• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Wind and Structures
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• v.14 no.3
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• pp.221-238
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• 2011

The estimated response of large-scale engineering structures to severe wind loads is prone to modelling uncertainties that can only ultimately be assessed by full-scale testing. To this end ambient vibration data from full-scale monitoring of the historic Clifton Suspension Bridge has been analysed using a combination of a frequency domain system identification method and a more elaborate stochastic identification technique. There is evidence of incipient coupling action between the first vertical and torsional modes in strong winds, providing unique full-scale data and making this an interesting case study. Flutter derivative estimation, which has rarely previously been attempted on full-scale data, was performed to provide deeper insight into the bridge aerodynamic behaviour, identifying trends towards flutter at higher wind speeds. It is shown that, as for other early suspension bridges with bluff cross-sections, single-degree-of-freedom flutter could potentially occur at wind speeds somewhat below requirements for modern designs. The analysis also demonstrates the viability of system identification techniques for extracting valuable results from full-scale data.

• Journal of The Korean Astronomical Society
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• v.46 no.1
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• pp.33-40
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• 2013

We investigate the dependence of the extended X-ray emission from the halos of optically luminous early-type galaxies on the small-scale (the nearest neighbor distance) and large-scale (the average density inside the 20 nearest galaxies) environments. We cross-match the 3rd Data Release of the Second XMMNewton Serendipitous Source Catalog (2XMMi-DR3) to a volume-limited sample of the Sloan Digital Sky Survey (SDSS) Data Release 7 with $M_r$ < -19.5 and 0.020 < z < 0.085, and find 20 early-type galaxies that have extended X-ray detections. The X-ray luminosity of the galaxies is found to have a tighter correlation with the optical and near infrared luminosities when the galaxy is situated in the low large-scale density region than in the high large-scale density region. Furthermore, the X-ray to optical (r-band) luminosity ratio, $L_X/L_r$, shows a clear correlation with the distance to the nearest neighbor and with large-scale density environment only where the galaxies in pair interact hydrodynamically with seperations of $r_p$ < $r_{vir}$. These findings indicate that the galaxies in the high local density region have other mechanisms that are responsible for their halo X-ray luminosities than the current presence of a close encounter, or alternatively, in the high local density region the cooling time of the heated gas halo is longer than the typical time between the subsequent encounters.

• Ocean Systems Engineering
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• v.4 no.4
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• pp.309-325
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• 2014

Geotextile tubes are basically a huge sack filled with sand or dredged soil. Geotextile tubes are made of permeable woven or non-woven synthetic fibers (i.e., polyester or PET and polypropylene or PP). The geotextile tubes' performances in strength, dewatering, retaining solid particles and stacked stability have been studied extensively in the past. However, only little research has been done in the observation of the deformation behavior of geotextile tubes. In this paper, a large-scale apparatus for geotextile tube experiment is introduced. The apparatus is equipped with a slurry mixing station, pumping and delivery station, an observation station and a data station. For this study the large-scale apparatus was utilized in the studies regarding the stresses on the geotextile and the deformation behavior of the geotextile tube. Model tests were conducted using a custom-made woven geotextile tubes. Load cells placed at the inner belly of the geotextile tube to monitor the total soil pressure. Strain gauges were also placed on the outer skin of the tube to measure the geotextile strain. The pressure and strain sensors are attached to a data logger that sends the collected data to a desktop computer. The experiment results showed that the maximum geotextile strain occurs at the sides of the tube and the soil pressure distribution varies at each geotextile tube section.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• Journal of KIISE
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• v.41 no.12
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• pp.1126-1135
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• 2014

In recent years, RDF partitioning schemes have been studied for the effective distributed storage and management of large-scale RDF data. In this paper, we propose an RDF dynamic partitioning scheme to support load balancing in dynamic environments where the RDF data is continuously inserted and updated. The proposed scheme creates clusters and sub-clusters according to the frequency of the RDF data used by queries to set graph partitioning criteria. We partition the created clusters and sub-clusters by considering the workloads and data sizes for the servers. Therefore, we resolve the data concentration of a specific server, resulting from the continuous insertion and update of the RDF data, in such a way that the load is distributed among servers in dynamic environments. It is shown through performance evaluation that the proposed scheme significantly improves the query processing time over the existing scheme.

• International Journal of Contents
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• v.16 no.4
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• pp.16-25
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• 2020

The size of observation data in astronomy has been increasing exponentially with the advents of wide-field optical telescopes. This means the needs of changes to the way used for large-scale astronomy data analysis. The complexity of analysis tools and the lack of extensibility of computing environments, however, lead to the difficulty and inefficiency of dealing with the huge observation data. To address this problem, this paper proposes a workflow execution system for analyzing large-scale astronomy data efficiently. The proposed system is composed of two parts: 1) a workflow execution manager and its RESTful endpoints that can automate and control data analysis tasks based on workflow templates and 2) an elastic resource manager as an underlying mechanism that can dynamically add/remove virtualized computing resources (i.e., virtual machines) according to the analysis requests. To realize our workflow execution system, we implement it on a testbed using OpenStack IaaS (Infrastructure as a Service) toolkit and HTCondor workload manager. We also exhaustively perform a broad range of experiments with different resource allocation patterns, system loads, etc. to show the effectiveness of the proposed system. The results show that the resource allocation mechanism works properly according to the number of queued and running tasks, resulting in improving resource utilization, and the workflow execution manager can handle more than 1,000 concurrent requests within a second with reasonable average response times. We finally describe a case study of data reduction system as an example application of our workflow execution system.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.23-24
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• 1996

We reexamined CDM texture large-scale structure (LSS) formation model. We confirmed that texture model is consistent with 4-year COBE data both in an open and a critical matter density (${\Omega}_0$ = 1) universes, and then obtained normalization for density perturbation power spectrum. We next compare the power spectrum with LSS observation data. Contrary to the previous literature, we found that texture model matches with these data in an open universe no better than in an ${\Omega}_0$ = 1 universe. We also found that the model is more likely to fit these data in a cosmological constant dominated ($\Lambda-$) universe.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1374-1376
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• 2003

NASA, NSF and USGS jointly conducted a LIDAR survey over several sites in the Antarctic Dry Valleys and its vicinity, acquiring numerous surface points by NASA's Airborne Topographic Mapper (ATM) conical laser scanning altimetry system. The data set have high blunder ratio, and the conical scanning pattern resulted large variation of the point densities. Hence, to reduce the undesirable effects due to these characteristics and process the huge number of points with reasonable time and resources, we developed a novel approach to generate large-scale and high-resolution DEMs in robust, efficient and nearly automatic manners. Based on this approach we produced DEMs and then verified them with reference data.