• Proceedings of the KSRS Conference
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• v.1
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• pp.150-152
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• 2006

The DISCOVER Project (${\underline{D}}istributed$ ${\underline{I}}nformation$ ${\underline{S}}ervices$ for ${\underline{C}}limate$ and ${\underline{O}}cean$ products and ${\underline{V}}isualizations$ for ${\underline{E}}arth$ ${\underline{R}}esearch$) is a NASA funded Earth Science REASoN project that strives to provide highly accurate, carefully calibrated, long-term climate data records and near-real-time ocean products suitable for the most demanding Earth research applications via easy-to-use display and data access tools. A key element of DISCOVER is the merging of data from the multiple sensors on multiple platforms into geophysical data sets consistent in both time and space. The project is a follow-on to the SSM/I Pathfinder and Passive Microwave ESIP projects which pioneered the simultaneous retrieval of sea surface temperature, surface wind speed, columnar water vapor, cloud liquid water content, and rain rate from SSM/I and TMI observations. The ocean products available through DISCOVER are derived from multi-sensor observations combined into daily products and a consistent multi-decadal climate time series. The DISCOVER team has a strong track record in identifying and removing unexpected sources of systematic error in radiometric measurements, including misspecification of SSM/I pointing geometry, the slightly emissive TMI antenna, and problems with the hot calibration source on AMSR-E. This in-depth experience with inter-calibration is absolutely essential for achieving our objective of merging multi-sensor observations into consistent data sets. Extreme care in satellite inter-calibration and commonality of geophysical algorithms is applied to all sensors. This presentation will introduce the DISCOVER products currently available from the web site, http://www.discover-earth.org and provide examples of the scientific application of both the diurnally corrected optimally interpolated global sea surface temperature product and the 4x-daily global microwave water vapor product.

• Journal of Astronomy and Space Sciences
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• v.27 no.3
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• pp.231-238
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• 2010

We analyzed global positioning system (GPS)-derived precipitable water vapor (PWV) trends of the Korea Astronomy and Space Science Institute 5 stations (Seoul, Daejeon, Mokpo, Milyang, Sokcho) where Korea Meteorological Administration meteorological data can be obtained at the same place. In the least squares analysis, the GPS PWV time series showed consistent positive trends (0.11 mm/year) over South Korea from 2000 to 2009. The annual increase of GPS PWV was comparable with the 0.17 mm/year and 0.02 mm/year from the National Center for Atmospheric Research Earth Observing Laboratory and Atmospheric InfraRed Sounder, respectively. For seasonal analysis, the increasing tendency was found by 0.05 mm/year, 0.16 mm/year, 0.04 mm/year in spring (March-May), summer (June-August) and winter (December-February), respectively. However, a negative trend (-0.14 mm/year) was seen in autumn (September-November). We examined the relationship between GPS PWV and temperature which is the one of the climatic elements. Two elements trends increased during the same period and the correlation coefficient was about 0.8. Also, we found the temperature rise has increased more GPS PWV and observed a stronger positive trend in summer than in winter. This is characterized by hot humid summer and cold dry winter of Korea climate and depending on the amount of water vapor the air contains at a certain temperature. In addition, it is assumed that GPS PWV positive trend is caused by increasing amount of saturated water vapor due to temperature rise in the Korean Peninsula. In the future, we plan to verify GPS PWV effectiveness as a tool to monitor changes in precipitable water through cause analysis of seasonal trends and indepth/long-term comparative analysis between GPS PWV and other climatic elements.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.46.1-46.1
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• 2018

The Fast Imaging Solar Spectrograph (FISS) on the Goode Solar Telescope (GST) at Big Bear Solar Observatory is the imaging Echelle spectrograph developed by the Solar Astronomy Group of Seoul National University and the Solar and Space Weather Group of Korea Astronomy and Space Science Institute. The instrument takes spectral data from a region on the Sun in two spectral bands simultaneously. The imaging is done by the organization of intensity data obtained from the fast raster scan of the slit over the field of view. Since the scan repeats many times, the whole set of data can be used to construct the movies of monochromatic intensity at arbitrary wavelengths within the spectral bands, and those of line-of-sight velocity inferred from different spectral lines. So far there are two standard observing configurations: one recording the $H{\alpha}$ line and the Ca II 8542 line simultaneously, and the other recording the Na I D2 line and Fe I 5435 line simultaneously. We have developed the procedures to produce the standard data for each observing configuration. The procedures include the spatial alignment, the correction of spectral shift of instrumental origin, and the lambdameter measurement of the line wavelength. The standard data include the movie of continuum intensity, the movies of intensity and velocity inferred from a chromospheric spectral line, the movies of intensity and velocity inferred from a photospheric line. The processed standard data will be freely available online (fiss.snu.ac.kr) to be used for research and public outreach. Moreover, the IDL procedures will be provided on request as well so that each researcher can adapt the programs for their own research.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.559-575
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• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.79-91
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• 2013

This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.

