• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.908-911
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• 2014

본 논문은 진공을 이용한 흡착방식과 바퀴형 이동방식을 이용하고 환경 탐지용 센서를 부착한 벽면 이동형 로봇의 물리적 해석을 통한 이동 성능 개선에 관한 연구로서, 대형 구조물의 안전 검사 및 위험한 시설물의 보수 작업 등을 보조하기 위한 목적이 있다. 로봇의 무게에 따른 중력을 견딜 수 있는 강력한 진공흡착방식과 고성능 모터제어에 의한 바퀴 이동방식을 혼합하고 효율적으로 평형을 유지 또는 제어하기 위하여 로봇에 미치는 다양한 힘과 모멘트를 분석하고 수식화 하였으며 기존의 수직이동 속도를 개선하기 위한 로봇의 물리적 변수를 추출하여 변수와 이동력간의 관계를 고찰하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.576-578
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• 2022

딥러닝 기술의 발전으로 객체 인색, 영상 분석에 관한 성능이 비약적으로 발전하였다. 하지만 고성능 GPU 를 사용하는 컴퓨팅 환경이 아닌 제한적인 엣지 디바이스 환경에서의 영상 처리 및 딥러닝 모델의 적용을 위해서는 엣지 디바이스에서 딥러닝 모델 실행 환경 과 이에 대한 분석이 필요하다. 본 논문에서는 RISC-V ISA 를 구현한 RISC-V 가상 플랫폼에 yolov3-tiny 모델 기반 객체 인식 시스템을 소프트웨어 레벨에서 포팅하여 구현하고, 샘플 이미지에 대한 네트워크 딥러닝 연산 및 객체 인식 알고리즘을 적용하여 그 결과를 도출하여 보았다. 본 적용을 바탕으로 RISC-V 기반 임베디드 엣지 디바이스 플랫폼에서 딥러닝 네트워크 연산과 객체 인식 알고리즘의 수행에 대한 분석과 딥러닝 연산 최적화를 위한 알고리즘 연구에 활용할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.608-611
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• 2022

The existing navigation applications mainly have a function that informs speed control, and it does not warn the location of black ice. If the navigation application performs this function, it will have a great effect on reducing traffic accidents. Therefore, in this paper, we propose a method for detecting black ice and a navigation application that warns it.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.154-155
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• 2023

WebAssembly(Wasm)은 웹에서 네이티브에 가까운 속도로 실행 가능하고, 고성능 어플리케이션의 구현도 가능하기 때문에 브라우저 및 기타 플랫폼에서 활발히 사용되고 있다. 이로 인해 Wasm에 대한 보안성이 대두되고 있는데, 이때 취약점을 탐지하는 Fuzzing 기법을 적용한 연구들이 있다. Fuzzing 기법에 대한 분류 및 대표적인 도구를 소개하고 각 기법 간 차이점 및 한계점과 향후 연구 방향을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.154-155
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• 2023

오픈 소스인 YOLO(You Only Look Once) 객체 탐지 알고리즘이 공개된 이후, 산업 현장에서는 고성능 컴퓨터에서 벗어나 효율과 특수한 환경에 사용하기 위해 임베디드 시스템에 도입하고 있다. 그러나, NVIDIA의 Jetson nano의 경우, Pytorch의 YOLOv7 딥러닝 모델에 대한 추론이 진행되지 않는다. 따라서 제한적인 전력과 메모리, 연산능력 최적화 과정은 필수적이다. 본 논문은 NVIDIA의 임베디드 플랫폼 Jetson 계열의 Xavier NX, Orin AGX, Nano에서 딥러닝 모델을 적용하기 위한 최적화 과정과 플랫폼에서 다양한 크기의 YOLOv7의 PyTorch 모델들을 Tensor RT로 변환하여 FPS(Frames Per Second)를 측정 및 비교한다. 측정 결과를 통해, 각 임베디드 플랫폼에서 YOLOv7 모델의 추론은 Tensor RT는 Pytorch에서 약 4.1배 적은 FPS 변동성과 약 2.25배 정도의 FPS 속도향상을 보였다.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.50-58
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• 2005

