• Asia-Pacific Journal of Business
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• v.10 no.3
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• pp.1-16
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• 2019

This paper examines how firm's investment has been affected by offshoring and financialization in Korea over the period 2000-2014 by using industry-level data collected from World Input Output Database (WIOD) and firm-level data collected from the KIS-Value Database. The findings are summarized as follows. First, offshoring index as expected shows a negative relationship with real investment. This negative impact is stronger in a large firm group. Second, there is a positive relationship between dividend payments and real investment. The positive relationship is greater in a small & medium-sized firm group. Third, the purchase of financial assets and the income generated from financial assets are positively related to real investment. The positive relationship is stronger in the small & medium-sized firm group. The empirical results show that firm size is a factor that effectively affects firm's real investment. This paper suggests that the influence of financialization and offshoring on firm's real investment should be assessed in various contexts rather than in a unilateral context.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.204-206
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• 2022

Digital twin, which is evaluated as the core of smart factory advancement, is a technology that implements a digital replica in the virtual world with the same properties and functions of assets in the real world. Since the smart factory to which digital twin is applied can support services such as real-time production process monitoring, production process simulation, and predictive maintenance of facilities, it is expected to contribute to reducing production costs and improving productivity. AAS (Asset Administration Shell) is an essential technology for implementing digital twin and supports a method to digitally represent physical assets in real world. In this paper, we design AAS for manufacturing data gathering to be used in real-time CNC (Computer Numerical Control) monitoring system in operation by considering manufacturing facility in smart factory as assets.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.29-34
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• 2008

Demand for car navigation systems has been an explosive increase because of prevalence of owner-drivers, spare time spread, and so on. In addition, car navigation systems are achieving a rapid growth to be the important part of telematics industry with services such as real-time traffic information and DMB(Digital Multimedia Broadcasting). The network data, one of the composition components in car navigation systems, is most important component because that is abstract of real world road network and base data in pathfinding. However most of the car navigation systems have been stand-alone system. Thus user's network data becomes outdated according to the time passing and does not reflect the change of road information in real world. To overcome this problem, users have to update network data in car navigation systems periodically. This method is quite cumbersome process. For this reason, this study proposed a new update method to serve the difference network data on user's device and the real world in real time, and simulated to verify.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.38-44
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the real-world driving study, 52 drivers drove approximately 11.0 km of rural road (about 20 min), 7.9 km of urban road (about 25 min), and 20.8 km of highway (about 20 min). The results suggested that the appropriate number of blinks during the last 60 seconds was 4 times, and the head movement interval was 35 seconds. The results from drowsy driving data will be presented in another paper - part 2.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.45-50
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the drowsy driving study, 10 drivers drove approximately 37 km of a monotonous highway (about 22 min) twice. The results suggested that the appropriate duration of eyes continuously closed was 4 seconds. The results from real-world driving data were presented in the other paper - part 1.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.103-111
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• 2003

Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.822-834
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• 2017

In M&S, radar model is a software module to identify position data of simulation objects. In this paper, we propose a radar performance model for simulations of air defenses. The previous radar simulations are complicated and difficult to model and implement since radar systems in real world themselves require a lot of considerations and computation time. Moreover, the previous radar simulations completely depended on radar equations in academic fields; therefore, there are differences between data from radar equations and data from real world in mission level analyses. In order to solve these problems, we firstly define functionality of radar systems for air defense. Then, we design and implement the radar performance model that is a simple model and deals with being independent from the radar equations in engineering levels of M&S. With our radar performance model, we focus on analyses of missions in our missile model and being operated in measured data in real world in order to make sure of reliability of our mission analysis as much as it is possible. In this paper, we have conducted case studies, and we identified the practicality of our radar performance model.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.197-204
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• 2020

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.11
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• pp.96-101
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• 2015

In recent years, researches regarding optimal placement of CCTV(Closed-circuit Television) cameras via architecture modeling has been conducted. However, for analyzing surveillance coverage through actual human movement, the application of VA(Video Analytics) function of IP(Internet Protocol) cameras has not been studied. This paper compares two methods using data captured from real-world cameras and data acquired from a virtual environment. In using real cameras, we develop GUI(Graphical User Interface) to be used as a logfile which is stored hourly and daily through VA functions and to be used commercially for placement of products inside a shop. The virtual environment was constructed to emulate an real world such as the building structure and the camera with its specifications. Moreover, suitable placement of the camera is done by recognizing obstacles and the number of people counted within the camera's range of view. This research aims to solve time and economic constraints of actual installation of surveillance cameras in real-world environment and to do feasibility study of virtual environment.