• Journal of KIBIM
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• v.14 no.2
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• pp.1-12
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• 2024

The paper proposes a methodology that combines the Random Sample Consensus (RANSAC) algorithm and the Point Cloud Encoder-Decoder Network (PCEDNet) algorithm to automatically extract the length of infrastructure elements from point cloud data acquired through 3D LiDAR scans of retaining walls. This methodology is expected to significantly improve time and cost efficiency compared to traditional manual measurement techniques, which are crucial for the data-driven analysis required in the precision-demanding construction sector. Additionally, the extracted positional and dimensional data can contribute to enhanced accuracy and reliability in Scan-to-BIM processes. The results of this study are anticipated to provide important insights that could accelerate the digital transformation of the construction industry. This paper provides empirical data on how the integration of digital technologies can enhance efficiency and accuracy in the construction industry, and offers directions for future research and application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of the Optical Society of Korea
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• v.19 no.6
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• pp.695-699
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• 2015

A high spectral resolution lidar (HSRL) system based on injection-seeded Nd:YAG laser and iodine absorption filter has been developed for the quantitative measurement of aerosol and cloud. The laser frequency is stabilized at 80 MHz by a frequency locking system and the absorption line of iodine cell is selected at the 1111 line with 2 GHz width. The observations show that the HSRL can provide vertical profiles of particle extinction coefficient, backscattering coefficient and lidar ratio for cloud and aerosol up to 12 km altitude, simultaneously. For the measured cases, the lidar ratios are 10~20 sr for cloud, 28~37 sr for dust, and 58~70 sr for urban pollution aerosol. It reveals the potential of HSRL to distinguish the type of aerosol and cloud. Time series measurements are given and demonstrate that the HSRL has ability to continuously observe the aerosol and cloud for day and night.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.99-110
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• 2023

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.77-82
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• 2023

Multi-cloud rendering has been attracting attention recently as the computational load of rendering fluctuates over time and each rendering process can be performed independently. However, it is challenging in multi-cloud rendering to deliver large amounts of input data instantly with consistency constraints. In this paper, we develop a new distributed file system for multi-cloud rendering. In our file system, a local machine maintains a file server that manages versions of rendering input files, and each cloud node maintains a rendering cache manager, which performs distributed cooperative caching by considering file versions. Measurement studies with rendering workloads show that the proposed file system performs better than NFS and the uploading schemes by 745% and 56%, respectively, in terms of I/O throughput and execution time.

• Proceedings of the Optical Society of Korea Conference
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• 2009.02a
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• pp.451-452
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• 2009

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.221-222
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• 2010

• Proceedings of the Optical Society of Korea Conference
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• 2009.10a
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• pp.177-178
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• 2009

• Proceedings of the Optical Society of Korea Conference
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• 2007.07a
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• pp.143-144
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• 2007

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.10a
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• pp.227-228
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• 2006