Recently, the imaging of geological structures beneath water-covered areas has been in great demand because of numerous tunnel and bridge construction projects on river or lake sites. An electrical resistivity survey can be effective in such a situation because it provides a subsurface image of faults or weak zones beneath the water layer. Even though conventional resistivity surveys in water-covered areas, in which electrodes are installed on the water bottom, do give high-resolution subsurface images, much time and effort is required to install electrodes. Therefore, an easier and more convenient method is sought to find the strike direction of the main zones of weakness, especially for reconnaissance surveys. In this paper, we investigate the applicability of the streamer resistivity survey method, which uses electrodes in a streamer cable towed by ship or boat, for delineating a fault zone. We do this through numerical experiments with models of water-covered areas. We demonstrate that the fault zone can be imaged, not only by installing electrodes on the water bottom, but also by using floating electrodes, when the depth of water is less than twice the electrode spacing. In addition, we compare the signal-to-noise ratio and resolving power of four kinds of electrode arrays that can be adapted to the streamer resistivity method. Following this numerical study, we carried out both conventional and streamer resistivity surveys for the planned tunnel construction site located at the Han River in Seoul, Korea. To obtain high-resolution resistivity images we used the conventional method, and installed electrodes on the water bottom along the planned route of the tunnel beneath the river. Applying a two-dimensional inversion scheme to the measured data, we found three distinctive low-resistivity anomalies, which we interpreted as associated with fault zones. To determine the strike direction of these three fault zones, we used the quick and convenient streamer resistivity.

• Korean Journal of Optics and Photonics
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• v.34 no.4
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• pp.139-150
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• 2023

Despite various light detection and ranging (LiDAR) architectures, it is very difficult to achieve long-range detection and high resolution in both vertical and horizontal directions with a wide field of view (FOV). The scanning architecture is advantageous for high-performance LiDAR that can attain long-range detection and high resolution for vertical and horizontal directions. However, a large-area photodetector (PD), which is disadvantageous for detection speed, is essentially required to secure the wide FOV. Thus we propose a PD based on the static unitary detector (STUD) technique that can operate multiple small-area PDs as a single large-area PD at a high speed. The InP/InGaAs STUD PIN-PD proposed in this paper is fabricated in various types, ranging from 1,256 ㎛×949 ㎛ using 32 small-area PDs of 1,256 ㎛×19 ㎛. In addition, we measure and analyze the noise and signal characteristics of the LiDAR receiving board, as well as the performance and sensitivity of various types of STUD PDs. Finally, the LiDAR receiving board utilizing the STUD PD is applied to a 3D scanning LiDAR prototype that uses a 1.5-㎛ master oscillator power amplifier laser. This LiDAR precisely detects long-range objects over 50 m away, and acquires high-resolution 3D images of 320 pixels×240 pixels with a diagonal FOV of 32.6 degrees simultaneously.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• Composites Research
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• v.31 no.4
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• pp.151-155
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• 2018

Mechanical joint and adhesive joint are two typical joining methods for structures. The adhesive joints distribute the load over a larger area than mechanical joints and have excellent fatigue properties. However, the strength of adhesive joint greatly depends on the environmental conditions and the skill of the operator. Therefore, there is a need for techniques to evaluate the quality of the adhesive joints. The electric resistance method is a very promising technique for detecting defects by measuring the electrical resistance of an adhesive joint in which CNTs are dispersed in an adhesive. In this study, Aluminium-Aluminium adhesive single lap joint specimens were fabricated by using the adhesive dispersing CNTs using a sonicator and a 3-roll mill, and the static strengths and defect detection capabilities of the joints using the electrical resistance method were evaluated according to the CNTs content.