• Wind and Structures
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• v.26 no.1
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• pp.11-24
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• 2018

Full-scale measurements of wind action on the open roof structure of the WuXi grand theater, which is composed of eight large-span free-form leaf-shaped space trusses with the largest span of 76.79 m, were conducted during the passage of Typhoons HaiKui and SuLi. The wind pressure field data were continuously and simultaneously monitored using a wind pressure monitoring system installed on the roof structure during the typhoons. A detailed analysis of the field data was performed to investigate the characteristics of the fluctuating wind pressure on the open roof, such as the wind pressure spectrum, spatial correlation coefficients, peak wind pressures and non-Gaussian wind pressure characteristics, under typhoon conditions. Three classical methods were used to calculate the peak factors of the wind pressure on the open roof, and the suggested design method and peak factors were given. The non-Gaussianity of the wind pressure was discussed in terms of the third and fourth statistical moments of the measured wind pressure, and the corresponding indication of the non-Gaussianity on the open roof was proposed. The result shows that there were large pulses in the time-histories of the measured wind pressure on Roof A2 in the field. The spatial correlation of the wind pressures on roof A2 between the upper surface and lower surface is very weak. When the skewness is larger than 0.3 and the kurtosis is larger than 3.7, the wind pressure time series on roof A2 can be taken as a non-Gaussian distribution, and the other series can be taken as a Gaussian distribution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.94-106
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• 2018

The epidemiological model is useful for creating simulation and associated preventive measures for disease spread, and provides a detailed understanding of the spread of disease space through contact with individuals. In this study, propose an agent-based spatial model(ABM) integrated with spatial big data to simulate the spread of MERS-CoV infections in real time as a result of the interaction between individuals in space. The model described direct contact between individuals and hospitals, taking into account three factors : population, time, and space. The dynamic relationship of the population was based on the MERS-CoV case in Seoul Metropolitan Government in 2015. The model was used to predict the occurrence of MERS, compare the actual spread of MERS with the results of this model by time series, and verify the validity of the model by applying various scenarios. Testing various preventive measures using the measures proposed to select a quarantine strategy in the event of MERS-CoV outbreaks is expected to play an important role in controlling the spread of MERS-CoV.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.1-9
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• 2005

This study was performed to analyze time series landuse pattern of urban areas and the change of the areas by using remotely sensed multiple sensors. The results were as follows. First, according to the result of time series analysis, most agricultural land has been changed into built-up areas by development work such as the land development or land readjustment project, arrangement of science parks or military facilities, and location of public establishment like government buildings. Second, if the expansion of built-up areas maintains the present scale and speed, it seems that a lot of parts of land would be changed into built-up areas, especially centering around agricultural land, so it is necessary to establish the plan for urban space. Third, I have synthetically collected the data of the project of urban development and systematically monitored the process of in expansion the built-up areas up to now (from the past). I hereby could lay the foundation that makes us scientifically forecast the direction of expansion in the built-up areas by the urban development in the future.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.513-524
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• 2006

Normalization transform is very useful for finding the overall trend of the time-series data since it enables finding sequences with similar fluctuation patterns. The previous subsequence matching method with normalization transform, however, would incur index overhead both in storage space and in update maintenance since it should build multiple indexes for supporting arbitrary length of query sequences. To solve this problem, we propose a single index approach for the normalization transformed subsequence matching that supports arbitrary length of query sequences. For the single index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. The inclusion-normalization transform normalizes a window by using the mean and the standard deviation of a subsequence that includes the window. Next, we formally prove correctness of the proposed method that uses the inclusion-normalization transform for the normalization transformed subsequence matching. We then propose subsequence matching and index building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to $2.5{\sim}2.8$ times over the previous method. Our approach has an additional advantage of being generalized to support many sorts of other transforms as well as normalization transform. Therefore, we believe our work will be widely used in many sorts of transform-based subsequence matching methods.