• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.221-226
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• 2007

This paper suggests a valuation framework of investment project using the concept of real options. We show the valuation process of real assets using the risk-neutral pricing. Especially, we focus on the investment lag. Real assets have investment lag in general. The decision time and the payment time are not identical. So the investment lag should be considered when valuing real assets for reality. We provide the valuation process for real assets, including R&D project. The results of this paper can be used for the real assets valuation and strategic decision analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.660-665
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• 1993

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD ( an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very fast training and processing implementation required for real time control.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.89-118
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• 1996

The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Video Image Detector systems (VIDs) is presented. Through VIDs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is between under the fuzzy logic controller. Integration of the aforementioned schemes into and ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.327-330
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• 2007

This article discusses a capacity planning method in QoS-guaranteed IP networks such as BcN (Broadband convergence Network). Since IP based networks have been developed to transport best-effort data traffic, the introduction of multi-service component in BcN requires fundamental modifications in capacity planning and network dimensioning. In this article, we present the key issues of the capacity planning in multi-service IP networks. To provide a foundation for network dimensioning procedure, we describe a systematic approach for classification and modeling of BcN traffic based on the QoS requirements of BcN services. We propose a capacity planning framework considering data traffic and real-time streaming traffic separately. The multi-service Erlang model, an extension of the conventional Erlang B loss model, is introduced to determine required link capacity for the call based real-time streaming traffic. The application of multi-service Erlang model can provide significant improvement in network planning due to sharing of network bandwidth among the different services.

• Journal of Construction Engineering and Project Management
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• v.2 no.3
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• pp.48-57
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• 2012

With increasing number of serious construction disasters such as death, a safety management in real-time is more required recently. One of the ways to support the needs, a convergence with Information Technology (IT) has been noticed. Although number of inefficiencies including a limitation of IT equipment (weight, control inconveniences) has been concerned, data exchanging and processing fields have been improved at a wide range with an advance of mobile communication and an introduction of smart mobiles that are simple and convenient. In this research, a framework is realized in connection with smart mobile to find the problems of current safety management and derive demands in order to support effective safety management. Two smart mobile applications, Safety Management application and Safety Guideline application, enable a real-time safety management and less re-work by provided safety information.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.419-423
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• 2009

u-City 상에는 다양한 종류의 임베디드 기기가 존재하며, 최근 이들 간의 다양한 데이터 처리 및 기기 간 통신의 상호운영성에 대한 문제가 이슈화 되고 있다. RT-EdSB (Real-time Embedded Service Bus)는 ESB와 DDS 그리고 CEP를 기반으로 하는 프레임워크로 임베디드 기기에서 발생하는 다양한 수많은 데이터의 실시간으로 처리하면서 동시에 임베디드 기기간의 통신의 상호운영성을 보장하는 것을 목표로 하고 있다. 본 논문에서는 RT-EdSB 프레임워크 아키텍처를 제안하고 또한 u-City에서의 RT-EdSB의 활용 방안을 제시하고 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.485-491
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• 1998

이산사건 시스템은 시간의 이산순간에 상태변화가 발생하는 시스템으로서 공정제어, Robotics, 교통시스템, Flexible Manufacturing System, 통신등 많은 분야의 시스템이 이산사건 시스템들이지만 아직도 포괄적이고 융통성 있는 제어이론이 연구되지 않았다. 본 연구는 특히 Real-Time Temporal Logic Framework(RTTL)에서 비결정적으로 발생되어지는 확정적인 사건들로써 유발되어지는 비결정적 이산사건 시스템의 모델링 방법을 제시하였다. 이 방법을 두 개의 machine들로 구성된 Flexible Manufacturing System(FMS)에 적용하여 설명하였다. 이 방법은 복잡한 이산사건 시스템의 모델링을 모듈화하여 간편하게 표현 할 수 있는 우수한 특성을 가지고 있다.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.606-616
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• 2022

Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.