• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.8-15
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• 2011

In this paper, we propose a new structure modeling algorithm from 3D cloud points of terrestrial LADAR data. Terrestrial LIDAR data have various obstacles which make it difficult to apply conventional algorithms designed for air-borne LIDAR data. In the proposed algorithm, the field data are separated into several clusters by adopting the structure extraction method which uses color information and Hough transform. And cluster based Delaunay triangulation technique is sequentially applied to model the artificial structure. Each cluster has its own priority and it makes possible to determine whether a cluster needs to be considered not. The proposed algorithm not only minimizes the effects of noise data but also interactively controls the level of modeling by using cluster-based approach.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.12
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• pp.68-76
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• 2015

We examine the standalone lightning warning system (LWS) and its warning performances for three years. This system acquires and analyzes the data of cloud-to-ground strike (CG), intra-cloud discharge (IC) and electrostatic field (EF) to produce prior warnings with respect to the impending arrival of CG in the area of concern (AOC). The warnings in this system are carried out based on the fixed two areas method. To evaluate warning performances, we analyzed the statistics of warnings with probability of detection (POD) and false alarm ratio (FAR). Based on the previous study, we revised the trigger and clear conditions of lightning warning for improving the performances of the system. As a result of this revision, POD increased from 0.18 to 0.44 and FAR decreased from 0.96 to 0.78 during the summer of 2014. However, the LWS was not possible to trigger effective alerts (EA) because there was no effective lead time (LT) for the fixed two areas method. Problems related to the low detection efficiency of IC and the use of EF data for warnings still decreased POD and increased FAR. Hence, we proposed the development method of a new LWS (NLWS) that would be composed of integrated weather data, the flexible two areas and the user software in order to trigger EA and improve warning performances.

• Journal of the Korean Society of Costume
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• v.59 no.8
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• pp.132-151
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• 2009

All of 132 kinds of fabrics are used in excavated costume from the Dongrae Jung's Tomb. Classified by its materials, it is divided into small groups as follows: 58 pieces of silk tabby 43.9%, 2 of filament silk tabby 1.5%, 14 of thin filament silk tabby 10.6%, 19 of spun yarn silk 14.4%, 4 of twill without a pattern 3.0%, 8 of patterned silk tabby 6.1%, 23 of satin damask 17.4%, 1 of damask with supplementary gold thread 0.8%, 2 of mixture fabric with silk and cotton 1.5%, and 1 of ramie fabric 0.8%. Classified by ways of weaving: 96 pieces of plain weave 73%, 23 of satin weave 17%, 8 of patterned silk tabby consisting of plain weave material and twill weave pattern-6%, 4 of twill weave 3%, and 1 of compound weave 1 %. In point of patterns, the most often used ones are plant patterns such as lotus patterns, peony patterns, plum blossom patterns, flowers representing seasons patterns, and small flower patterns. For animal patterns, it has phoenix patterns designed together with flowers representing seasons patterns. And for natural scenery patterns, it shows cloud patterns with treasures patterns together. For object patterns, it also shows treasures patterns mixed with cloud or lotus together. For geometrical patterns, it has rhomboid patterns and 卍 character patterns; some show only rhomboid patterns and others show material patterns of 卍 character patterns blended together with fruit, lotus, etc.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.261-267
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• 2007

