• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.11
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• pp.1104-1112
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• 2014

We present a design of the deployable lightweight antenna to be used in the satellite satisfying the required performance of the onboard sensor. The analysis is performed on the SAR antenna requirements, deploying techniques including material selection, and the characterization of deployable antenna with central disk. The performance of the solid deployable antennas and the mesh antennas are simulated, and the CFRP(Carbon Fiber Reinforced Plastics) samples are manufactured and tested. It is confirmed that the deployable antennas with central disk can meet the required performance by using deploying panels or mesh.

• Journal of KIISE
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• v.44 no.9
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• pp.976-985
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• 2017

Wireless mesh network (WMN) is a promising technology for building a cost-effective and easily-deployed wireless networking infrastructure. To efficiently utilize limited radio resources in WMNs, packet transmissions (particularly, redundant packet transmissions) should be carefully managed. We therefore propose a lightweight traffic redundancy elimination (LTRE) scheme to reduce redundant packet transmissions in software-defined wireless mesh networks (SD-WMNs). In LTRE, the controller determines the optimal path of each packet to maximize the amount of traffic reduction. In addition, LTRE employs three novel techniques: 1) machine learning (ML)-based information request, 2) ID-based source routing, and 3) popularity-aware cache update. Simulation results show that LTRE can significantly reduce the traffic overhead by 18.34% to 48.89%.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.1-9
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• 2023

Recently, as Synthetic Aperture Radar (SAR), communication, and signal surveillance missions of spacecraft have become more advanced, research has been actively conducted on the deployable large mesh antenna system with excellent storage efficiency compared to the deployment area, and light weight. Deployable Mesh antennae are characterized by an increase in the number of Openings Per Inch (OPI), which is a measure of mesh weaving density as the mission frequency band increases, and this OPI change directly affects the thermal optical properties of the mesh antenna, so research on this is required. In this paper, to verify the thermal relationship between the optical properties of the mesh and antenna reflector, thermal sensitivity analysis between the mesh and the antenna reflector is performed by in-orbit thermal analysis with various optical characteristics of the mesh based on existing overseas research cases. In addition, the temperature gradient effect of the mesh reflector is analyzed.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.192.1-192.1
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• 2012

영상레이더 (SAR: Synthetic Aperture Radar) 혹은 통신 안테나/탑재체를 장착한 위성은 원하는 주파수 대역의 운용을 위해 대형 안테나 구조물 장착이 필수적이다. 이러한 대형 구조물은 일체형으로 제작되어 위성체에 접속되었을 때, 요구 무게 및 점유하는 부피가 매우 큰 특징을 지닌다. 따라서, 대형 안테나 구조물의 중량으로 인해 발사비 증가가 불가피하며, 페어링 내부의 허용된 공간에 안테나 크기는 큰 제약적 요인이 된다. 그리고, 전개식이어도, 전개후 질량 관성이 큰 구조물이 된다. 위성체의 발사 비용과 직결되는 안테나의 경량화를 위한 많은 기술이 연구되고 있다. 특히, 접이식 팽창형 안테나는 반사체의 유연한 메쉬 구조 및 경량 지지구조물로 안테나의 무게를 줄이고 수납 효율을 향상시킨다. 또한, 전개 후 안테나의 목적한 기능을 충분히 보장함과 동시에 질량 관성이 작은 구조물에 의한 위성의 기동성을 극대화할 수 있다. 국내에서도 전천후 지상관측감시 및 통신 안테나의 필요성 증대에 따라 초경량 전개형 안테나 개발의 필요성이 크게 대두되고 있으나, 현재까지는 관련 기술개발 사례가 전무한 실정으로 국내 독자적 기술 확보가 시급한 실정이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.42-43
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• 2022

본 논문은 모바일 단말에서 실시간으로 동작하는 딥러닝 기반 경량 초고해상화 기술에 관한 내용이다. 대용량 3차원 메쉬 모델의 비실시간 압축은 실시간 스트리밍 응용 시나리오에서 제약점으로 작용하고 있고, 본 논문에서는 두 입력 텐서의 차원을 일치시켜야 하는 element-wise 덧셈 대신 concatenation을 활용해 연산량을 개선하고, float-to-int8 양자화 과정에서 발생하는 오차를 줄이기 위해 weight clipping 및 regularization 기법을 활용해 초고해상화 화질 성능을 개선하였다. 제안하는 알고리즘은 기존 모바일 초고해상화 기술을 화질 측면에서 0.12dB, 처리 속도 측면에서 13.6ms 개선하였고, Mobile AI & AIM 2022 실시간 이미지 초고해상화 대회에서 1등을 수상하였다.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.