• Cartoon and Animation Studies
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• s.37
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• pp.143-158
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• 2014

In the modeling processes of 3D computer animation, methods to build optimal work conditions to realize real forms for more efficient works have been advanced. Digital sculpting software, published in 1999, ZBrush has been positioned as an essential factor in character model work requiring of realistic descriptions through different manufacturing methods from previous modeling work processes and easy shape realization. Their functional areas are expanding. So, in this production case paper, as a method to product more optimized animation character models, the efficiency of production method linking digital sculpting software (Z-Brush) and animation production software (Maya) was deliberated and its consequences and implications are suggested. To this end, first the technical features of polygon modeling and Retopology were reviewed. Second, based on it, the efficiency of animation character modeling work processes through step linking ZBrush and Maya suggested in this paper was analyzed. Third, based on the features drawn before, in order to prove the hypothesis on modeling optimization method suggested in this paper, the production process of character Dumvee from a short animation film, 'Cula & Mina' was analyzed as an example. Through this study, it was found that technical approach easiness and high level of completion could be realized through two software linked work processes. This study is considered to be a reference for optimizing production process of related industries or modeling-related classes by deliberating different modeling process linked systems.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.54-64
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• 2015

The modular construction has several advantages such as high quality of product, safe work condition and short construction duration. The manufacturing planning of modular construction should consider time frame of manufacturing, transport and erection process with limited resources (e.g., modular units, transporter and workers). The manufacturing planning of multi modular construction project manages the modular construction's characteristics and diversity of projects, as a type of modular unit, modular unit quantities, and date for delivery. However, current modular manufacturing planning techniques are weak in dealing with resource interactions and each project requirement in multi modular construction project environments. Inefficient allocation of resources during multi modular construction project may cause delays and cost overruns to construction operation. In this circumstance, this research suggest a manufacturing planning model for schedule optimization of multi project of modular construction, using genetic algorithm as one of the powerful method for schedule optimization with multiple constrained resources. Comparing to the result of the existed schedule of case study, setting optimized scheduling for multi project decrease the total factory producing schedule. By using proposed optimization tool, efficient allocation of resource and saving project time is expected.

• Korean Journal of Materials Research
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• v.18 no.10
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• pp.547-551
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• 2008

Crack-free joining of $Si_3N_4\;and\;Al_2O_3$ using 15 layers has been achieved by a unique approach introducing Sialon polytypoids as a functionally graded materials (FGMs) bonding layer. In the past, hot press sintering of multilayered FGMs with 20 layers of thickness $500{\mu}m$ each has been fabricated successfully. In this study, the number of layers for FGM was reduced to 15 layers from 20 layers for optimization. For fabrication, model was hot pressed at 38 MPa while heating up to $1700^{\circ}$, and it was cooled at $2^{\circ}$/min to minimize residual stress during sintering. Initially, FGM with 15 layers had cracks near 90 wt.% 12H / 10 wt.% $Al_2O_3$ and 90 wt.% 12H/10 wt.% $Si_3N_4$ layers. To solve this problem, FEM (finite element method) program based on the maximum tensile stress theory was applied to design optimized FGM layers of crack free joint. The sample is 3-dimensional cylindrical shape where this has been transformed to 2-dimensional axisymmetric mode. Based on the simulation, crack-free FGM sample was obtained by designing axial, hoop and radial stresses less than tensile strength values across all the layers of FGM. Therefore, we were able to predict and prevent the damage by calculating its thermal stress using its elastic modulus and coefficient of thermal expansion. Such analyses are especially useful for FGM samples where the residual stresses are very difficult to measure experimentally.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.317-327
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• 2016

Flipped learning is a new learning technique which can maximize the learning effect by mixing two or more different learning environments including online & offline, and recently introduced system: 'Google classroom' is the optimized internet platform for flipped learning. This study tried to apply flipped learning to regular course 2nd grade dental students(n=70) and evaluated the satisfaction of students. The subjects of periodontology and operative dentistry were chosen to evaluate flipped learning model for regular course 2nd grade dental students(n=70). Each class consisted of six classes, and three times of them were performed in conventional classes and the other three times were in flipped learning method by using Google classroom. Evaluation of satisfaction progressed at the end of class. In this study, application of flipped learning in the dental college classes showed high efficiency in terms of degree of understanding, self-directed learning and motivation. Collectively, it was shown that flipped learning using Google classroom can be a reliable platform in dental classes.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.4
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• pp.592-601
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• 2015

