• 한국공간정보시스템학회:학술대회논문집
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• 2000.06a
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• pp.67-76
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• 2000

Recently, SAR Interferometry method is actively being studied as a new technic in topographic mapping using satellite imageries. it extract height values using two SAR imageries covering same areas. Unlike when using SPOT imageries, it isn't affected by atmospheric conditions and time. But it is difficult to process radar imageries and the height accuracy is very low where relief displacements are high. In this study, we produced DEM(Digital Elevation Model) using ERS-1, ERS-2 tandem data and analysed the height accuracy over 14 ground control points. The mean error in height was 14.06m. But when using airborne SAR data, it Is expected that we can produce more accurate DEM which will be able to ue used in updating 1/10,000 or 1/25,000 map.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.56-68
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• 2002

The Universal Variable Length Coding(UVLC) scheme in H.26L has nice features such as error resiliency and two-way decodability. However, it has lower coding efficiency than the conventional Huffman coding. To improve the coding efficiency of UVLC, we Propose to use a dynamic codeword mapping that changes association between symbols and codewords in order to utilize the statistical characteristics of symbols as much as possible but without losing any features of the UVLC. Both encoder and decoder use the same re-association table, and hence the encoder need not send additional overhead for the re-mapping relationship to the decoder. Simulation results show that without significant change of the current H.26L coding scheme, the proposed method additionally attains up to about 8% and about 5% bit reductions respectively in intra and inter frames over the current H.26L encoding method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.398-404
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• 2020

In this paper, we propose a new design method of signal constellation of the spiral quadrature amplitude modulation (QAM) exploiting a modified gradient descent search algorithm and its binary mapping rule. Unlike the conventional method, the new method, which uses and the constellation optimization algorithm and the maximum number of iterations as a parameter for the iterative design, is more robust to phase noise. And the proposed binary mapping rule significantly reduces the average Hamming distance of the spiral constellation. As a result, the proposed spiral QAM constellation has much improved error performance compared to the conventional ones even in a very severe phase noise environment. It is, therefore, considered that the proposed QAM may be a useful modulation format for coherent optical communication systems and orthogonal frequency division multiplexing (OFDM) systems.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.65-73
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• 2008

The characterization process for the accurate color reproduction in mobile phone with camera and LCD is popular. The camera and LCD characterization, gamut mapping process is necessary to map the camera's input color stimulus, CIEXYZ value, into the LCD's output color stimulus. Each characterization is the process estimating the relation between input and output signals. In case of LCD, because of output device, the output color stimulus for the arbitrary input signal can be measured by spectro-radiometer However, in the camera, as the input device, the characterization is an inaccurate and needs the manual works in the process obtaining the output signal because the input signal can not be generated. Moreover, after gamut mapping process, the noise is increased because the optimized gamma tone curve of camera for the noise is distorted by the characterization. Thus, this paper proposed the system of obtaining the output signal of camera and the method of gamma correction for the noise. The camera's output signal is obtained by RGB values of patches from captured the color chart image. However, besides the illumination, the error for the location of the chart in the viewfinder is generated when many camera modules are captured the chart. The method of correcting the position to correct the error from manual works. The position of camera is estimated by captured image. This process and moving of camera is accomplished repeatedly, and the optimized position can be obtained. Moreover, the lightness curve of camera output is corrected partly to reduce the noise from the characterization process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.937-945
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• 2013

The accuracy of flood inundation maps is determined by the uncertainty propagated from all variables involved in the overall process including input data, model parameters and modeling approaches. This study investigated the uncertainty arising from key variables (flow condition and Manning's n) among model variables in flood inundation mapping for the Missouri River near Boonville, Missouri, USA. Methodology of this study involves the generalized likelihood uncertainty estimation (GLUE) to quantify the uncertainty bounds of flood inundation area. Uncertainty bounds in the GLUE procedure are evaluated by selecting two likelihood functions, which is two statistic (inverse of sum of squared error (1/SAE) and inverse of sum of absolute error (1/SSE)) based on an observed water surface elevation and simulated water surface elevations. The results from GLUE show that likelihood measure based on 1/SSE is more sensitive on observation than likelihood measure based on 1/SAE, and that the uncertainty propagated from two variables produces an uncertainty bound of about 2% in the inundation area compared to observed inundation. Based on the results obtained form this study, it is expected that this study will be useful to identify the characteristic of flood.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.143-154
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• 2012

