• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.36-51
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• 2008

As the global warming has influenced on various sectors including agriculture, forestry, fisheries and health, it is essential to project more accurate future climate for an assessment of climate change impact and adaptation strategy. This study examines spatial distribution of onset dates and durations of season decomposed by applying a lowpass filtering using observed 30-year (1971-2000) data and projected 2090s data based on the IPCC SRES A1B emission scenario in South Korea. In general, the distributions of spring and winter onset date are affected by latitudes, topography and proximity to oceans. However, onset dates of summer and autumn are a little affected by proximity to oceans and topography than by latitudes. In the 2090s (2091-2100), the onset dates of spring begin about 40 days earlier and the onset dates of summer begin 25-30 days earlier as compare with present time. On the other hand, the onset dates of winter begin about 50 days later in the southern and eastern coastal area and in the southern inland. The onset dates of autumn begin about 20 days later. In the 2090s, summer duration is longer and winter duration is shorter as compare with present time at southern and eastern coastal area.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.