• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.9
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• pp.873-877
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• 2013

The application of inkjet technology has been broadening from home printers to manufacturing tools. Recently, there have been demands for high-resolution printing, especially in the field of printed electronics applications. To improve upon the conventional inkjet printing patterning method, electrohydrodynamic (EHD) inkjet technology has recently attracted attention because droplets smaller than the nozzle diameter can be ejected and materials with wider viscosity range can be used for jetting. In this study, an EHD jet printing system for fine patterning is presented. To print various patterns based on drop on demand printing, vector and raster printing algorithm are implanted in the printing software. Fine conductive patterns with line width of less than $7{\mu}m$ can be easily achieved via EHD jet using a nozzle with inner diameter of $8{\mu}m$.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.199-204
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• 2002

Since motion estimation and motion compensation methods remove the redundant data to employ the temporal redundancy in images, it plays an important role in digital video compression. Because of its high computational complexity, however, it is difficult to apply to high-resolution applications in real time environments. If we have information about the motion of an image block before the motion estimation, the location of a better starting point for the search of an exact motion vector can be determined to expedite the searching process. In this paper, we present an adaptive motion estimation approach bated on temporal correlations of consecutive image frames that defines the search pattern and determines the location of the initial search point adaptively. Through experiments, compared with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(dB) better than DS in terms of PSNR(Peak Signal to Noise Ratio) and improves as high as 50% compared with DS in terms of average number of search point per motion vector estimation.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.

• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.849-862
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• 2017

The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution in ungauged areas.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.587-599
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• 2011

As one measure of image interpretability, NIIRS(National Imagery Interpretability Rating Scale) has been used. Unlike MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), and GSD(Ground Sampling Distance), NIIRS can describe the quality of overall image at user's perspective. NIIRS is observed with human observation directly or estimated by edge analysis. For edge analysis specially manufactured artificial target is used commonly. This target, formed with a tarp of black and white patterns, is deployed on the ground and imaged by the satellite. Due to this, the artificial target-based method needs a big expense and can not be performed often. In this paper, we propose a new edge analysis method that enables to estimate NIIRS accurately. In this method, natural targets available in the image are used and characteristics of the target are considered. For assessment of the algorithm, various experiments were carried out. The results showed that our algorithm can be used as an alternative to the artificial target-based method.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.93-93
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• 2011

전 세계적으로 기후변화로 인한 기상재해의 피해가 매년 증가하고 있으며, 기후변화로 인한 시민들의 안전, 재산, 인명피해 또한 늘어나고 있다. 이러한 피해를 최소화하기 위해서는 도시지역을 중심으로 한 신뢰성 높은 미래 기후전망 기법이 필수적이며, 미래 기후전망을 바탕으로 하여 기후변화로 인한 향후 발생할 수 있는 위험성의 정도를 전망하여 적응대책을 수립할 필요가 있다. 본 연구에서는 도시지역의 미래 기후전망 기법을 개발하여 서울시의 미래기후를 전망한다. 본 연구를 수행하기 위하여 먼저 IPCC 기후시나리오에 대한 조사를 수행하여 자료를 수집한다. 수집한 자료를 바탕으로 역학적 상세화와 통계적 상세화 기법을 이용하여 고해상도 기후 시나리오를 생산하였다. 역학적 상세화 기법은 A2시나리오의 ECHO-G/S에서 생산된 기후 시나리오를 이용하여 지역 기후모델인 RegCM3에 적용하여 상세화 과정을 수행하였다. RegCM3를 이용하여 60km로 상세화한 후에 one-way double-nested system을 구축하여 20km까지 상세화 하였다. 20km 해상도의 기후 시나리오는 서울시와 같은 좁은 지역의 기후를 분석하기에는 어려움이 있으므로, RegCM3에 사용할 수 있는 Sub-BATS라는 기법을 이용하여 5km의 고해상도 기후 시나리오를 생산하였다. 역학적 상세화 결과는 관측결과에 비해 과소 추정되는 경향이 있어, 편차보정을 통하여 관측값에 가까운 자료를 만들어 주었다. 역학적 상세화 결과를 분석한 결과, 기준기간에 비해 미래기간(S3)에는 전체적으로 약 4.9도의 기온상승과 강수량 증가가 나타났으며, 특히 9월에 가장 큰 상승폭을 나타내고 있었다. 강수량의 경우 증가 경향이 뚜렷이 나타나고 있었으며, 여름철에 큰 증가폭을 나타내고 있었다. 통계적 상세화 기법은 역학적 상세화 기법에서 사용된 ECHO-G/S를 포함한 13개의 GCM결과와 우리나라의 57개 지점에 대한 CSEOF기법을 이용하여 기후 시나리오를 생산하였다. 이 자료는 서울시에 대하여 하나의 지점밖에 존재하지 않아, 서울시내의 지역별 미래 기후전망에는 문제가 있었으므로, Delta method라는 기법을 이용하여 서울 및 인근지역의 AWS 35개 지점에 대하여 미래 기후시나리오를 생산하였다. 통계적 상세화 결과, 13개 GCM의 기온변화는 전체평균 약 3.1도 상승하였고, 겨울과 여름철의 변화폭이 가장 크며, 모델의 불확실성 또한 겨울과 여름에 가장 큰 특징을 가지고 있다. 강수량의 경우 MME에서는 약간의 상승은 나타나고 있었지만 모델간의 불확실성은 여름철에 크게 나타나고 있었다. 역학적 및 통계적 상세화 기후 시나리오(ECHO-G/S, A2)를 비교 분석한 결과, 기온은 역학적 상세화 결과가 약간 크게 나타났으며, 전체적으로 유사한 패턴을 보이고 있었다. 강수량 또한 역학적 상세화 결과가 크게 나타나고 있었다. 역학적 및 통계적 상세화 결과는 S1의 경우 유사한 특징을 보이고 있었지만 S3로 갈수록 차이가 크게 나타나고 있었다.

