• 한국전산유체공학회지
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• 제11권4호
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• pp.48-55
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• 2006

A computational study was carried out in order to investigate aerodynamic characteristics on leading edge sweepback angles of Flying-Wing configurations. The viscous-compressible Navire-Stokes equation and Spalart-Allmaras turbulence model of the commercial CFD code were adopted for this computation analysis. This investigation examined aerodynamic characteristics of three different types of leading edge sweepback angles: $30^{\circ}C,\;35^{\circ}C\;and\;40^{\circ}C$. The freestream Mach number was M=0.80 and the angle of attack ranged from ${\alpha}=0^{\circ}C\;to\;{\alpha}=20^{\circ}C$. The results show that the increases in sweepback angle of the Flying-Wing configuration creates more efficient aerodynamic performance.

• 한국통신학회논문지
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• 제23권9A호
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• pp.2372-2382
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• 1998

In this paper, we propose a new edge based finite state vector quantization method having better performance than conventional side-match finite state vector quantization. In our proposed scheme, each dCT transformed block is classified to 17 classes according to edge types. Each class has a different codebook based on its characteristis. Encoder classified each block to motion block or stationary block and constructed a merging map by using edge and motion information, and sent to decoder. We controled amoutn of bing bits transmitted with selecting modes accoridng to bandwidth of transmitting channel. Compared with conventional algorithms, H.263 and H.261 at low bit rate, our proposed algorithm shows better picture quality and good performance.

• Advances in nano research
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• 제16권6호
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• pp.601-613
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• 2024

A new analytical buckling solution of a thermo-electro-magneto-elastic (TEME) cylindrical nano-shell made of BiTiO₃-CoFe₂O₄ materials is obtained based on Hamiltonian approach. The Winkler and Pasternak elastic foundations as well as thermo-electro-magneto-mechanical loadings are applied, and two different types of edge conditions are taken into the investigation. According to nonlocal strain gradient theory (NSGT) and surface elasticity theory in conjunction with the Kirchhoff-Love theory, governing equations of the nano-shell are acquired, and the buckling bifurcation condition is obtained by adopting the Navier's method. The detailed parameter study is conducted to investigate the effects of axial and circumferential wave numbers, scale parameters, elastic foundations, edge conditions and thermo-electro-magnetic loadings on the buckling behavior of the nano-shell. The proposed model can be applied in design and analysis of TEME nano components with multi-field coupled behavior, multiple edge conditions and scale effect.

• 환경영향평가
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• 제27권3호
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• pp.279-290
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• 2018

도시화와 인간의 영향으로 도심 내 산림 임연부가 증가함에 따라 도시 산림 관리 측면에서 도시 산림 임연부의 현황 파악과 모니터링의 중요성이 대두되고 있다. 본 연구는 도시 산림 임연부의 현황파악을 위해 시간적 예측, 공간적 예측에서 정확도가 높은 FSDAF(Flexible Spatio-temporal DAta Fusion) 융합 영상 기법을 활용하여 도출한 $NDVI_{max}$ 영상을 사용하여 인접한 토지피복 유형에 따른 도시 산림 임연부의 식생 활력도 차이를 평가하는데 목적이 있다. 서울시 내 도시 산림 임연부를 대상으로 분석해 본 결과, 산림 내부로 갈수록 식생활력도가 증가하는 경향이 나타났다. 임연부에 인접한 4가지 토지피복 유형 중 도로가 산림 임연부에 미치는 영향이 가장 큰 것으로 나타났다. 특히, 도로로부터 산림 임연부의 30m까지 그 영향이 가장 두드러지게 나타났으며, 90m까지 영향을 미치는 것으로 나타났다. 본 연구의 결과는 도시 산림 모니터링 및 도시 산림 임연부 관리 측면에서 토지 피복 유형과 토지피복 변화가 인접한 산림에 미치는 영향을 평가하는데 활용 가능할 것으로 기대된다.

• 대한원격탐사학회지
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• 제32권1호
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• pp.13-24
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• 2016

This paper presents a method to evaluate the performance of subpixel localization operators using target image data. Subpixel localization of edges is important to extract the precise shape of objects from images. In this study, each target image was designed to provide reference lines and edges to which the localization operators can be applied. We selected two types of moment-based operators: Gray-level Moment (GM) operator and Spatial Moment (SM) operator for comparison. The original edge localization operators with kernel size 5 are tested and their extended versions with kernel size 7 are also tested. Target images were collected with varying Camera-to-Object Distance (COD). From the target images, reference lines are estimated and edge profiles along the estimated reference lines are accumulated. Then, evaluation of the performance of edge localization operators was performed by comparing the locations calculated by each operator and by superimposing them on edge profiles. Also, enhancement of edge localization by increasing the kernel size was also quantified. The experimental result shows that the SM operator whose kernel size is 7 provides higher accuracy than other operators implemented in this study.

