• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.23-30
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• 2020

As the 4th industrial revolution emerged, the latest technologies such as IoT, AI, Big data, AR/VR/XR are emerging. However, in the field of virtual reality (VR) technology platform services, there is no standardization and systematic support. In addition, various platform technologies related to virtual reality have been presented, making it difficult to select an API that should be selected for development. In this study, we analyzed the method for virtual reality development and the virtual reality (VR) technology that is being serviced by users. In addition, by presenting the advantages and disadvantages of each development platform, we intend to present a reference point for developers to select an efficient platform. In addition, it will help the developer to select an effective equipment and software platform in comparison with the advantages and disadvantages of various HMD devices used in virtual reality. The virtual reality (VR) development environment test used products from Oculus, and the software development environment was tested with two types: WebBased VR and HMD embedded.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.905-908
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• 2008

The BTL project, 4 years since its operation, has benchmarked the PFI Project in Japan and has been introduced. Given the evaluation step to select a preferred bidder, in a technological factor, the basic plans are corrected and complemented, whereas in a price factor, the low price bidding system is being enforced. There is concern that how to select preferred bidders and how to operate project costs during operation and management period may be problematic. Thus, in this study, using the Analytic Hierarchy Process, the method of deciding the pribrity to select preferred bidders in an early stage of the project and the evaluation model to evaluate the validity of BTL project expenses in process of project enforcement are established. Targeting the group. composed of experts who have experiences in the BTL project. Then, the levelling of evaluation factors and grouping have been carried out as following: LCC analysis and disbursement for government including two more detailed factors.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.919-926
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• 2002

In this paper, new multicast scheduling algorithms are proposed for the WDM single-hop broadcast-and-select networks. The existing multicast scheduling algorithms are focused on the partitioning a multicast group into several subgroups to reduce the delay time of each receiver. These partitioning algorithms are grouping method of the receivers already tuned to the transmitter's wavelength. However, these algorithms ignore the state of receivers, which leads to increase the number of subgroups and the delay time. Therefore, 1 propose two new multicast scheduling algorithms called H_EAR and PGM that partition a multicast group to subgroups using the tunable transmitter, state information of receivers, and pseudo group concept. The performance of proposed algorithms are evaluated through the computer simulation. They show the better performance comparing with the existing multicast scheduling algorithm.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.499-510
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• 2016

It is very important to select relevant variables in regression models for survival analysis. In this paper, we introduce a penalized variable-selection procedure in multi-level frailty models based on the "frailtyHL" R package (Ha et al., 2012). Here, the estimation procedure of models is based on the penalized hierarchical likelihood, and three penalty functions (LASSO, SCAD and HL) are considered. The proposed methods are illustrated with multi-country/multi-center bladder cancer survival data from the EORTC in Belgium. We compare the results of three variable-selection methods and discuss their advantages and disadvantages. In particular, the results of data analysis showed that the SCAD and HL methods select well important variables than in the LASSO method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.79-88
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• 2007

This paper suggests a hierarchial method to select the target sites for the nonpoint source pollution management considering factors which reflect the interrelationships of significant outflow characteristics of nonpoint source pollution at given sites. The factors consist of land slope, delivery distance to the outlet, effective rainfall, impervious area ratio and soil loss. The weight of each factor was calculated by an analytic hierarchy process(AHP) algorithm and the resulting influencing index was defined from the sum of the product of each factor and its computed weight value. The higher index reflect the proposed target sites for nonpoint source pollution management. The proposed method was applied to the Baran HP#6 watershed, located southwest from Suwon city. The Agricultural Nonpoint Pollution Source(AGNPS) model was also applied to identify sites contributing significantly to the nonpoint source pollution loads from the watershed. The spatial correlation between the two results for sites was analyzed using Moran's I values. The I values were $0.38{\sim}0.45$ for total nitrogen(T-N), and $0.15{\sim}0.22$ for total phosphorus(T-P), respectively. The results showed that two independent estimates for sites within the test water-shed were highly correlated, and that the proposed hierarchial method may be applied to select the target sites for nonpoint source pollution management.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.476-479
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• 2004

In the process of industrialization of our country in 1970's, Korea's housing strategy has focused on housing supply. Rapid completion and supply of apartment house buildings have caused a problem of their early deterioration due to their poor quality. In 1987 Housing Construction Promotion Act, therefore, included new regulations on housing re-construction. In July 2001, other new regulations on remodeling apartment buildings were also included in the Act. Reconstruction and remodeling have been considered to deal with such problems. This study suggests an appraisal criteria if they can select reconstruction or remodeling method in conducting their apartment building project. At first, evaluation items should be selected by the criteria of function31ity and economical efficiency. Second, the selected item is screened by AHP and multi-criteria decision-making methods. The result of study shows that the owners of apartment units will be able to select reasonable alternatives through the suggested appraisal method

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1643-1653
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• 1992

A method, which uses analytical or numerical modal analysis results, e.g. from finite element analysis, to select desirable response measurement and excitation points for an efficient modal testing is introduced. First, points of master degree of freedom(DOP) are determined so as to statistically minimize errors between responses of a full order model and those estimated from the reduced order model. Such master DOF's are selected as the response measurement points. Then a criterion named 'driving point model constant(DPMC)' related to the magnitudes of resonance peaks of the driving point freqency response functions used to select the point of excitation out of the master DOF's. In this work, the method is demonstrated through applications to modal testing on a one dimensional cantilever beam and an aluminum plate and the results are compared with those by another technique. also, the method is applied to a two dimensional structural component of a passenger car.