• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.67-74
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• 2010

The aim of this paper is to come up with a methodology of migration for current transportation digital map in order to construct NDRM, which is the most essential map data for car navigation system. The model suggested through our study is able to efficiently produce navigable service map for route finding and guidance as well as to make the best of general road network developed by KOTI.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Genomics & Informatics
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• v.1 no.2
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• pp.108-112
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• 2003

We have constructed a molecular linkage map of pepper (Capsicum spp.) in an interspecific $F_2$ population of 107 plants with 320 RFLP, 136 AFLP, and 46 SSR markers. The resulting linkage map consists of 15 linkage groups covering 1,720 cM with an average map distance of 3.7 cM between framework markers. Most RFLP markers ($80\%$) were pepper-derived clones and these markers were evenly distributed all over the genome. Genes for defense and biosynthesis of carotenoids and capsaicinoids were mapped on this linkage map. By using 30 primer combinations, AFLP markers were generated in the $F_2$ population. For development of SSR markers in Capsicum, microsatellites were isolated from two small-insert genomic libraries and the GenBank database. This combined map provides a starting point for high-resolution QTL analysis, gene isolation, and molecular breeding.

• Korean Journal of Veterinary Research
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• v.55 no.2
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• pp.89-95
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• 2015

Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is one of the most widespread and economically important diseases in cattle. Current diagnostic methods are based on the detection of anti-MAP antibodies in serum or isolation of the causative agent. However, these techniques are often not applicable for cases of subclinical infection due to relatively low sensitivity. Therefore, finding new antigen candidates that strongly react with the host immune system had been attempted. To effectively detect infection during the subclinical stage, several antigen candidates were selected based on previous researches. Characteristics of the selected antigen candidates were analyzed using bioinformatics-based prediction tools. A total of nine antigens were selected (MAP0862, MAP3817c, MAP2077c, MAP0860c, MAP3954, MAP3155c, MAP1204, MAP1087, and MAP2963c) that have MAP-specific and/or high immune responses to infected animals. Using a transmembrane prediction tool, five of the nine antigen candidates were predicted to be membrane protein (MAP3817c, MAP3954, MAP3155c, MAP1087, and MAP1204). Some of the predicted protein structures identified using the I-TASSER server shared similarities with known proteins found in the Protein Data Bank database (MAP0862, MAP1204, and MAP2077c). In future studies, the characteristics and diagnostic efficiency of the selected antigen candidates will be evaluated.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.277-281
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• 2013

Modern speaker verification systems based on support vector machines (SVMs) use Gaussian mixture model (GMM) supervectors as their input feature vectors, and the maximum a posteriori (MAP) adaptation is a conventional method for generating speaker-dependent GMMs by adapting a universal background model (UBM). MAP adaptation requires the appropriate amount of input utterance due to the number of model parameters to be estimated. On the other hand, with limited utterances, unreliable MAP adaptation can be performed, which causes adaptation noise even though the Bayesian priors used in the MAP adaptation smooth the movements between the UBM and speaker dependent GMMs. This paper proposes a sparse MAP adaptation method, which is known to perform well in the automatic speech recognition area. By introducing sparse MAP adaptation to the GMM-SVM-based speaker verification system, the adaptation noise can be mitigated effectively. The proposed method utilizes the L0 norm as a regularizer to induce sparsity. The experimental results on the TIMIT database showed that the sparse MAP-based GMM-SVM speaker verification system yields a 42.6% relative reduction in the equal error rate with few additional computations.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.536-538
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• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.57-68
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• 1999

In case of topographic information construction it is most important as basemap determines the success or failure of GIS It is argument that digitizing works of basemap are divided by NGIS and PBLIS. So, it have to accompany the combination and application of cadastral information for constructing a usful basemap. In this study, we generated ortho image map using 1:5,000 aerial-photography image, present the comparative analysis for the overlay accuracy and map revision of topographic and cadastral information using ortho image map to base map. So, we present application schemes for land use, environment, and city planning field as well as union database by overlaying of image map and cadastral information.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.7-18
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• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.

• Asian review of World Histories
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• v.4 no.1
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• pp.83-123
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• 2016

We describe a vision of historical analysis at the world scale, through the digital assembly of historical sources into a cloud-based database, where machine-learning techniques can be used to summarize the database into a time-integrated actor-to-actor complex network. Using this time-integrated network as a template, we then apply the method of automatic narratives to discover key actors ('who'), key events ('what'), key periods ('when'), key locations ('where'), key motives ('why'), and key actions ('how') that can be presented as hypotheses to world historians. We show two test cases on how this method works. To accelerate the pace of knowledge discovery and verification, we describe how historians would interact with these automatic narratives through an online, map-based knowledge aggregator that learns how scholars filter information, and eventually takes over this function to free historians from the more important tasks of verification, and stitching together coherent storylines. Ultimately, multiple coherent storylines that are not necessary compatible with each other can be discovered through human-computer interactions by the map-based knowledge aggregator.

• Genomics & Informatics
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• v.5 no.4
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• pp.188-193
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• 2007

The Functional Element SNPs Database (FESD) categorizes functional elements in human genic regions and provides a set of single nucleotide polymorphisms (SNPs) located within each area. Users may select a set of SNPs in specific functional elements with haplotype information and obtain flanking sequences for genotyping. Our previous version of FESD has been improved in several ways. We regenerated all the data in FESD II from recently updated source data such as HapMap, UCSC GoldenPath, dbSNP, OMIM, and $TRANSFAC^{(R)}$. Users can obtain information about tagSNPs and simulate LD blocks for each gene from four ethnicities in the HapMap project on the fly. FESD II employs a Java/JSP web interface for better platform portability and higher speed than PHP in the previous version. As a result, FESD II provides its users with more powerful information about functional element SNPs of human ethnicities.