• 한국산림과학회지
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• 제102권1호
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• pp.113-121
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• 2013

가장자리는 산불 피해 후 산림생태계 변화 및 회복과정에 중요한 역할을 하는 것으로 알려져 있다. 본 연구는 산불피해지의 공간 자기상관성을 고려하여 산불피해지 가장자리 형성과 경관특성변수들과의 관계 분석에 목적을 두고 수행되었다. 연구대상지로는 2000년도에 발생한 삼척 산불피해지를 선정하였으며, 대상지내 경관특성변수 측정을 위하여 산불피해지 전 지역을 포함하도록 500 $m^2$ 격자를 생성하였다. 연구에 사용된 경관 특성 변수들로는 표고, 경사, TWI(Topographic Wetness Index), SRI(Solar Radiation Index)을 사용하였고, 연료유형변수와 토지피복 변수를 포함시켰다. 격자들은 산불피해지 경계선과 교차하는 격자는 가장자리로 그 외의 격자들은 내부지역으로 설정하였다. 공간자기 상관을 보정하기 위하여 경관 변형된 t-검정과 상관분석을 실시하였다. 분석 결과 산불피해지 경계 형성과 양의 관계를 보이는 변수는 TWI, SRI, 물, 전, 답, 개발지, 나대지이며, 음의 관계를 보이는 경관특성변수는 경사, 표고, 그리고 모든 연료 유형 변수들이었다. 특히 TWI은 r=0.437로 경계형성과 강한 양의 관계를 보였다. 따라서 산불 피해지의 경계는 산림과 이질적인 토지피복 혹은 토지이용이 존재하는 경우와 경사가 완만하고 표고는 낮으며, 토양 및 지표면의 상대습도가 높은 지형에서 형성될 가능성이 높은 것으로 나타났다.

• 전기학회논문지
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• 제59권3호
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• pp.651-656
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• 2010

This paper proposes a new dynamic PCA algorithm to recognize types of electric poles, which is necessary for a mobile robot moving along the neutral line for inspecting high-voltage facilities. Since the mobile robot needs to pass over the electric poles and grasp the neutral wire again for the next region inspection, the detection of the electric pole type is a critical factor for the successful passing-over the electric pole. The CCD camera installed on the mobile robot captures the image of the electric pole while it is approaching to the electric pole. Applying the dynamic PCA algorithm to the CCD image, the electric pole type has been classified to provide the stable grasping operation for the mobile robot. The new dynamic PCA algorithm replaces the reference image in real time to improve the robustness of the PCA algorithm, adjusts the brightness to get the clear images, and applies the Laplacian edge detection algorithm to increase the recognition rate of electric pole type. Through the real experiments, the effectiveness of this proposed dynamic PCA algorithm method using Laplacian edge detecting method has been demonstrated, which improves the recognition rate about 20% comparing to the conventional PCA algorithm.

• International Journal of Advanced Culture Technology
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• 제10권2호
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• pp.300-306
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• 2022

Currently, Korea is building traffic infrastructure using Intelligent Transport Systems (ITS), but the pedestrian traffic accident rate is very high. The purpose of this paper is to prevent the risk of traffic accidents by jaywalking pedestrians. The development of this study aims to detect pedestrians who trespass using the public data set provided by the Artificial Intelligence Hub (AIHub). The data set uses training data: 673,150 pieces and validation data: 131,385 pieces, and the types include snow, rain, fog, etc., and there is a total of 7 types including passenger cars, small buses, large buses, trucks, large trailers, motorcycles, and pedestrians. has a class format of Learning is carried out using YOLOv5 as an implementation model, and as an object detection and edge detection method of an input image, a canny edge model is applied to classify and visualize human objects within the detected road boundary range. In this study, it was designed and implemented to detect pedestrians using the deep learning-based YOLOv5 model. As the final result, the mAP 0.5 showed a real-time detection rate of 61% and 114.9 fps at 338 epochs using the YOLOv5 model.

• 한국환경생태학회지
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• 제3권1호
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• pp.51-69
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• 1989

가야산국립 공원의 주연부 식생구조와 우세수종을 밝히기 위해 1989년 7~8월 사이에 현지조사를 한 결과는 다음과 같다. 수간선형과 전진형 주연부식생이 출현했다. 주연부에서 삼림내부로의 거리에 따른 출현수종들의 상대우점치변화는 방위, 토양수분, 기존상층우점식생에 영향을 받는 것으로 나타났으며 특히, 소나무림내부에서는 소나무와 친화성있는 양수들이 우세하게 출현하고 있었다. 삼림주연부에서 삼림내부로의 거리증가에 따른 생태지수들의 변동이 일정하지 않았으나 주연부깊이는 대략 15~20m로 나타났다. 주연부 우점수종은 고도, 방위보다는 토양수분에 영향받는 것으로 나타났으나 종구성의 유사성은 고도에 영향받는 것으로 나타났다. 고도, 방위, 지형측위치에 따라 주연부 수종들의 출현빈도의 차이가 있었으며, 고도, 방위, 입지조건에 관계없이 조록싸리, 병꽃나무, 물푸레나무는 높은 출현빈도를 나타냈다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.