• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.3
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• pp.111-121
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• 2016

In this study, measurements were conducted by image processing to do an in-depth evaluation of strain of rebar in a uniaxial tension test. The distribution of strain and the necking region were evaluated. The image processing is used to analyze the color information of a colored image, so that the parts consistent with desired targets can be distinguished from the other parts. After this process, the image was converted to a binary one. Centroids of each target region are obtained in the binary images. After repeating such process on the images from starting point to the finishing point of the test, elongation between targets is calculated based on the centroid of each target. The tensile test were conducted on grade 60 #7(D22) and #9(D29) rebars fabricated in accordance with ASTM A615 standards. Strain results from image processing were compared to the results from a conventional strain gauge, in order to see the validity of the image processing. With the image processing, the measuring was possible in not only the initial elastic region but also the necking region of more than 0.5(50%) strain. The image processing can remove the measuring limits as long as the targets can be video recorded. It also can measure strain at various spots because the targets can easily be attached and detached. Thus it is concluded that the image processing helps overcome limits in strain measuring and will be used in various ways.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.61-71
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• 2022

Soil color is used to determine soil classification and its physical, chemical, and biological properties. Visual determination is the most commonly used method for identifying soil color. However, it is subjective and, in many cases, non-repeatable. Digital image processing obtains useful information from digital images, accelerates soil classification, and enables the rapid identification of soil types in a field. This study develops a digital image processing-based soil color analysis technology that can consider irregular light conditions in the field. The digital image studio was designed to simulate the characteristics of natural light (illuminance and color temperature). Also, digital images of two soil samples (Jumoonjin sand and Anseong weathered soil) were captured under 12 different light conditions. For the RGB and CIELAB color systems, soil color intensities of 24 images were obtained using digital image processing. CIELAB was suitable for dealing with irregular light conditions in the field.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.620-630
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• 2006

Event-detection is an important application of sensor networks. Join operations can facilitate event-detection with a condition table predefined by a user. When join operations are used for event-detection, it is desirable, if possible, to do in-network join processing to reduce communication costs. In this paper, we propose an energy-efficient in-network join algorithm, called PBA. In PBA, each partition of a condition table is stored along the path from each node to the base station, and then in-network joins are performed on the path. Since each node can identify the parts to store in its storage by its level, PBA reduces the cost of disseminating a condition table considerably Moreover, while the existing method does not work well when the ratio of the size of the condition table to the density of the network is a little bit large, our proposed method PBA does not have such a restriction and works efficiently in most cases. The results of experiments show that PBA is efficient usually and especially provides significant cost reduction over existing one when a condition table is relatively large in comparison with the density of the network, or the routing tree of the network is high.

• Database Research
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• v.34 no.3
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• pp.28-44
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• 2018

In a database system, the most expensive operation among relational operations is a join operation. Generally, CPU-based join operations uses parallel processing with either 1 core or 16 cores at most, which does not significantly improve the function. On the other hand, GPGPU(General-Purpose computing on Graphics Processing Units) allows parallel processing through thousands of processing units, greatly reducing the time required to perform join operations. Parallelization of the operation using GPGPU uses NVIDIA's CUDA SDK. In this paper, we implement parallelization of the join operation using GPGPU and compare the performances. The used join operations are Nested Loop Join (NLJ), Sort Merge Join (SMJ) and Hash Join (HJ), and GPGPU equipment uses TITAN Xp, GTX 1080 Ti and GTX 1080. We measure and compare the performance of join operations based on CPU and GPGPU. We compare this performance with the performance of the previous study on the join operation based on GPGPU. The results of experiment show that the performance based on GPGPU is 6~328 times faster than the one based on CPU.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2016.05a
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• pp.277-278
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• 2016

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.572-573
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• 2007

지능형 디스크 시스템은 디스크 내부에 CPU, 메모리 등을 내장한 스토리지 서브시스템으로서, 디스크 자체에서 프로세싱이 가능한 장점을 가진다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.235-236
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• 2023

The process of construction site supervision plays a crucial role in ensuring safety and quality assurance in construction projects. However, traditional methods of supervision largely depend on human vision and individual experience, posing limitations in quickly detecting and preventing all defects. In particular, the thorough supervision of expansive sites is time-consuming and makes it challenging to identify all defects. This study proposes a new construction supervision system that utilizes vision processing technology and Artificial Intelligence(AI) to automatically detect and analyze defects as a solution to these issues. The system we developed is provided in the form of an application that operates on portable devices, designed to a lower technical barrier so that even non-experts can easily aid construction site supervision. The developed system swiftly and accurately identifies various potential defects at the construction site. As such, the introduction of this system is expected to significantly enhance the speed and accuracy of the construction supervision process.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.605-614
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• 2007

As a standard query language to search XML data, XQuery and XPath were proposed by W3C. By widely using XQuery and XPath languages, recent researches focus on the development of query processing algorithm and data structure for efficiently processing XML query with the enormous XML database system. Recently, when processing XML path expressions, the concept of the structural join which may determine the structural relationship between XML elements, e.g., ancestor-descendant or parent-child, has been one of the dominant XPath processing mechanisms. However, structural joins which frequently occur in XPath query processing require high cost. In this paper, we propose a new structural join algorithm, called SISJ, based on our structured index, called SI, in order to process XPath queries efficiently. Experimental results show that our algorithm performs marginally better than previous ones. However, in the case of high recursive documents, it performed more than 30% by the pruning feature of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.834-835
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• 2023

'비프팜(VIP-farm)'은 'video processing farm'의 약자로 스마트팜으로써 컨테이너에 부착한 센서와 카메라로 조도·온도·습도 등의 내부 정보를 자동으로 취득 및 분석하여 성장 환경을 원격 제어한다. 뿐만 아니라, 기존 스마트팜의 과육 정보량 부족을 보완하기 위해 영상 처리를 이용하여 과일의 개수와 숙성도를 평가하고 적정 수확 시기를 안내하며, 사용자 간 정보공유 및 소통이 가능하도록 하는 기능을 가진다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.239-240
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• 2023

Construction site management involves overseeing tasks from the construction phase to the maintenance stage, and digitalization of construction sites is necessary for digital construction site management. In this study, we aim to conduct research on object recognition at construction sites using drones. Images of construction sites captured by drones are reconstructed into BIM (Building Information Modeling) models, and objects are recognized after partially rendering the models using artificial intelligence. For the photorealistic rendering of the BIM models, both traditional filtering techniques and the generative adversarial network (GAN) model were used, while the YOLO (You Only Look Once) model was employed for object recognition. This study is expected to provide insights into the research direction of digital construction site management and help assess the potential and future value of introducing artificial intelligence in the construction industry.