• 아태비즈니스연구
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• 제10권3호
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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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.204-206
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• 2022

스마트 팩토리(Smart Factory) 고도화의 핵심으로 평가되는 디지털 트윈(Digital Twin)은 현실 세계의 자산과 동일한 속성 및 기능을 가지는 디지털 복제본을 가상의 세계에 구현하는 기술이다. 디지털 트윈 기술이 적용된 스마트팩토리는 생산공정의 실시간 모니터링, 생산공정 시뮬레이션, 생산설비 예지보전 등의 서비스를 지원할 수 있어 생산비용 절감 및 생산성 향상에 기여할 것으로 기대된다. AAS(Asset Administration Shell)는 디지털 트윈을 구현하기 위한 필수 기술로, 현실의 물리적 자산을 디지털로 표현하는 방법을 제공한다. 본 논문에서는 스마트팩토리 내 생산설비를 자산으로 간주하여, 운용 중인 실시간 CNC(Computer Numerical Control) 모니터링 시스템에서 활용할 제조 데이터 수집을 위한 AAS를 설계하였다.

• 대한공간정보학회지
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• 제16권3호
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• pp.29-34
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• 2008

차량용 네비게이션 시스템은 자가 운전 차량의 증가, 여가 문화 확산 등으로 수요가 폭발적으로 증가하고 있으며, 실시간 교통 정보, 디지털 멀티미디어 방송 등의 기능이 융합되면서 텔레매틱스의 가장 중요한 분야로 급성장하고 있다. 이러한 네비게이션 시스템의 다양한 구성 요소 중 네트워크 데이터는 실세계의 도로망을 반영하며, 경로탐색의 기반이 되는 데이터로 가장 핵심 요소라 할 수 있다. 하지만, 현재의 네비게이션 시스템은 stand-alone 형태로 단말기 내의 네트워크 데이터는 자체가 과거의 데이터로 이를 보완하기 위하여 사용자는 주기적으로 업데이트를 수행해야 하는 단점을 가지고 있다. 따라서 본 연구에서는 무선 통신을 활용하여 사용자가 요구하는 경로를 검증하여 항상 최신의 네트워크 데이터를 활용할 수 있는 기법을 제안하고 시뮬레이션을 통하여 제안 기법의 타당성을 검증하였다.

• 자동차안전학회지
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• 제13권1호
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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.

• 자동차안전학회지
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• 제13권1호
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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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• 제14권1호
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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.

• 한국군사과학기술학회지
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• 제20권6호
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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.

• 로봇학회논문지
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• 제15권2호
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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.

• 조명전기설비학회논문지
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• 제29권11호
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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.