As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Ni$\tilde{n}$o and La Ni$\tilde{n}$a). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.589-599
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• 2018

The land cover map has been produced by using satellite and aerial images. However, these two images have the limitations in spatial resolution, and it is difficult to acquire images of a area at desired time because of the influence of clouds. In addition, it is costly and time-consuming that mapping land cover map of a small area used by satellite and aerial images. This study used multispectral camera-based drone to acquire multi-temporal images for orthoimages generation. The efficiency of produced land cover map was evaluated using time series analysis. The results indicated that the proposed method can generated RGB orthoimage and multispectral orthoimage with RMSE (Root Mean Square Error) of ${\pm}10mm$, ${\pm}11mm$, ${\pm}26mm$ and ${\pm}28mm$, ${\pm}27mm$, ${\pm}47mm$ on X, Y, H respectively. The accuracy of the pixel-based and object-based land cover map was analyzed and the results showed that the accuracy and Kappa coefficient of object-based classification were higher than that of pixel-based classification, which were 93.75%, 92.42% on July, 92.50%, 91.20% on October, 92.92%, 91.77% on February, respectively. Moreover, the proposed method can accurately capture the quantitative area change of the object. In summary, the suggest study demonstrated the possibility and efficiency of using multispectral camera-based drone in production of land cover map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2293-2302
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• 2013

Flood inundation map has been used as a fundamental information in flood risk management. However, there are various sources of uncertainty in flood inundation mapping, which can be another risk in preventing damage from flood. Therefore, it is necessary to remove or reduce uncertainty sources to improve the accuracy of flood inundation maps. However, the entire removal of uncertainty source may be impossible and inefficient due to limitations of knowledge and finance. Sensitivity analysis of uncertainty sources allows an efficient flood risk management by considering various conditions in flood inundation mapping because an uncertainty source under different conditions may propagate in different ways. The objectives of this study are (1) to perform sensitivity analysis of uncertainty sources by different conditions on flood inundation map using the FOA method and (2) to find a major contributor to a propagated uncertainty in the flood inundation map in Flatrock at Columbus, U.S.A. Result of this study illustrates that an uncertainty in a variable is differently propagated to flood inundation map by combination with other uncertainty sources. Moreover, elevation error was found to be the most sensitive to uncertainty in the flood inundation map of the study reach.

• Journal of the Korean Chemical Society
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• v.55 no.3
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• pp.509-518
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• 2011

In this study, we investigated the effects of analogy-generating in small group in elementary science-gifted education upon the types and the mapping errors of student-generated analogies, and the perceptions of the instruction. Fifth graders (N=37) at two science-gifted classes in two elementary schools were selected and assigned to individualistic analogygenerating (IA, n=19) and pair analogy-generating (PA, n=18) groups. After the students of each group performed the experiment and were taught about 'saturated solution' concept in the first class, they administered the test on the self-generating analogies on the concept in the second class. The students in the PA group also administered the test on perceptions of analogy-generating in small group and some of them were interviewed deeply. The results revealed that the students in the PA group made more verbal/pictorial, structural/functional, enriched, and higher systematic analogies than those in the IA group. However, there were little difference between the two groups in the subcategories of artificiality (artificial and everyday) and abstraction (abstract and concrete). The students in the PA group fewer mapping errors than those in the IA group. Many students in PA group perceived the analogy-generating in small group positively upon various cognitive and motivational aspects. However, they also pointed a few disadvantages of the activity. Educational implications of these findings are discussed.