• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.27-32
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• 2008

Purpose : We developed a 3D CSI (chemical shift imaging) sequence that uses the PRESS (point resolved spectroscopy) excitation scheme and spiral-based readout gradients. Materials and Methods : We implemented constant-density spirals ($32{\times}32$ matrix, $24{\times}24\;cm$ FOV) which use analytic equations to enable real-time prescription on the scanner. In-vivo data from the brain were collected and reconstructed using the gridding algorithm. Results : Data illustrate that with our imaging sequence, the benefits of the PRESS technique, which include elimination of lipid artifacts, remain intact while flexible scan time versus resolution tradeoffs can be achieved using the constant-density spirals. Volumetric high resolution 3D CSI covering 5760 cm3 could be obtained in 12.5 minutes. Conclusion : Spiral-based readout gradients offer a flexible tradeoff between scan time versus resolution. By combining this feature with PRESS based excitation, efficient methods of volumetric spectroscopic imaging can be accomplished by obtaining whole brain coverage while eliminating lipid contamination.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.3
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• pp.215-221
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• 2012

In this paper, we introduce an optimization method to improve pursuit performance of a pursuer in a pursuit-evasion game (PEG). Pursuers build a probability map and employ a hybrid pursuit policy which combines the merits of local-max and global-max pursuit policies to search and capture evaders as soon as possible in a 2-dimensional space. We propose an episodic parameter optimization (EPO) algorithm to learn good values for the weighting parameters of a hybrid pursuit policy. The EPO algorithm is performed while many episodes of the PEG are run repeatedly and the reward of each episode is accumulated using reinforcement learning, and the candidate weighting parameter is selected in a way that maximizes the total averaged reward by using the golden section search method. We found the best pursuit policy in various situations which are the different number of evaders and the different size of spaces and analyzed results.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.41-51
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• 2002

Recently, many studies extracting the geography information using digital aerial image have been implemented. But it is very difficult that automatically recognizing objects using edge detection method on the aerial image, and so that work have practiced manually or semi-automatically. Therefore, in this study, we have removed impedimental elements for recognition using the image which overlapped the significant information bands of brightness-sliced aerial images, then have developed the algorithm which can automatically recognize and extract road information and we will try to apply that method when we develope a system. For this, first of all, we have developed the 'template conformal-transformation moving operator' for automatically recognizing crosswalk area from crosswalk band image and the 'window normal search algorithm' which is able to track road area based on long-side length of crosswalk, so that we have proposed the method that can extract directly the road information from the aerial image.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.527-532
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• 2012

Terrestrial LiDAR emerges as a main mapping technology for indoor 3D cadastre, cultural heritage conservation and, building management in that it provides fast, accurate, and reliable 3D data. In this paper, a new 3D modeling method consisting of segmentation stage and outline extraction stage is proposed to develop indoor 3D model from the terrestrial LiDAR. In the segmentation process, RANSAC and a refinement grid is used to identify points that belong to identical planar planes. In the outline tracing process, a tracing grid and a data conversion method are used to extract outlines of indoor 3D models. However, despite of an improvement of productivity, the proposed approach requires an optimization process to adjust parameters such as a threshold of the RANSAC and sizes of the refinement and outline extraction grids. Furthermore, it is required to model curvilinear and rounded shape of the indoor structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.45-53
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• 2012

In this paper, we proposed the efficient addressing scheme for improving the performance of routing algorithm by using ZigBee in Smart Grid environment. A distributed address allocation scheme used an existing algorithm that has wasted in address space. Therefore proposing x, y and z coordinate axes from divided address space of 16 bit to solve this problems. Each node was reduced not only bitwise but also multi hop using the coordinate axes while routing than $Cskip$ algorithm. I compared the performance between the standard and the proposed mechanism through the numerical analysis. Simulation verified performance about decrease averaging multi hop count that compare proposing algorithm and another. The numerical analysis results show that proposed algorithm reduced the multi hop better than ZigBee distributed address assignmen.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.46-56
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• 2021

Predicting the incident clearance time is important for eliminating the high transportation costs and congestion from non-repetitive congestion caused by incidents. In this study, the factors influencing the clearance time suitable for domestic road conditions were analyzed, using a training dataset for predicting the incident clearance time using artificial neural networks. In a previous study, the under-prediction problem for high incident clearance time was used. In the present study, over-sampling training data applied using the SMOGN technique was obtained and applied to the model as a solution. As a result, the DNN model applying the SMOGN technique could compensate for the limitations of the previously developed prediction model by predicting the clearance time with the highest accuracy among the models developed in the research process with MAE = 18.3 minutes.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.28-34
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• 2002

This paper describes embedding non-linearly watermark data in the components of pixel intensities in the spatial domain of an image. The principle of the proposed method is that when an image is segmented regularly to the blocks, the pixels of the block have the non-linear properties without any similarity. That is, for the block with strong non-linear property human can't feel the visual different to the modified pixel values, on the other hand for the block with weak non-linear property human can feel the visual different to the a little modified pixel values. Thus we could embed the watermark data according to the non-linear property of the blocks. As the result of the simulation, against some general image processing attacks our algorithm could keep robust and be responsible for the copyright certainly. 

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.856-868
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• 2007

We propose an effective method for block classification in a document image. The gray level document image is converted to the binary image for a block segmentation. This binary image would be smoothed to find the locations and sizes of each block. And especially during this smoothing, the inner block heights of each block are obtained. The gray level image is divided to several blocks by these location informations. The SGLDM(spatial gray level dependence matrices) are made using the each gray-level document block and the seven second-order statistical texture features are extracted from the (0,1) direction's SGLDM which include the document attributes. Document image blocks are classified to two groups, text and non-text group, by the inner block height of the block at the nearest neighbor rule. The seven texture features(that were extracted from the SGLDM) are used for the five detail categories of small font, large font, table, graphic and photo blocks. These document blocks are available not only for structure analysis of document recognition but also the various applied area.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.508-522
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• 2019

Because of the slope failure, not only property damage but also human damage can occur, slope stability analysis should be conducted to predict and reinforce of the slope. This paper, defines the ground areas that can be characterized in terms of slope failure such as Rockmass jointset, Rockmass fault, Soil, Leakage water and Crush zone in sloped images. As a result, it was shown that the deep learning instance segmentation network can be used to recognize and automatically segment the precise shape of the ground region with different characteristics shown in the image. It showed the possibility of supporting the slope mapping work and automatically calculating the ground characteristics information of slopes necessary for decision making such as slope reinforcement.