• Journal of Digital Convergence
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• v.13 no.7
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• pp.349-357
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• 2015

Giant character used various SF movies and games in media convergence ages, and is essential for giant character in success contents. It This study defined giant character of SF movies and games, it analysed the eight of external characteristics and internal characteristics of giant character in SF movies and games. The external characteristics defined shape, silhouette, size and color, the internal characteristics defined fear, satanism, image and story focus, playfulness. Above, it was structured positioning model of giant character based eight characteristics and analyzation of example of SF movies and games. The elements of positioning model of giant characters are darkness, huge, abnormal, human, animal, fear, satanism, story focused, image focused and playfulness, and this study was proposed these model of elements of eight in SF movies and games.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.336-343
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• 2021

AI technology is spreading not only to industrial fields, but also to culture, art, and content fields. In this paper, we proposed a system based on AI technology that can automate the process of reproducing contents using characters for movie contents. After creating the basic appearance of the character by using the StyleGAN2 model from the video extracted from the movie contents, analyzing the character's personality and propensity using the extracted dialogue data, it was determined from the contemplative appearance based on the yin-yang and five elements to the character's propensity. Accordingly, the external characteristics are reflected in the character. Using the OpenPose model, a character's motion is created, and the finally generated data is integrated to reproduce the content. It is expected that many movie contents can be reproduced through the study of the proposed system.

• Journal of The Korean Association For Science Education
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• v.37 no.5
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• pp.847-857
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• 2017

This study examined the effect of the Collaborative Problem-Solving for Character Competence (CoProC) instruction model within the context of secondary science education. The participants of this study were comprised of 143 Korean students, each of whom was in the 10th grade spread across four class cohorts. These cohorts were further divided into an experimental group (comprised of 73 students from two different classes), which received the CoProC program; and a control group (70 students from two other classes), which did not. In order to assess the effect of CoProC instruction model upon participants' character competence, we designed and administered a Character Competence Test for participants. The CoProC instruction model consists of 3 fundamental steps: Preparation, Problem-solving, and Evaluation. Key character competence targeted in the CoProC program include caring, collaboration, communication, responsibility, respect, honesty, self-regulation, and the development of positive self-image. Thus, these same qualities were targeted and analyzed in the Character Competence Test, which was administered before and after the CoProC activities. The results show a significant increase in the experimental group's competency for caring, collaboration, responsibility, respect, and self-regulation when compared to the control group. Based on these results, we have found that CoProC instruction model to be an effective teaching intervention toward cultivating character competence in a secondary science education setting.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2883-2893
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• 2018

We suggest a stratified conditional unrelated question randomized response model to more efficiently estimate a sensitive character A when the population is composed of several strata. In that model, only the respondents who answered "yes" through randomization device which was consisted of a less sensitive character B and a question forcing to answer "yes" respond to our suggested model and we deal with two allocation problems of proportional allocation and optimal one. We expand the suggested model into two sample stratified conditional unrelated question model to cover the case of unknowing unrelated character and deduce minimal variance through optimal sample size of stratum h. Finally, we show that the suggested model is more efficiency than stratified unrelated models and the stratified Carr et al.'s model (1982) under some given conditions, and show numerically that the smaller the values ${\pi}_{h2}$ and ${\pi}_{hy}$, the more efficiency the fit of the model.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.150-151
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• 2002

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.133-142
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• 2019

In physics-based character animation, trajectory optimization has been widely adopted for automatic motion synthesis, through the prediction of an optimal sequence of future states of the character based on its system dynamics model. In general, the system dynamics model is neither in a closed form nor differentiable when it handles the contact dynamics between a character and the environment with rigid body collisions. Employing smoothed contact dynamics, researchers have suggested efficient trajectory optimization techniques based on numerical differentiation of the resulting system dynamics. However, the numerical derivative of the system dynamics model could be inaccurate unlike its analytical counterpart, which may affect the stability of trajectory optimization. In this paper, we propose a novel method to derive the closed-form derivative for the system dynamics by properly approximating the contact model. Based on the resulting derivatives of the system dynamics model, we also present a model predictive control (MPC)-based motion synthesis framework to robustly control the motion of a biped character according to on-line user input without any example motion data.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• The Journal of Korean Philosophical History
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• no.30
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• pp.283-310
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• 2010

This paper attempted to solve the problem of contemporary youth character education through the traditional character education. For this purpose, to revaluate the educational contents of the Chosen Dynasty with current viewpoint and investigate there is what kind of meaning in current society. In other words, tried to observe about principles and methodology of youth education which based on the human being and education theory of the Confucianism. Specially analysis led about youth education theory of the Toegye and Yulgok who represents Korean studying abroad, to suggest learning model that can apply today and propose that will be able to succeed traditional education in modern ways in future. This paper reviewed that using a traditional education model for the development of youth character education as a basic task of character education program, and proposed its three kinds of components and learning model. First, Arranged character education by 10 kinds of core concept. Second, divided character education into 4 stages - recognition, reflection, application, expansion. Then suggested based on the will, emotion and mind- each of the key concept reapplied by the 4 stages.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.78-83
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• 2004

In the early days, we could be provided almost internet contents without pay. It's been turning out the time we need to pay for getting some internet contents as it has become one of important media. It's already been a kind of big market. We need to develop the revenue model of character brand in the internet business. Character brand could make the internet contents on a position of advantage. We've made the revenue through using character brand and making commodities and services more valuable. We use the internet in a wide range of daily life communication. It's expected to extend. It's needed to study which kind of revenue model is existed at the moment.

• Proceedings of the IEEK Conference
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• summer
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• pp.259-262
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• 2004

In most of the character recognition systems, the method of template matching or statistical method using hidden Markov model is used to extract and recognize feature shapes. In this paper, we used modified chain-code which has 8-directions but 4-codes, and made the chain-code of hand-written character, after that, converted it into transition chain-code by applying to HMM(Hidden Markov Model). The transition chain code by HMM is analyzed as signal flow graph by Mason's theory which is generally used to calculate forward gain at automatic control system. If the specific forward gain and feedback gain is properly set, the forward gain of transition chain-code using Mason's theory can be distinguished depending on each object for recognition. This data of the gain is reorganized as tree structure, hence making it possible to distinguish different hand-written characters. With this method, $91\%$ recognition rate was acquired.