• Journal of computational fluids engineering
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• v.11 no.4 s.35
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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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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• v.16 no.6
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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.

• Journal of Environmental Impact Assessment
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• v.27 no.3
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• pp.279-290
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• 2018

The importance of monitoring and assessing the status of urban forests in the aspect of urban forest management is emerging as urban forest edges increase due to urbanization and human impacts. The purpose of this study was to investigate the status of vegetation condition of urban forest edge that is affected by different land cover types using $NDVI_{max}$ images derived from FSDAF (Flexible Spatio-temporal DAta Fusion). Among 4 land cover types,roads had the greatest effect on the forest edge, especially up to 30m, and it was found to affect up to 90m in Seoul urban forest. It was also found that $NDVI_{max}$ increased with distance away from the forest edge. The results of this study are expected to be useful for assessing the effects of land cover types and land cover change on forest edges in terms of urban forest monitoring and urban forest management.

• Korean Journal of Remote Sensing
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• v.32 no.1
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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.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.113-121
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• 2013

It has been known that edges of forest fire areas play significant roles in post-fire change of forest ecosystem and recovery process. The purpose of this study was to analyze the relationships between edge formation of burned forests and landscape characteristics with consideration on spatial autocorrelation. Samcheok fire site burned in 2000 was selected as the study area. Seven hundred fifty three of 500 $m^2$ grid cells were generated for measuring landscape characteristics. This study used the topographic variables including slop, elevation, topographic wetness index, solar radiation index and proportions of fuel and land use types. In delineating landscape characteristics correlation analysis with modified t-test were performed for exploring the relationships between edge formation and landscape characteristics. The results indicated that edge formation of burned forests was positively correlated with most variables including TWI, SRI, water, paddy, developed, farm, grass, bare soil, and negatively related with elevation, slope and all fuel types. Especially TWI (r=0.437) showed a strong positive correlation with edge formation. According to the results, edge of burned forests were likely formed when proportions of heterogeneous land use types were high with mild slope and low elevation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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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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• v.10 no.2
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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.

• Korean Journal of Environment and Ecology
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• v.3 no.1
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• pp.51-69
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• 1989

To investigate edge vegetation structure and edge species in Kaya Mountain National Park, field survey was executed from July to August, 1989 and the result are as follows. Cantilevered and advancing types of edge vegetation were observed on site, The relative importance values of major species were changed along distance from edge to forest interior and were seemed to be affected by aspect, soil moisture and present tree layer vegetation. Especially, light-oriented species were observed as a codominant species under pine tree canopy due to selective allelopathy effect and thin canopy. Ecological indices according to the distance from edge to forest interior did not show regular pattern, but edge depth was estimated as 15-20m, approximately, Dominant species of edge seemed to be affected by soil moisture rather than altitude and aspect, but floristic similarities seemed to be affected by altitude. Frequency classes of edge species were different by aspect, altitude and physiogra-phical location. Lespedeza maximowiczii, Weigela subsessilis and Fraxinus rhynchophylla showed high frequency class in all environment conditions.

• Journal of Information Processing Systems
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• v.20 no.3
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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.