• Journal of English Language & Literature
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• v.56 no.6
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• pp.1295-1310
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• 2010

In her "After Language Poetry: Innovation and Its Theoretical Discontents" in Contemporary Poetics (2007), Marjorie Perloff spotted Steve McCaffery's and Lyn Hejinian's points of reference and opacity/transparency in poetic language, and theorizes in her perspicacious insights that poetic language is not a window, to be seen through, a transparent glass pointing to something outside it, but a system of signs with its own semiological interconnectedness. Providing a critique and contextualizing Perloff's argument, the purpose of this paper is to introduce a topological model for poetry, language, and theory and further to elaborate the relation between the theory and the practice of language poetry in terms of "the revolution of language." Jacques Lacan's poetics of knowledge and of the topology of the mind, in particular, that of "extimacy," can articulate the way how language poetry problematizes the opposition between inside and outside in the substance of language itself. In fact, as signifiers always refer not to things, but to other signifiers, signifiers becomes unconscious, and can say more than they actually says. The original signifiers become unconscious through the process of repression which makes a structure of multiple and polyphonic signifying chains. Language poets use this polyphonic language of the Other at Freudian "Another Scene" and Lacan's "Other." When the reader participates the constructive meanings, the locus of the language writing transforms itself into that of the Other which becomes the open field of language. The language poet can even manage to put himself in the between-the-two, a strange place, the place of the dream and of the Unheimlichkeit (uncanny), and suture between "the outer skin of the interior" and "the inner skin of the exterior" of the impossible real of definite meaning. The objective goal of the evacuation of meaning is all the same the first aspect suggested by the aims of the experimentalism by the language poetry. The open linguistic fields of the language poetry, then, will be supplemented by The Freudian "unconscious" processes of dreams, free associations, slips of tongue, and symptoms which are composed of this polyphonic language. These fields can be properly excavated by the methods and topological mapping of the poetics of extimacy and of the klein bottle.

• 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 KIPS Transactions:PartA
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• v.11A no.5
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• pp.341-346
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• 2004

Recently A Hyper-Star Graph HS(m, k) has been introduced as a new interconnection network of new topology for parallel processing. Hyper-Star Graph has properties of hypercube and star graph, further improve the network cost of a hypercube with the same number of nodes. In this paper, we show that Hyper-Star Graph HS(m, k) is subgraph of hypercube. And we also show that regular graph, Hyper-Star Graph HS(2n, n) is node-symmetric by introduced mapping algorithm. In addition, we introduce an efficient one-to-all broadcasting scheme - takes 2n-1 times - in Hyper-Star Graph HS(2n, n) based on a spanning tree with minimum height.

• 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.

• The Korean Journal of Mycology
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• v.43 no.4
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• pp.231-238
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• 2015

Genome-wide reanalyzed data of 25 Flammulina strains were compared against the reference genome (KACC42780) to establish a genome-wide single nucleotide polymorphism (SNP). The rate of mapping differences between the strains reflected in the strain variation in its result. Genome-wide SNPs distribution divided into types of homozygous SNP and heterozygous SNP moreover all of the strains demonstrated a wide variation in all of the regions. In the further study of topological relationship between the collected strains, phylogenetic tree was separated into 3 major groups. Group I contained F. velutipes var. related strains of ASI 4062, 4148, 4195. Group 2 contained strains that are different species of ASI 4188 F. elastica, ASI 4190 F. fennae, and ASI 4194 F. rossica. The other 19 strains F. velutipes were classified as a single group. However, further experiment to discriminate its genetic relationship between the white group and brown group did not verify its validity. The inferred tree exhibited a phylogenetic relationship between Korea white fruitbody forming strains of ASI 4210, 4166, 4178 and Japan white fruitbody forming strains of ASI 4209, 4167 confirmed to be genetically closely related.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.3
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• pp.56-63
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• 1999

In power system, substation facilities have become too complex and large according to extended power system. Also, some facilities becorre old and often break down unexpectedly. In order to improve the sectrity of transformer out of substation facilities, the development of diagnosis technique to transformer is very needed. In this paper, we developed a method to be analysis the origin and degree of aging by KSOM based on the dissolved gases in power transfonrer. KSOM can do topological mapping for the multi-dimensional pattern based on the dissolved gases to two dimensional plane. And potential possibility and degree of aging for nonna1 transfonrer are presented using the proposed quantitative criterion. Furtherrrore, the aging process of transfonrer is analyzed based on the proposed criterion to special transfonrer. To demonstrate the validity of peoposed method, case study is performed and its results are presented.sented.

• 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.25 no.6_1
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• pp.507-516
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• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.