• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.20-20
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• 2009

저항 점 용접은 1930년대에 Thomson에 의해 방법이 제안된 이후로 자동차, 전자, 항공기, 철도산업등에서 박판 금속(sheet metal)의 접합에 가장 널리 사용되고 있는 공정이다. 특히 자동차 차체와 같이 대부분 박판으로 구성되는 구조물에서는 저항 점 용접의 사용 범위가 매우 넓기 때문에 자동차 산업에서는 가장 기본적인 근본 기술 중의 하나로 인식되고 있다. 보통 자동차 한대를 생산하는데 소요되는 저항 점 용접 타점은 3000~4000개 정도로 자동차 차체 용접 공정의 대부분을 차지하고 있다. 또한 로봇과 연동된 자동화 공정으로 적용되고 있다. 최근의 자동차 차체를 구성하는 금속 재료가 자동차의 경량화, 친화경 소재의 사용자의 요구로 인해 새로운 강판이 사용된다. 자동차의 연비 향상을 위해서 다른 방법보다 자동차의 무게를 감소시키는 것이 가장 효율적이고, 쉽기 때문에 고장력 강판의 사용이 급속하게 증가하고 있다. 뿐만 아니라 차제의 부식성, 내마모성 향상을 위해 도금 처리된 강판의 사용도 활발하게 이루어지고 있다. 최근에 도장 공정 감소를 위해 도금 처리위에 도료 착색을 용이하게 하는 도료의 일부를 금속 표면에 처리된 강판의 개발도 진행되는 등 금속 소재의 변화가 다양하게 진행되고 있다. 이러한 새로운 강종은 기존의 AC 용접이나 DC 용접으로는 용접성 확보에 어려움을 가지고 있어, 새로운 저항 점 용접 공정의 연구 개발이 필요하다. 본 연구에서는 저항 점 용접 공정의 개선을 위해서 인버터 저항 점 용접기에서 용접 공정 중 전류를 제어하기 위한 효율적인 제어기 개발 방법과 개발된 제어기를 바탕으로 용접 중에 용접부의 품질을 예측하여, 용접 전류 및 가압력을 실시간 제어하여 안정적인 용접부의 품질을 갖질 수 있는 지능형 저항 점 용접기의 적응 제어기를 개발하는데 있다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.489-495
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• 2014

A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3-5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1497-1508
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• 1993

Real-time mobile robot controllers usually have been designed focused on control theory without paying attention to the importance of system integration. This paper demonstrates that autonomous mobile robots require a real-time controller with a wide range of capabilities in addition to control theory. An architectural frame work supporting these capabilities has been designed in actual hardware environments. Individual modules such as a path planner, a path tracking controller, position estimators, wheel controllers and other cruical elements have been successfully integrated into the control system using this frame work. The overall performance of the system was investigated via a series of tracking experiments with a prototype mobile robot named LCAR deveoped in the laboratory. The context of the research involves the architecture, its implementation and experimental results.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.99-106
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• 2015

This research is focused on the development of the proprietary database embedded in the OTT device, which is used for searching and indexing video contents, and also the development of the search algorithm in the form of the critical components of the interface application with the OTT's database to provide video query searching, such as remote control smartphone application. As the number of available channels has increased to anywhere from dozens to hundreds of channels, it has become increasingly difficult for the viewer to find programs they want to watch. To address this issue, content providers are now in need of methods to recommend programs catering to each viewer's preference. the present study aims provide of the algorithm which recommends contents of OTT program by analyzing personal watching pattern based on one's history.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.55-63
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• 2003

The traffic accident is the prerequisite of the traffic accident reconstruction. In this study, the traffic accident (forward collision) and traffic accident reconstruction (inverse collision) simulations are conducted to improve the quality and accuracy of the traffic accident reconstruction. The vehicle and tire models are used to simulate the trajectories for the post-impact motion of the vehicles after collision. The impact dynamic model applicable to the forward and inverse collision simulations is also provided. The accuracy of impact analysis for the vehicular collision depends on the accuracy of the coefficients of restitution and friction. The neural network is used to estimate these coefficients. The forward and inverse collision simulations for the multi-collisions are conducted. The new method fur the accident reconstruction is proposed to calculate the pre-impact velocities of the vehicles without using the trial and error process which requires the repeated calculations of the initial velocities until the forward collision simulation satisfies with the accident evidences. This method estimates the pre-impact velocities of the vehicles by analyzing the trajectories of the vehicles. The vehicle slides on a road surface not only under the skidding during an emergency braking but also under the steering. A vehicle over steering or cornering with excessive speed loses the traction and leaves tile yaw marks on the road surface. The new critical speed formula based on the vehicle dynamics is proposed to analyze the yaw marks and shows smaller errors than ones of the existing critical speed formula.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.482-489
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• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of Korean Society on Water Environment
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• v.30 no.5
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• pp.491-502
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• 2014

As meteorology is the driving force for lake thermodynamics and mixing processes, the effects of climate change on the physical limnology and associated ecosystem are emerging issues. The potential impacts of climate change on the physical features of a reservoir include the heat budget and thermodynamic balance across the air-water interface, formation and stability of the thermal stratification, and the timing of turn over. In addition, the changed physical processes may result in alteration of materials and energy flow because the biogeochemical processes of a stratified waterbody is strongly associated with the thermal stability. In this study, a novel modeling framework that consists of an artificial neural network (ANN), a watershed model (SWAT), a reservoir operation model(HEC-ResSim) and a hydrodynamic and water quality model (CE-QUAL-W2) is developed for projecting the effects of climate change on the reservoir water temperature and thermal stability. The results showed that increasing air temperature will cause higher epilimnion temperatures, earlier and more persistent thermal stratification, and increased thermal stability in the future. The Schmidt stability index used to evaluate the stratification strength showed tendency to increase, implying that the climate change may have considerable impacts on the water quality and ecosystem through changing the vertical mixing characteristics of the reservoir.