• Cartoon and Animation Studies
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• s.46
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• pp.129-150
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• 2017

With the change in educational environment of cartoon creation and diversification of webtoon platforms, various ways of engaging webtoon authors have been suggested. Under this situation, Korea Manhwa Contents Agency(KOMACON) and Korea Creative Content Agency(KOCCA) provide support to webtoon authors directly and indirectly to nurture professional webtoon talents. Contents creative human resource joint project being carried out by KOCCA is mainly to nurture and support contents experts by developing their creativity through tight training between mentors and mentees, creating job opportunities, building the support system for creative activities, and supporting commercialization during the project. Undergoing the process of recruitment and selection, the participants of this project are educated, trained and developed according to education programs provided by the hosting agency, and this project has a model to compensate for creative activities for a ceratin period of time. However, there has been a problem that it is difficult to constantly keep and manage webtoon talents who are cultivated by human resource management of less than one-year project. This study analyzed creative human resource joint project which is a human resource development model, using human recourse theory and suggested a strategic human resource model based on webtoon authors' human resource model development.

• Spatial Information Research
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• v.23 no.2
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• pp.49-57
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• 2015

Recently, Through the linkage and opening, the fusion of the spatial information, it is necessary for productive ecosystem to provide a variety of information and to increase the civil use. Depending on the economic growth, demand for quality of life and well-being has been on the increase. Spatial information service contents for the public convenience has emerged to solve the problem such as health, safety, welfare and discomfort of daily life This study aims to implement search services for a tailored residence space through the three-dimensional data modeling on cadastral information. To achieve this goal, we established the requirements for deriving a registered object by investigating recent trend with respect to existing cadastral data model and defined property and relationship. Focusing on Songpa-gu, Jamsil station in Seoul, we implemented search services for a tailored residence space for three-dimensional right analysis in conjunction with residential and commercial complex building. As a result, we derived a way to supply 3D cadastre information through open platforms (VWorld) and to represent efficiently, which is able to improve the quality of spatial information service contents for the public convenience as well as to widen utilization of information.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.289-296
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• 2014

The rebar detailing is an important work influencing the final performance and quality of RC structures. But it is one of the most irrational and illogical activity in construction site. Many groups of workers, including main contractors, structural engineers, shop drawers, rebar fabricators, and etc., participate in this activity. A loosely-organized process for this activity is apt to produce a big amount of rebar loss or even degraded structures. A 3-dimensional rebar auto-placing system, called as Rebar Hub, has been designed and implemented in this research. Rebar Hub provides a totally integrated service from 3D structural modeling of buildings to rebar auto-placing considering anchorage, splice, and the length of ordered rebar. In addition, Rebar Hub can recognize the 2D drawing CAD files and then build 3D structural models which are used for the start point of 3D rebar auto-placing. After rebar auto-placing, each members of the 3D structural model have rebar information belonging to them. It means that the rebar information can be used for the afterward works such as quantity-survey, manufacturing and fabrication of rebars. Rebar Hub is showing outstanding performance while applying to practical projects. It has almost five times productivity and reduces the rebar loss up to 3~8% of the initially-surveyed amount of rebar.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.29-37
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• 2020

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system. In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques. In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention. Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance. Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system. Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data. In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data. The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.93-101
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• 2018

A city is like a living organism that is born, grows and become extinct within an ecosystem. In recent years, more and more cities have been planned by designers rather than spontaneously growing over time. This planning means that if a city is not appropriately designed at the beginning, it is very hard to fix or adjust it later. A poor urban design inconveniences many people, and forces them to adjust to it. For this reason, it is important to design a city as optimally as possible at the design phase. One of the reasons why a city is not designed optimally is complexity. Previous urban design methods have attempted to resolve the complexity by using a top-down problem-solving method based on the experience and knowledge of the urban planner. However, such an approach does not have the organic characteristics of a bottom-up problem-solving method of an artificial life, based on the creation principle of the ecosystem. Therefore, in this study the general principle of artificial life, which can provide a solution to the bigger problems that accumulate as a result of the solutions to small units of problems, is adopted. This enables us to draw various urban design alternatives, and it proves that the alternatives, despite being drawn through a limited modeling method, have almost no differences from those designed by an expert, and its possibilities of future development has also been verified.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.107-120
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• 2003

Over the decades, a lot of studies have dealt with the traffic characteristics and phenomena at a merging area. However, relatively few analytical techniques have been developed to evaluate the traffic flow at the area and, especially, the ramp merging capacity has rarely been. This study focused on the merging behaviors that were characterized by the relationship between the shoulder lane flow and the on-ramp flow, and modeled these behaviors to determine ramp merge capacity by using gap acceptance theory. In the process of building the model, both an ideal mergence and a forced mergence were considered when ramp-merging vehicles entered the gap provided by the flow of the shoulder lane. In addition, the model for the critical gap was proposed because the critical gap was the most influential factor to determine merging capacity in the developed models. The developed models showed that the merging capacity value was on the increase as the critical gap decreased and the shoulder lane volume increased. This study has a meaning of modeling the merging behaviors including the forced merging type to determine ramp merging capacity more precisely. The findings of this study would help analyze traffic phenomena and understand traffic behaviors at a merging area, and might be applicable to decide the primary parameters of on-ramp control by considering the effects of ramp merging flow.

• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.554-563
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• 2015

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5287-5294
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• 2016

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.

• Journal of health informatics and statistics
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• v.42 no.4
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• pp.309-316
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• 2017

Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.