• Journal of the Korean Institute of Landscape Architecture
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• v.32 no.6 s.107
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• pp.36-51
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• 2005

The purpose of this thesis is to analyse modem urbanization process and the morphological transformation of the urban tissues in Kunsan between the you 1899 and 2001, The method of this study is to investigate the transformation process of morphological elements such as plot structure, building layout, building facades, land use, exterior space structure and their use, with actual field surveys, the analysis of land registration maps in 1912, and various topological map. Morphological analysis on modern Kunsan is progressed by three steps-typo-morphological analysis of urban tissue in old-town area, interpretation of morphological process, and transformation process, of morphological structure in Japanese concession in view of plots system. As a result, it is found that there is cyclical relationship among the morphological transformation processes of morphological elements, plots, buildings, land-uses, and access space to buildings. From the view of town plan change, the period of restoration of war damage in 1950s and compressive growing period in 1960s have important meaning in the morphological process of old-town area. Particularly the first building plan and layout type together with plot form and structure is acted as the main factor to decide the subsequent plot transformation system, exterior space system and the particular streetscape in Kunsan.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.1-54.1
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• 2019

Solar observations often show that interaction of more than one flux rope is involved in solar eruptions. In this regard, Lau and Finn (1996) intensively studied the interaction of two flux ropes, which reside in between two parallel planes each mimicking one polarity region of the solar photosphere. However, this geometry is quite far from the real solar situation, in which all feet of flux tubes are rooted in one surface only. In this paper, we study the interaction of two flux ropes in a semi-infinite region above a plane representing the solar photosphere. Four cases of the flux rope interaction are investigated in our MHD simulation study: (1) parallel axial fields and parallel axial currents (co-helicity), (2) antiparallel axial fields and parallel axial currents (counter-helicity), (3) parallel axial fields and antiparallel axial currents (counter-helicity), and (4) antiparallel axial fields and antiparallel axial currents (co-helicity). Each case consists of four or six subcases according to the background field direction relative to the flux ropes and the relative positions of the flux rope footpoints. In our simulations, all the cases eventually show eruptive behaviors, but their degree of explosiveness and field topological evolutions are quite different. We construct artificial emission measure maps based on the simulations and compare them with images of CME observations, which provides us with information on what field configurations may generate certain eruption features.

• The Mathematical Education
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• v.16 no.2
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• pp.22-26
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• 1978

It is shown that a map having an extension to an open map between the Alex-androff base compactifications of its domain and range has a unique such extension. J.S. Wasileski has introduced the Alexandroff base compactifications of Hausdorff spaces endowed with Alexandroff bases. We introduce a definition of morphism between such spaces to obtain a category which we denote by ABC. We prove that the Alexandroff base compactification on objects can be extended to a functor on ABC and that the compact objects give an epireflective subcategory of ABC. For each topological space X there exists a completely regular space $\alpha$X and a surjective continuous function $\alpha$$_{x}$ : Xlongrightarrow$\alpha$X such that for each completely regular space Z and g$\in$C (X, Z) there exists a unique g$\in$C($\alpha$X, 2) with g=g$^{\circ}$$\beta$$_{x}$. Such a pair ($\alpha$$_{x}$, $\alpha$X) is called a completely regularization of X. Let TOP be the category of topological spaces and continuous functions and let CREG be the category of completely regular spaces and continuous functions. The functor $\alpha$ : TOPlongrightarrowCREG is a completely regular reflection functor. For each topological space X there exists a compact Hausdorff space $\beta$X and a dense continuous function $\beta$x : Xlongrightarrow$\beta$X such that for each compact Hausdorff space K and g$\in$C (X, K) there exists a uniqueg$\in$C($\beta$X, K) with g=g$^{\circ}$$\beta$$_{x}$. Such a pair ($\beta$$_{x}$, $\beta$X) is called a Stone-Cech compactification of X. Let COMPT$_2$ be the category of compact Hausdorff spaces and continuous functions. The functor $\beta$ : TOPlongrightarrowCOMPT$_2$ is a compact reflection functor. For each topological space X there exists a realcompact space (equation omitted) and a dense continuous function (equation omitted) such that for each realcompact space Z and g$\in$C(X, 2) there exists a unique g$\in$C (equation omitted) with g=g$^{\circ}$(equation omitted). Such a pair (equation omitted) is called a Hewitt's realcompactification of X. Let RCOM be the category of realcompact spaces and continuous functions. The functor (equation omitted) : TOPlongrightarrowRCOM is a realcompact refection functor. In [2], D. Harris established the existence of a category of spaces and maps on which the Wallman compactification is an epirefiective functor. H. L. Bentley and S. A. Naimpally [1] generalized the result of Harris concerning the functorial properties of the Wallman compactification of a T$_1$-space. J. S. Wasileski [5] constructed a new compactification called Alexandroff base compactification. In order to fix our notations and for the sake of convenience. we begin with recalling reflection and Alexandroff base compactification.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Investigative Magnetic Resonance Imaging
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• v.15 no.2
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• pp.110-122
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• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.12 no.1
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• pp.43-52
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• 1994

This paper outlines a new approach to the line generalization when preparing small scale map on the basis of existing large scale digital map. Line generalizations are conducted based on Douglas algorithm using 1/25,000 scale topographic maps of southeastern JEJU island which produced by National Geographic Institute to analyze the fitness to the original and problems of graphical representation. Compare to the same scale map which was generated by manual method, a verity of small, but sometimes significant errors ＆ modification of topological relationship have been detected. The research gives full details of three algorithms that operationalize the smallest visible object method, together with some empirical results. A comparison of the results produced by the new algorithms with those produced by manual generalization and Douglas method of data reduction is provided. Also this paper presents the preliminary results of an relationships between the size of smallest visual object and requiring data storages for each algorithms.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.296-309
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• 2003

One of commercial applications of mobile computing is ATIS(Advanced Traveler Information Systems) in ITS(Intelligent Transport Systems). In ATIS, a primary mobile computing task is to compute the shortest path from the current location to the destination. In this paper, we have studied the shortest path re-computation problem that arises in the DRGS(Dynamic Route Guidance System) in ATIS where the cost of topological digital road map is frequently updated as traffic condition changes dynamically. Previously suggested methods either re-compute the shortest path from scratch or re-compute the shortest path just between the two end nodes of the edge where the cost change occurs. However, these methods we trivial in that they do not intelligently utilize the previously computed shortest path information. In this paper, we propose an efficient approximate shortest path re-computation method based on the dynamic window scheme. The proposed method re-computes an approximate shortest path very quickly by utilizing the previously computed shortest path information. We first show the theoretical analysis of our methods and then present an in-depth experimental performance analysis by implementing it on grid graphs as well as a real digital road map.