• Spatial Information Research
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• v.11 no.2
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• pp.101-117
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• 2003

High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.181-189
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• 2012

Recently, interest in potential for estimating water quality using high resolution satellite images is increasing. However, low SNR(Signal to Noise Ratio) over inland water and radiometric errors such as non-linearity of brightness value of high resolution satellite images often lead to accuracy degradation in water quality estimation. Therefore radiometric correction should be carried out to estimate water quality for high resolution satellite images. For KOMPSAT-2 images parameters for brightness value-radiance conversion are not available and precise radiometric correction is difficult. To exploit KOMPSAT-2 images for water quality monitoring, it is necessary to investigate non-linearity of brightness value and noise over inland water. In this paper, we performed brightness value comparison between KOMPSAT-2 images and IKONOS/GeoEye-1, which are known to show the linearity. We used the images obtained over the same area and on the same date for comparison. As a result, we showed that although KOMPSAT-2 images are more noisy;the trend of brightness value and pattern of noise are almost similar to reference images. The results showed that appropriate target area to minimize the impact of noise was $5{\times}5$. Non-linearity of brightness value between KOMPSAT-2 and reference images was not observed. Therefore we could conclude that KOMPSAT-2 may be used for estimation of water quality parameters such as concentration of chlorophyll.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.523-533
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• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.446-454
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• 2023

The impact of climate change on agriculture is substantial, especially as global warming is projected to lead to varying temperature and humidity patterns in the future. These changes pose a higher risk for both crops and livestock, exposing them to environmental stressors under altered climatic conditions. Specifically, as temperatures are expected to rise, the risk of heat stress is assessable through the Temperature-Humidity Index (THI), derived from temperature and relative humidity data. This study involved the comparison of THI collected from 10 Korea Meteorological Administration ASOS stations spanning a 60-year period from 1961 to 2020. Moreover, high-resolution temperature and humidity distribution data from 1981 to 2020 were employed to generate high-resolution TH I distributions, analyzing temporal changes. Additionally, the number of days characterized by heat stress, derived from TH I, was compared over different time periods. Generally, TH I showed an upward trend over the past, albeit with varying rates across different locations. As TH I increased, the frequency of heat stress days also rose, indicating potential future cost increases in the livestock industry due to heat-related challenges. The findings emphasize the feasibility of evaluating heat stress risk in livestock using THI and underscore the need for research analyzing THI under future climate change scenarios.