• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Progress in Medical Physics
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• v.11 no.1
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• pp.49-57
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• 2000

New method about the dose optimization problem in radiation treatment was researched. Since all conditions are more complex and there are more relevant variables, the solution of three-dimensional treatment planning is much more complicate than that of current two-dimensional one. There(ore, in this study, as a method to solve three-dimensional dose optimization problem, the considered variables was minized and researched by reducing the domain that solutions can exist and pre-determining the important beam parameters. First, the dangerous beam range that passes critical organ was found by coordinate transformation between linear accelerator coordinate and patient coordinate. And the beam size and rotation angle for rectangular collimator that conform tumor at arbitrary beam position was also determined. As a result, the available beam position could be reduced and the dependency on beam size and rotation angle, that is very important parameter in treatment planning, totally removed. Therefore, the resultant combinations of relevant variables could be greatly reduced and the dose optimization by objective function can be done with minimum variables. From the above results, the dose optimization problem was solved for the two-dimensional radiation treatment planning useful in clinic. The objective function was made by combination of dose gradient, critical organ dose and dose homogeniety. And the optimum variables were determined by applying step search method to objective function. From the dose distributions by optimum variables, the merit of new dose optimization method was verified and it can be implemented on commercial radiation treatment planning system with further research.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.348-350
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• 1997

Because of internal voids ininsulators give rise to partial discharge(PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and reduction of the insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation. We analyzed the PD pulse and AE pulses by regression analysis, compared to these obtained the correlation coefficient and determination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. Finally using statically operator such as the center of gravity(G), the gradient of the discharge distribution(C), we have analyzed for the prediction of life which we can be obtained the time, occurred of many pulse of small discharge amplitude.

• International Journal of Highway Engineering
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• v.8 no.4 s.30
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• pp.159-179
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• 2006

By using the current road design method that is based on the regulation about structure and facilities standard of the road and the route plan guide of a national road and the alignment optimization road design method which is studied in the inside and outside of country, this study operate the route plan of the sample study and compare and analysis the route character, consequently the current design method has local optimization that is formed the plan by the stage and the section. Alignment optimization road design has the system optimal route search. But cost function has limite that caused by construction parameter that is not included in cost function. So we design a road route included cost function in main fields. As a result, we obtain a realistic and economically road route. The alignment optimization road design model has to be made up some problems, like the change of vertical gradient in the tunnel section, though this defects it has a lot of merits as a geometric design tool, especially in the feasibility study and the scheme design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.555-564
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• 2014

The boundary noise in image syntheses using DIBR consists of noisy pixels that are separated from foreground objects into background region. It is generated mainly by edge misalignment between the reference image and depth map or blurred edge in the reference image. Since hole areas are generally filled with neighboring pixels, boundary noise adjacent to the hole is the main cause of quality degradation in synthesized images. To solve this problem, a new boundary noise removal algorithm using a preprocessing of the depth map is proposed in this paper. The most common way to eliminate boundary noise caused by boundary misalignment is to modify depth map so that the boundary of the depth map can be matched to that of the reference image. Most conventional methods, however, show poor performances of boundary detection especially in blurred edge, because they are based on a simple boundary search algorithm which exploits signal gradient. In the proposed method, a two-step hierarchical approach for boundary detection is adopted which enables effective boundary detection between the transition and background regions. Experimental results show that the proposed method outperforms conventional ones subjectively and objectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.165-173
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• 2004

Since the conventional image segmentation methods using mathematical morphology tend to yield over-segmented results, they normally need postprocess which merges small regions to obtain larger ones. To solve this over-segmentation problem without postprocess had to increase size of structuring element used marker extraction. As size of structuring element is very large, edge of region segments incorrectly. Therefore, this paper selectively applies structuring element of mathematical morphology to improve performance of image segmentation and classifies input image into texture region, edge region and simple region using averaged local variance and image gradient. Proposed image segmentation method removes the cause for over-segmentation of image as selectively applies size of structuring element to each region. Simulation results show that proposed method correctly segment for pixel region of similar luminance value and more correctly search texture region and edge region than conventional methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.469-487
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• 2007

Grain-Size Trend Analysis is the methodology to identify net sediment transport pathways, based on the assumption that the movement of sediment from the source to deposit leaves the identifiable spatial pattern of mean, sorting, and skewness of grain size. It can easily be implemented with low cost, so it has great potentials to contribute to geomorphological research, whereas it can also be used inadequately without recognition of its limitations. This research aims to compare three established methods of grain-size trend analysis to search for the adequate way of application, and also suggest the research tasks needed in improving this methodology 1D pathway method can corporate the field experience into analyzing the pathway, provide the useful information of depositional environments through X-distribution, and identify the long-term trend effectively. However, it has disadvantage of the dependence on subjective interpretation, and a relatively coarse temporal scale. Gao-Collins's 2D transport vector method has the objective procedure, has the capability to visualize the transport pattern in 2D format, and to identify the pattern at a finer temporal scale, whereas characteristic distance and semiquantitative filtering are controversial. Le Roux's alternative 2D transport vector method has two improvement of Gao-Collins's in that it expands the empirical rules, considers the gradient of each parameters as well as the order, and has the ability to identify the pattern at a finer temporal scale, while the basic concepts are arbitrary and complicated. The application of grain sire trend analysis requires the selection of adequate method and the design of proper sampling scheme, based on the field knowledge of researcher, the temporal scale of sediment transport pattern targeted, and information needed. Besides, the relationship between the depth of sample and representative temporal scale should be systematically investigated in improving this methodology.