• Journal of the Korea Computer Graphics Society
• /
• v.21 no.3
• /
• pp.55-61
• /
• 2015

In this paper, we introduce a method to synthesize realistic make-up effects on input images efficiently. In particular, we focus on shading on the make-up effects due to the lighting and face curvature. By doing this, we can synthesize a wider range of effects realistically than the previous methods. To do this, the information about lighting information together with the normal vectors on all pixels over the face region in the input image. Since the previous methods that compute lighting information and normal vectors require relatively heavy computation cost, we introduce an approach to approximate lighting information using cascade pose regression process and normal vectors by transforming, rendering, and warping a standard 3D face model. The proposed method consumes much less computation time than the previous methods. In our experiment, we show the proposed approximation technique can produce naturally looking virtual make-up effects.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.4
• /
• pp.21-29
• /
• 2017

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.4
• /
• pp.1-6
• /
• 2023

To enhance immersive experiences for metaverse environements, background music is often used. However, the background music is mostly pre-matched and repeated which might occur a distractive experience to users as it does not align well with rapidly changing user-interactive contents. Thus, we implemented a system to provide a more immersive metaverse conversation experience by 1) developing a regression neural network that extracts emotions from an utterance using KEMDy20, the Korean multimodal emotion dataset 2) selecting music corresponding to the extracted emotions from an utterance by the DEAM dataset where music is tagged with arousal-valence levels 3) combining it with a virtual space where users can have a real-time conversation with avatars.

• Journal of Advanced Research in Ocean Engineering
• /
• v.3 no.1
• /
• pp.32-40
• /
• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Structural Engineering and Mechanics
• /
• v.54 no.1
• /
• pp.149-168
• /
• 2015

In the bolted connections, bolt placements are generally described and are generally made in the direction of design effects and in the perpendicular direction to design effects. In these both directions, the reliability of the distance of bolts to the edges of connection plate and the distance of bolts to each other is investigated for high strength steel connections built up with high strength bolts in this study. For this purpose, simple SL (bearing type shear connection) and SLP (bearing type shear connection for body-fit bolts) type steel connections with St 52 grade steel plates with 8 different thicknesses and with 8.8D grade high strength bolts (HV) were constituted and analyzed under H (Dead Loads+Live Loads+Snow Loads+Roof Loads) and HZ (H Loads+Wind Loads+Earthquake Loads) loadings. Geometric properties, material properties and design actions were taken as random variables. Monte Carlo Simulation method was used to compute failure risk and the first order second moment method was used to determine the reliability indexes of those different distances describing the placement of bolts. Results obtained from computations have been presented in graphics and in a Table. Then, they were compared with some values proposed by some structural codes. Finally, new equations were constituted for minimum and maximum values of distances describing bolt placement by regression analyses performed on those results.

• ETRI Journal
• /
• v.11 no.1
• /
• pp.109-122
• /
• 1989

We developed new quadtree and octree representation schemes which reduce the storage requirements from exponential to polynomial. The new schemes not only lessen the large storage requirements of the existing quadtree and octree representation schemes but guarantee an exact representation of the original object. These are made possible by adopting a new set of termination conditions that ensure finiteness of the quadtree and octree during the decomposition. These new data structures are analyzed theoretically and tested empirically. For space complexity, we analyzed its best case, worst case, and average case. Given an $n_e$-gon, we show that the expected number of nodes in our quadtree isO($$$n_e^1.292$) For a polyhedron with $n_f$ faces, the expected number of nodes in the new octree is O($$$n_f^1.667$). For time complexity, we again analyzed the best, worst, and average cases for constructing such quadtree and octree and find the average to be the same as those of the space complexity. Finally, random $n_e$- gons are generated as test data. Regression equations are fitted and are shown to support the claims on the average case performance.

• The Journal of Economics, Marketing and Management
• /
• v.11 no.4
• /
• pp.53-63
• /
• 2023

Purpose: The purpose of this study is to examine the research trends in the papers published by Korean researchers related to traditional markets, to check what topics have been studied, and to make various suggestions for research directions and effective ways to revitalize traditional markets. Research design, data and methodology: To this end, this study conducted word frequency analysis, co-occurrence frequency analysis, BERTopic, LDA, dynamic topic modeling and OLS regression analysis using Python 3.7 on the English abstracts of a total of 502 papers extracted through ScienceON. Results: As a result of word frequency analysis and co-occurrence frequency analysis, it was found that studies related to traditional markets have been conducted not only on factors related to customers, but also on traditional market merchants and government policies, and the degree of service, quality, and satisfaction perceived by customers using traditional markets. Through BERTopic and LDA, three topics such as 'Traditional market safety management' were identified, and among them, it was found that 'Traditional market safety management' is relatively less attention by researchers. Conclusions: The results of this study suggest that future research on the revitalization of traditional markets should be conducted from a specific consulting perspective along with the establishment of various data, a causal model study from various perspectives such as the characteristics of merchants as well as consumers, and an integrated and convergent approach to policy formulation by the government and local governments.

• The Journal of the Korea Contents Association
• /
• v.19 no.10
• /
• pp.21-31
• /
• 2019

With the development of the aviation industry, aircraft painting design also plays the role of transmitting the national image while conveying the individual image of the airline. The recognition and recognition of the national image can be obtained by building a national brand. As a result, more and more companies are using the national brand image of their own country or other countries to add value to the company. Objective: To better reflect the national brand recognition for the design of the tail fin in the future aircraft painting design. This paper mainly studies the correlation between the tail graphic elements of aircraft painting design and national brand recognition based on the tail graphics of the three major airline alliance members. Based on the prior research, the relevant hypotheses were proposed and the questionnaire was designed. Secondly, a questionnaire survey was conducted on the passengers using the aircraft, and the correlation analysis was performed on the data by the SPSS regression analysis method. Conclusion: Data analysis has a strong correlation.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.3
• /
• pp.117-125
• /
• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Spatial Information Research
• /
• v.18 no.5
• /
• pp.119-131
• /
• 2010

Researchers require repetitive works such as data format analysis, reformatting and map reprojection in order to use geospatial data. To solve above problems, they are building web-based simulation systems with web developers. But the web-based systems are not efficiently developed because there is not the appropriate simulation framework for a web-based system using geospatial data. In this study, the geospatial simulation framework that can be effectively applied to the web-based system was designed and proposed. Also, the framework was composed of 7 modules; web mapping service, GIS mapping, statistics, model, processing,graphics, and geospatial datasets. In order to evaluate the effectiveness of the framework, a case study of urban growth has been verified. Experts who are not specialized in geospatial information disciplines expect to build easily a web-based system using geospatial data.