Rubus coreanus is known as Korean black raspberry, native to Korea, Japan, and China. Preliminary studies evaluating their potential for cancer treatment in mammalian test systems are ongoing. In recent years, interest has been renewed due to their high levels of anthocyanins. Anthocyanins in black raspberry are important due to their potential health benefits as dietary antioxidant, anti-inflammatory compound, and as a chemopreventive agent. In the present study, Saccharomyces cerevisiae BA29 was isolated from black raspberry fruit and fruit juice as a biogenic amine non-producing strain for manufacturing of black raspberry wine, after which we investigated its characteristics: biogenic amine-producing ability, cell growth ability, alcohol-fermentation ability, and resistance to alcohol, glucose, and sulfur dioxide. Based on preliminary experiments, we optimized culture medium compositions for improving dried cell weight of S. cerevisiae BA29 by response surface methodology (RSM) as a statistical method. Design for RSM used a central composite design, and molasses with the industrial applicability was used as a carbon source. Through statistical analysis, we obtained optimum values as follows: molasses 200 g/L, peptone 30 g/L, and yeast extract 40 g/L. For the model verification, we confirmed about 3-fold improvement of dried cell weight from 6.39 to 20.9167 g/L compared to basal yeast peptone dextrose medium. Finally, we manufactured black raspberry wine using S. cerevisiae BA29 and produced alcohol of 20.33%. In conclusion, S. cerevisiae isolated from black raspberry fruit and juices has a great potential in the fermentation of black raspberry wine.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.682-691
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• 2005

We present a two-step evolution system that produces controllable virtual creatures in physically simulated 3D environment. Previous evolutionary methods for virtual creatures did not allow any user intervention during evolution process, because they generated a creature's shape, locomotion, and high-level behaviors such as target-following and obstacle avoidance simultaneously by one-time evolution process. In this work, we divide a single system into manageable two sub-systems, and this more likely allowsuser interaction. In the first stage, a body structure and low-level motor controllers of a creature for straight movement are generated by an evolutionary algorithm. Next, a high-level control to follow a given path is achieved by a neural network. The connection weights of the neural network are optimized by a genetic algorithm. The evolved controller could follow any given path fairly well. Moreover, users can choose or abort creatures according to their taste before the entire evolution process is finished. This paper also presents a new sinusoidal controller and a simplified hydrodynamics model for a capped-cylinder, which is the basic body primitive of a creature.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.539-547
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• 2023

In recent years, university education has emphasized a learner-centered education model with a change in educational paradigm. This study aims to explore students' diverse opinions and improve the quality of education by analyzing the open-ended responses of university lecture evaluations using topic modeling. To this end, a total of 45,001 open-ended responses based on the results of lecture evaluations from 2017 to 2022 in non-metropolitan universities were divided into majors and liberal arts, and a short-form optimized Biterm Topic Modeling (BTM) analysis was conducted. As a result of the analysis, major lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward questions and discussions", "attitude toward attendance and grading", "attitude toward practical activities and presentations", and "attitude toward communication and collaboration", while liberal arts lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward grades and evaluations", "attitude toward attendance and syllabus", "attitude toward academic knowledge and interest", and "attitude toward communication and questions". The results of this study, which analyzed various feedback from students, provide insights that can be used to compare the characteristics of majors and liberal arts courses and improve teaching and learning experiences.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Bulletin of the Korean Space Science Society
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• 2007.10a
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• pp.139-143
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• 2007

Electric propulsion has become a cost effective and sound engineering solution for many space applications. The success of SMART-1 and MUSES-C developed by European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) each proved that even small spacecraft could accomplish planetary mission with electric propulsion systems. A small electric propulsion system which is Hall effect thruster like SMART-1 is under development by SaTReC and GDPL (Glow Discharge Plasma Lab.) in KAIST for the next microsatellite, STSAT-3. To achieve optimized propulsion system, it is very necessary to understand plasma motions of Hall effect thruster. In this paper, we try to approach comprehensive plasma model with the particle simulation complementary to Particle In Cell (PIC) simulation. We think these two different approaches will help experimenters to optimize Hall effect thruster performances.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.