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RISS 인기검색어
- 검색키워드
- 저자 : 권방현 (검색결과 3 건)
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비전을 이용한 이동로봇의 자가측위와 VRML과의 영상 오버레이
Inaccurate localization exposes a robot to many dangerous conditions. It could make a robot be moved to wrong direction or damaged by collision with surrounding obstacles. There are numerous approaches to self-positioning, and there are different modalities as well (vision, laser range finders, ultrasonic sonars). Since sensor information is generally uncertain and contains noise, there are many researches to reduce the noise. But, the correctness is limited because most researches are based on statistical approach. The goal of our research is to measure more exact robot location by matching between built 3D model and real vision image. To determine the position of mobile robot, landmark-localization technique is applied. Landmarks are any detectable structure in the physical environment. Some use vertical lines, others use specially designed markers, e.g., crosses or patterns of concentric circles. In this paper, specially designed markers are used as landmarks. Given known focal length and a single image of three landmarks it is possible to compute the angular separation between the lines of sight of the landmarks. If the world positions of the landmarks are known the angular separations can be used to compute the robot position and heading relative to a 2D floor map. The robot, That is, identifies landmarks in the environment and carries out the self-positioning. The image-processing and neural network pattern matching techniques are employed to recognize landmarks placed in a robot working environment. After self-positioning, the 2D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning and shows the result of overlapping between the 2D scene and VRML scene. In addition we describe the advantage expected from overlapping both scenes.
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Technology Valuation : Technology Value Attractiveness Model
권방현 Graduate School Korea University 2001 국내박사
Increasing number of transactions and investments in technology has sparked a growing interest in technology valuation. However, it has not been easy to come up with an objective valuation of technology due to variance in technology value, purpose of valuation, and technology patterns. The main objective of this study lies in the development of a new approach for technology valuation. The details of the objective are; establishment of classification scheme for technology valuation approach, development of the Technology Value Attractiveness Model (TVAM), experiment of the TVAM using an empirical study, comparative study between industries using the Technology Value Attractiveness (TVA), and embodiment of the technology valuation system. In this study, the potential value is defined as the technology value. The potential value represents the market appraised expected profit, which is subject to the technology competitiveness and the market growth. It is assumed that the potential value of the technology is calculated in the market under the Price-to-Book Value (PBR), which is the rate between the stock price an d the net assets value. TVA is measured as the index to forecast the PBR, which is the proxy variable for the potential value of the technology. TVA is composed of the Intrinsic Value of Technology(IVT), which reflects technology competitiveness, and the Extrinsic Value of Technology(EVT), which represent technology profit ability. IVT is measured with Technology Competence (TC) and Technology Substantiality (TS), which are the variables that express technology competitiveness in the present and the future. EVT is measured with Profit Size (PS) and Profit Growth rate (PG), which are the variables that express the technology profitability from market in the present and the future. This study experiments the feasibility of the TVAM by conducting an empirical study on small & medium sized manufacturing companies registered on KOSDAQ. They are classified under information technology, chemistry, metal, electricity, and machinery industries. The results identify the feasibility of the TVAM through a high correlation between the TVA and the PBR. Comparative study between industries demonstrates the IT industry of having the highest technology value followed by chemistry, metal, electricity, and machinery industries, respectively. The study introduces four strategic patterns to improve the technology value with strategy matrix of technology value; differentiation strategy, leader strategy, exploitation strategy, and follower strategy. Each strategic pattern offers a technology management policy to improve or maintain the technology value. Web-based Interactive Technology Valuation(WITV) system is also developed to support the technology valuation. In the WITV system, the technology valuation is implemented with the TVAM and the analyzed data of the five industries. This system emphasizes end-user friendly interface in particular and improves its utility with what-if analysis module.
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