In this study, the Slab Ocean Model (SOM) is coupled with an Atmospheric General Circulation Model (AGCM) which developed in University of Kangnung based on the land surface model of Biosphere-Atmosphere Transfer Scheme (BATS). The purposes of this study are to understand radiative role of clouds considering of the atmospheric feedback, and to compare the Clouds Radiative Forcing (CRF) come from the analyses using the clear-cloud sky method and CGCM. The new CGCM was integrated by using two sets of the clouds with radiative role (EXP-A) and without radiative role (EXP-B). Clouds in this two cases show the negative effect $-26.0\;Wm^{-2}$ of difference of radiation budget at top of atmosphere (TOA). The annual global means radiation budget of this simulation at TOA is larger than the estimations ($-17.0 Wm^{-2}$) came from Earth Radiation Budget Experiment (ERBE). The work showed the surface negative effect with $-18.6 Wm^{-2}$ in the two different simulations of CRF. Otherwise, sensible heat flux in the simulation shows a great contribution with positive forcing of $+24.4 Wm^{-2}$. It is found that cooling effect to the surface temperature due to radiative role of clouds is about $7.5^{\circ}C$. From this study it could make an accurate of the different CRF estimation considering either feedback of EXP-B or not EXP-A under clear-sky and cloud-sky conditions respectively at TOA. This result clearly shows its difference of CRF $-11.1 Wm^{-2}$.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.193-203
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• 2009

Snow cover is one of the important parameters since it determines surface energy balance and its variation. To classify snow and cloud from satellite data is very important process when inferring land surface information. Generally, misclassified cloud and snow pixel can lead directly to error factor for retrieval of surface products from satellite data. Therefore, in this study, we perform algorithm for detecting snow cover area with remote sensing data. We just utilize visible reflectance, and infrared channels rather than using NDSI (Normalized Difference Snow Index) which is one of optimized methods to detect snow cover. Because COMS MI (Meteorological Imager) channels doesn't include near infra-red, which is used to produce NDSI. Detecting snow cover with visible channel is well performed over clear sky area, but it is difficult to discriminate snow cover from mixed cloudy pixels. To improve those detecting abilities, brightness temperature difference (BTD) between 11 and 3.7 is used for snow detection. BTD method shows improved results than using only visible channel.

• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.111-125
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• 1996

A forecast technique using GMS-4(Geostationary Meteorological Satellite) infrared images and its $T_{BB}$ (Brightness Temperature) data to determine the tropical cyclone center and to analyze the tropical cyclone intensity has been developed. First, the determination of typhoon center using $T_{BB}$ distribution pattern is practiced by understanding a special feature of central cloud pattern and cloud band which is analyzed with the method of pseudo coloring. Then, to forecast the intensity of tropical cyclone, a relationship between the central pressure (or maximum wind speed) of tropical cyclone and $T_{BB}$ measured by GMS near the tropical cyclone center was investigated. The results showed a correlation with a high lag relationship between central pressures and $T_{BB}$. The mean Tee in the ring of 200~300km apart from the tropical cyclone center showed the best correlation to central pressure of the tropical cyclone after 24hour. From this relationship, a regression equation to forecast the central pressure (or maximum wind speed) was derived.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.469-475
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• 2021

In order to effectively maintain and manage river facilities, on going data collection of associated objects is important. However, the existing data acquisition methods of using a total station, a global navigation satellite system, or a terrestrial laser scanner have limitations in terms of cost/time/manpower when acquiring spatial information data on river facilities distributed over a wide and long area, unlike general facilities. In contrast, a mobile mapping system (MMS), which acquires data while moving its platform, acquires precise spatial information data for a large area in a short time, so it is suitable for use in the maintenance of linear facilities around rivers. As a result of applying a MMS to a research area of 4 km, 184,646,099 points were acquired during a 20-minute data acquisition period, and 378 cross-sections were extracted. By comparing this with computer-drawn river plans, it was confirmed that efficient levee management using a MMS is possible.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.652-658
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• 2021

When acquiring a point cloud using a 3D scanner, a registration process of making the acquired data based on each local coordinate into one data with a unified world coordinate system is required. Its process is difficult to obtain a satisfactory result with only one execution, and it is repeated several times to increase the registration precision. The criterion for determining the registration accuracy is an important factor. The previous methods for determining the accuracy of registration have a limitation in that the judgment may be ambiguous in some cases, and different results may be produced each time depending on the characteristics of the point cloud. Therefore, to calculate the accuracy of registration more precisely, I propose a method using the average distance value of the point group for the entire points rather than the corresponding points used in the registration. When this method is used, it is possible to determine the registration accuracy more reliably than the conventional methods.