• Journal of The Korean Astronomical Society
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• v.29 no.2
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• pp.107-117
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• 1996

We have studied the star forming activities and dust properties of Lynds 1251, a dark cloud located at relatively high galactic latitude. Eleven IRAS point sources identified toward Lynds 1251 are discussed. Estimate of stellar masses, and far-infrared lumnosities of the young stars associated with two prominent IRAS point sources imply that these are T-Tauri stars with masses smaller than $0.3 M_\bigodot$. The low dust temperature of 27 K and low ratio of FIR emission to hydrogen column density are probably due to the lack of internal heating sources. Presumably two low mass young stars do not have enough energy to heat up the dust and gas associated. The dust heating is dominated by the interstellar heating source, and the weaker interstellar radiation field can explain the exceptionally low dust temperatures found in Lynds 1251. The estimated dust mass of Lynds 1251 is just $\~1M_\bigodot$, or about 1/1000 of gas mass, which implies that there must be a substantial amount of colder dust. The infrared flux at $100{\mu}m$ is matching well with $^{13}CO$ peak temperature, while the $^{12}CO$ integrated intensity is matching with the boundary of dust emission. Overall, the dust properties of Lynds 1251 is similar to those of normal dark clouds even though it does have star forming activities.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.345-346
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• 2023

As the shape and size of detectable objects diversifying recognition and segmentation algorithms have been developed to acquire accurate shape information. Although a high density of data captured by the repetition of scanning improves the accuracy of algorithms the high dense data decreases the efficiency due to its large size. This paper proposes standardization algorithms using the feature of structural members on indoor point cloud data to improve the process. First of all we determine the reduction rate of the density based on the features of the target objects then the data reduction algorithm compresses the data based on the reduction rate. Second the data arrangement algorithm rotates the data until the normal vector of data is aligned along the coordinate axis to allow the following algorithms to operate properly. Final the data arrangement algorithm separates the rotated data into their leaning axis. This allows reverse engineering of indoor point clouds to obtain the efficiency and accuracy of refinement processes.

• Clean Technology
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• v.13 no.1 s.36
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• pp.28-33
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• 2007

We measured cloud points of Poly(methylmethacrylate) (PMMA) in various solvents using the high-pressure variable volume view cell apparatus. The solvents used for dissolving PMMA were chlorodifluoromethane (HCFC-22), dimethylether (DME), 1,1,1-trifluoroethane (HFC-143a), 1,1-difluoroethane (HFC-152a) and 1,1,1,2-tetrafluoroethane (HFC-134a), and the effect of $CO_2$ concentration on the phase behavior of $PMMA+HCFC-22+CO_2$ system and $PMMA+DME+CO_2$ system was observed. PMMA was dissolved well in HCFC-22 from about 340 K, 5MPa and in DME from about 300 K, 28MPa. However, PMMA was not dissolved at all up to 423.15 K, 160MPa in the other fluorine compound such as HFC-l43a, HFC-152a and HFC-134a. PMMA+HCFC-22, $PMMA+HCFC-22+CO_2$ and PMMA+DME systems exhibit the lower critical solution temperature (LCST) behavior, however, $PMMA+DME+CO_2$ system exhibits the upper critical solution temperature (UCST) behavior. In the $CO_2$ mixture, the cloud point pressure of PMMA was increased dramatically proportional to the amount of $CO_2$ added, and from this result, it was known that $CO_2$ could be used as an antisolvent for fabricating PMMA nano-particles. And the cloud point of PMMA could be controlled by changing the concentration of $CO_2$.

• Journal of The Korean Astronomical Society
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• v.38 no.2
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• pp.33-41
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• 2005

We have conducted observations toward the molecular cloud associated with the H II region Sh 156 in $^{13}CO$(J = 1-0), $C^{18}O$(J = 1-0), and CS(J = 2 -1) using the TRAO 14 m telescope. Combining with existing $^{12}CO$(J = 1- 0) data of the Outer Galaxy Survey, we delineated the physical properties of the cloud. We found that there is a significant sign of interaction between the H II region and the molecular gas. We estimated the masses of the molecular cloud, using three different techniques; the most plausible mass is estimated to be $1.37 {\times} 10^5 M_{\bigodot}$, using a conversion factor of $X = 1.9 {\times} 10^{20}\;cm^{-2} (K\;km\;s^{-1})^{-1}$, and this is similar to virial mass estimate. This implies that the cloud is gravitationally bound and in virial equilibrium even though it is closely associated with the H II region. In addition to existing outflow, we found several MSX and IRAS point sources associated with dense core regions. Thus, more star forming activities other than the existing H II region are also going on in this region.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3090-3102
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• 2015

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4226-4241
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• 2014

Cloud storage provides an easy, cost-effective and reliable way of data management for users without the burden of local data storage and maintenance. Whereas, this new paradigm poses many challenges on integrity and privacy of users' data, since users losing grip on their data after outsourcing the data to the cloud server. In order to address these problems, recently, Worku et al. have proposed an efficient privacy-preserving public auditing scheme for cloud storage. However, in this paper, we point out the security flaw existing in the scheme. An adversary, who is on-line and active, is capable of modifying the outsourced data arbitrarily and avoiding the detection by exploiting the security flaw. To fix this security flaw, we further propose a secure and efficient privacy-preserving public auditing scheme, which makes up the security flaw of Worku et al.'s scheme while retaining all the features. Finally, we give a formal security proof and the performance analysis, they show the proposed scheme has much more advantages over the Worku et al.'s scheme.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.113-123
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• 2014

Many people expect remarkable growth in Cloud Computing industry because of it's effectiveness. But Cloud Computing industry is still at an early stage. Especially, people who in the public sector hesitate to adopt Cloud Computing Services due to security issues and their conservative views. Also, they just have limited understanding, so we need to investigate what they really know and understand. The purpose of this study is to propose Improvement plans on cloud-based smart work service of a quasi-governmental agency based on the view point of preference and tendency. To discover three types of cloud-based smart work issues and interpret theoretical definitions of those, we conducted a subjectivity analysis through qualitative methodology. At the result, the study draws three elements of typology: convenience type, concentration type, and collaboration type, and provides directions to strategic-policy studies for future cloud-based smart work services of quasi-governmental agency.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1027-1036
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• 2020

In this study, a cloud-based processing method using Agisoft Metashape, a commercial software, and Amazon web service, a cloud computing service, is introduced and evaluated to quickly generate high-precision 3D realistic data from large volume UAV images acquired in disaster sites. Compared with on-premises method using a local computer and cloud services provided by Agisoft and Pix4D, the processes of aerial triangulation, 3D point cloud and DSM generation, mesh and texture generation, ortho-mosaic image production recorded similar time duration. The cloud method required uploading and downloading time for large volume data, but it showed a clear advantage that in situ processing was practically possible. In both the on-premises and cloud methods, there is a difference in processing time depending on the performance of the CPU and GPU, but notso much asin a performance benchmark. However, it wasfound that a laptop computer equipped with a low-performance GPU takes too much time to apply to in situ processing.