• Korea Trade Review
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• v.44 no.1
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• pp.1-15
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• 2019

This study aims to analyze the effects of trade on human capital accumulation and economic growth in Korean manufacturing industry. The results of empirical analysis by dynamic panel model are as follows. The increase in exports of skilled labor intensive industries has a positive effect on human capital and economic growth, and the impact of import on human capital accumulation and economic growth has alst a positive impact. The exports of unskilled intensive labor industries have a negative impact on human capital accumulation and economic growth. Imports of unskilled labor intensive industries have negative on human capital accumulation and economic growth. It is difficult to derive statistically significant results for the effects of trade on human capital accumulation and economic growth before and after 2008. However, as a result of the financial crisis in 2008, it seems that the effects have decreased since 2008.

• Korea Trade Review
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• v.47 no.6
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• pp.211-228
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• 2022

With the advent of a recent knowledge-based society, interest in patents is steadily increasing. The patent is an important indicator that can capture the level of R&D investment and technology development. In an era of deepening new protectionism and the pandemic of COVID-19, patents play an important role in sustainable economic development and establishing a strong domestic industrial ecosystem. In this paper, we analyze the impact of patent applications on the corporate performance of the Korean manufacturing industry over the past 21 years from 1999 to 2019. We divide patents into overseas patents and domestic patents and analyze the respective effects on the entire manufacturing industry, ICT industries, and non-ICT industries. Major findings are summarized as follows. First, patents have a positive effect on both exports and sales of Korean manufacturing companies. Second, overseas patents have a greater impact on corporate performance than domestic patents. Third, Patents have a more positive effect on ICT industries than on non-ICT industries.

• International Commerce and Information Review
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• v.16 no.3
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• pp.183-202
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• 2014

The time-lag effect of the policy was analyzed focusing on the financial subsidies which are the incentive for attracting the foreign direct investment for the Korean industries from 2007 to 2012. The analysis results show that Korea's policy for attracting the foreign direct investment has the time leg of 2 or 3 years after the implementation of the policy. If the goal is to attract the foreign investment or introduce the advanced industrial technologies, the tax reduction system would be better. However, if the goal is to get the short term effects such as job creation or regional development, the direct subsidy or the financial support (financing) or the lexicographic characteristics of the policy for foreign investment would be more effective for attracting the foreign investment. Accordingly, the Korea's policy for attracting the foreign direct investment should be focused on the realistic policies such as direct subsidies or financial support (financing) rather than the tax reduction system.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.61.1-61.1
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• 2012

We examine both optical and optical/near-infrared (NIR) color distributions of the globular cluster (GC) system in the core of the Coma cluster of galaxies (Abell 1656), centered on the giant elliptical galaxy NGC 4874, to study how non-linearities in the color-metallicity relations of GC systems in large elliptical galaxies are linked to bimodal optical color distributions. Since optical-NIR color distributions of extragalactic GC systems reflect the underlying features of the metallicity distributions, we also present the color-color relation for this GC system. In order to do this, we combine F160W ($H_{160}$) NIR imaging data acquired with the Wide Field Camera 3 IR Channel (WFC3/IR), newly installed on Hubble Space Telescope (HST), with F475W ($g_{475}$) and FF814W ($I_{814}$) optical imaging data from the HST Advanced Camera for Surveys (ACS). To quantitatively explain the feature of color distributions, we use the Gaussian Mixture Modeling (GMM) code. Finally, we show the radial distribution of the GCs in the field of NGC 4874.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.207-210
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• 2007

As the Internet is prevalent in our life, harmful contents have been increasing on the Internet, which has become a very serious problem. Among them, pornographic video is harmful as poison to our children. To prevent such an event, there are many filtering systems which are based on the keyword based methods or image based methods. The main purpose of this paper is to devise a system that classifies the pornographic videos based on the audio information. We use Mel-Cepstrum Modulation Energy (MCME) which is modulation energy calculated on the time trajectory of the Mel-Frequency cepstral coefficients (MFCC) and MFCC as the feature vector and Gaussian Mixture Model (GMM) as the classifier. With the experiments, the proposed system classified the 97.5% of pornographic data and 99.5% of non-pornographic data. We expect the proposed method can be used as a component of the more accurate classification system which uses video information and audio information simultaneously.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.103-111
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• 2020

Perforated metal sheets are used as panels of facades for controlling environmental factors while ensuring user's visibility. Despite their functional potentials, only a specific direction of facades or an orientation of a building was considered in the relevant studies. This study proposed a design methodology for the perforated panel facades that reflects the location on the facades and the user's requirements. The optimization of quantitative and qualitative performance is achieved through communication between designers and users in a VR system. In optimizing quantitative performances, designers use machine learning techniques such as clustering and genetic algorithm to allocate optimal panels on the facades. In optimizing qualitative performances, through the VR system, users intervene in evaluating performances whose preferences are depending on them. The experiment using the office project showed that designers were able to make decisions based on clustering using GMM to optimize multiple quantitative performances. The gap between the target and final performance could be narrowed by limiting the types of perforated panels considering mass customization. In assessing visibility as a qualitative performance, users were able to participate in the design process using the VR system.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.457-462
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• 2003

In this paper, we propose a speaker pruning method for real-time processing and improving performance of speaker identification system based on GMM(Gaussian Mixture Model). Conventional speaker identification methods, such as ML (Maximum Likelihood), WMR(weighting Model Rank), and MWMR(Modified WMR) we that frame likelihoods are calculated using the whole frames of each input speech and all of the speaker models and then a speaker having the biggest accumulated likelihood is selected. However, in these methods, calculation cost and processing time become larger as the increase of the number of input frames and speakers. To solve this problem in the proposed method, only a part of speaker models that have higher likelihood are selected using only a part of input frames, and identified speaker is decided from evaluating the selected speaker models. In this method, fm can be applied for improving the identification performance in speaker identification even the number of speakers is changed. In several experiments, the proposed method showed a reduction of 65% on calculation cost and an increase of 2% on identification rate than conventional methods. These results means that the proposed method can be applied effectively for a real-time processing and for improvement of performance in speaker identification.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.59-72
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• 2020

This paper examines whether lending structure can lower credit risk by employing econometric techniques of panel data for the Vietnamese banking system at the bank level used by economic sectors from 2011 to 2016. New light is being shed on assessing the impact of each industry's debt outstanding on credit risk. Adopting findings from previous studies, we assess credit risk from two different sources, including loan loss provision and non-performing loan. Moreover, we also focus on observing lending structure in many different aspects, from concentrative levels to the short-term and long-term stability levels of lending structure. The Generalized Method of Moments (GMM) estimator was applied to analyze the relationship between concentration and banking risks. In general, the results show that lending concentration may decrease credit risk. It is interesting to observe that the Vietnamese commercial bank lending portfolios have, on average, higher levels of diversity across different sectors. In particular, the increase in hotel and restaurant lending contributes to decrease credit risk while the lending portfolios of banks in agriculture, electricity, gas and water increase credit risk. This study suggests the need for further analysis and research about portfolio risks in lending activities for maintaining efficiency and stability in the commercial banking system.