in character animation research. Especially, keyboard typing animation is hard to create through the existing animation pipeline because the keyboard typing typically requires a high level of dexterous motion that involves the movement of various joints in a natural way. In this paper, we suggest a method for the generation of realistic keyboard typing motion based on physics simulation. To generate typing motion properly using physics-based simulation, the hand and the keyboard models should be positioned in an allowed range of simulation space, and the typing has to occur at a precise key location according to the input signal. Based on the observation, we incorporate natural tendency that accompanies actual keyboard typing. For example, we found out that the positions of the hands and fingers always assume the default pose, and the idle fingers tend to minimize their motion. We handle these various constraints in one solver to achieve the results of real-time natural keyboard typing simulation. These results can be employed in various animation and virtual reality applications.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.453-459
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• 2024

Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality. We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs, ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation of complex textual descriptions into visual representations.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.505-508
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• 2008

When it grasps a total property losses and relationship of damage factors, there is possibility of reducing the natural disaster damage which is a yearly repeated. Also, checking and supply to vulnerability should be presupposed. This study aims to find out a damage factor vulnerability using a natural disaster database by Park et al(2007). And added 2005 year data to database. Total 10 damage factors are deaths(person, including missing person) injury(person), victims(person), building(thousand), vessel(thousand), cultivated land(thousand), public facilities(thousand), others(thousand), total property losses(thousand). We analyzed of correlation analysis, ratio, population, area, regional character etc for damage factors.

• Journal of the Korean Institute of Landscape Architecture
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• v.32 no.1
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• pp.1-9
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• 2004

This study is focused on finding out the character of urban recreation forest and investigating facilities regulations of forest resources around urban areas to meet future recreation needs. The method of this study is to compare urban recreation forests with similar facilities like urban parks and natural recreation forests. By this way, this study not only identifies the characteristics of urban recreation forest as emerging recreational facility, but also outputs regulations that can be adapted to real environment. Urban recreation forest is defined as follows: It has forest with good natural landscape and easy access. It makes emotion rich and its function is focused on outdoor recreation nature education, and experiences for family group mainly within a day or on weekends. This study suggests that the minimal area of urban recreation forest should be over 100,000$m^2$, with a facility ratio less than 20%. Building coverage should be less than 7% when the urban recreation forest is under 300,000$m^2$, less than 5％ when 300,000∼500,000$m^2$, and less than 3％ when over 500,000$m^2$ The limits of building height is 3 stories, or under 12m. This study finds out the character, the needs of urban recreation and the criterion for project feasibility. significance of the result is supplying theoretical basement on related regulation. Based on the results, further study will establish landscape management method and legislation & application in a detailed examination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.7
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• pp.82-91
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• 1995

Many kind of springs are used in the suspension of automotive vehicles and among these the leaf spring and the air spring are included. These two springs have not been generally used together in one suspension, but recently the automotive models which use these two springs together increase. This reason is due to the merit of the combination of two type springs. The merits are two. One is the character of air spring, that is, the natural frequen- cy of system is constant in spite of variable weight. The other is the character of leaf spring, that is, the suspension mechanism is simple. The combination spring is used in medium size and special purpose bus. In this paper, we formulate the condition which the leaf spring must satisfy to be optimal design in the combination spring. And experiment is performed to prove the theory. The results are that the combination spring is better than leaf spring in the ride, and that the purposed theory is good for the combination spring design.

• Journal of Educational Research in Mathematics
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• v.26 no.3
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• pp.607-633
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• 2016

Teachers and students tend to perceive mathematics irrelevant with character education. Since there has been no teaching of cross-curriculum characters education in mathematics classrooms, this research was to look for the possibility of teaching the character education in mathematics. Through the pilot study, trying to rebuild character factors based on Moon, et al,(2011), the researcher applied them to developing 8 lesson plans and carried them to 12th graders of the high school in March and April, 2014 using pre and post tests. Based on statistically significant difference with the level of p<.05, in "relation with me", for 'appointment' in the pretest, the students of natural science(SNS) exceeded, but in the posttest the students of humanities(SH) exceeded. In "relation with the other", in the posttest, for 'forgiveness' and 'responsibility' SNS, but for 'ownership' SH exceeded. In 'relation with a group' in the pretest for 'community spirit' SNS, and in the posttest SH exceeded. In the pretest in this study SNS naturally perceived the value of character education in math classes since they were closer to mathematics but after the experience through character education with cross-curriculum factors, SH perceived its importance expecially 'appointment', 'ownership', and 'community spirit' more than SNS so that we could predict the possibility of teaching cross-curriculum character factors in mathematics even after they had preconception as a high school student.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1011-1019
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• 2015

CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most recent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from the background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHAs were correctly identified, it was demonstrated that the current CAPTCHA is not safe.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• KIISE Transactions on Computing Practices
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• v.20 no.10
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• pp.549-554
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• 2014

Handwritten character recognition data is usually imbalanced because it is collected from the natural language sentences written by different writers. The imbalanced data can cause seriously negative effect on the performance of most of machine learning algorithms. But this problem is typically ignored in handwritten character recognition, because it is considered that most of difficulties in handwritten character recognition is caused by the high variance in data set and similar shapes between characters. We propose the oversampling-based ensemble learning methods to solve imbalanced data problem in handwritten character recognition and to improve the recognition accuracy. Also we show that proposed method achieved improvements in recognition accuracy of minor classes as well as overall recognition accuracy empirically.