• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.2
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• pp.40-46
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• 2009

The purpose of this study was to estimate natural dyeing properties of Korean traditional paper (Hanji). We dyed the Korean traditional paper with dyestuff which extracted from wood meal of Cotinus coggygria Scop (smoke tree) using hot-water, ethanol and $K_{2}CO_{3}$ solution. As mordants, 0.5% of $AlK(SO_4)_2$, $FeCl_2$, and $CuCH_3(COO)_2$ solution were used respectively. The color of dyed Hanji mainly depended on not the methods of extraction but the kinds of mordant. The dyed Hanji mordant with $AlK(SO_4)_2$ colored vivid yellow, $FeCl_2$ colored dark olive, and $CuCH_3(COO)_2$ colored brown and/or orange. The dyed Hanji with hot-water extractive had the highest K/S value and ethanol and $K_{2}CO_{3}$ solution extractives were followed. The K/S value of dyed Hanji mordant with $AlK(SO_4)_2$ was higher than that of $FeCl_2$ and $CuCH_3(COO)_2$. The dyeing effectiveness of after-mordanting method was superior to the others but sim-mordanting method was the worst.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.202-212
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• 2014

To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.41-50
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• 2020

The National Geographic Information Institute of Korea takes digital aerial photograph images every two years to make and modify/renew the digital map. The cameras for aerial photogrammetry to capture these digital aerial photographs are divided into frame types and line types. Computer vision analysis of aerial photograph images was only possible for frame type. Thus, in this study, Line type aerial photograph images was intended to generate geospatial information through computer vision analysis, and forest geospatial information was created as a method for the utilization of aerial picture images. As a result, geospatial information generated by computer vision analysis of line type aerial photograph images showed that RMSE of horizontal and vertical position errors was less than quadruple that of GSD. Forest geospatial information was generated using geospatial information generated by computer vision analysis. It was confirmed that extraction of the crown of tree and calculation of tree height are possible. Through this study, it is expected that utilization of aerial photograph images will be improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.23-29
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• 2004

In this paper, we propose a search method for time sequences which supports the normalized distance as a similarity measure. In many applications where the shape of the time sequence is a major consideration, the normalized distance is a more suitable similarity measure than the simple Lp distance. To support normalized distance queries, most of the previous work has the preprocessing step for vertical shifting which normalizes each sequence by its mean. The proposed method is motivated by the property of sequence for feature extraction. That is, the variation between two adjacent elements of a time sequence is invariant under vertical shifting. The extracted feature is indexed by the spatial access method such as R-tree. The proposed method can match time series of similar shape without vertical shifting and guarantees no false dismissals. The experiments are performed on real data(stock price movement) to verify the performance of the proposed method.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Journal of KIISE
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• v.44 no.11
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• pp.1236-1243
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• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1007-1016
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• 2005

This paper presents a feature extraction method for shape based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scaling, it is necessary to preprocess the 3D models to represent them in a canonical coordinate system. We use the PCA(Principal Component Analysis) method to preprocess the 3D models. Also, we apply that to make a MBR(Minimum Boundary Rectangle) and a circumsphere. The proposed algorithm is as follows. We generate a circumsphere around 3D models, where radius equals 1(r=1) and locate each model in the center of the circumsphere. We produce the concentric spheres with a different radius($r_i=i/n,\;i=1,2,{\ldots},n$). After looking for meshes intersected with the concentric spheres, we compute the curvature of the meshes. We use these curvatures as the model descriptor. Experimental results numerically show the performance improvement of proposed algorithm from min. 0.1 to max. 0.6 in comparison with conventional methods by ANMRR, although our method uses .relatively small bins. This paper uses $R{^*}-tree$ as the indexing.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.267-274
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• 2018

This paper describes a head pose estimation method using PTZ(Pan-Tilt-Zoom) camera. When the external parameters of a camera is changed by rotation and translation, the estimated face pose for the same head also varies. In this paper, we propose a new method to estimate the head pose independently on varying the parameters of PTZ camera. The proposed method consists of 3 steps: face detection, feature extraction, and pose estimation. For each step, we respectively use MCT(Modified Census Transform) feature, the facial regression tree method, and the POSIT(Pose from Orthography and Scaling with ITeration) algorithm. The existing POSIT algorithm does not consider the rotation of a camera, but this paper improves the POSIT based on perspective projection in order to estimate the head pose robustly even when the external parameters of a camera are changed. Through experiments, we confirmed that RMSE(Root Mean Square Error) of the proposed method improve $0.6^{\circ}$ less then the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1087-1094
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• 2008

Recently, XML has been widely used as a standard for a data exchange, and there has emerged the tendency that the size of XML document becomes larger. The data transfer can cause problems due to the increase in traffic, especially when a massive data such as Data Warehouse is being collected and analyzed. Therefore, an XMDR wrapper can solve this problem since it analyzes the tree structures of XML Schema, regenerates XML Schema using the analyzed tree structures, and sends it to each station with an XMDR Query. XML documents which are returned as an outcome encode XML tags according to XML Schema, and send standardized messages. As the formalized XML documents decrease network traffic and comprise XML class information, they are efficient for extraction, conversion, and alignment of data. In addition, they are efficient for the conversion process through XSLT, too, as they have standardized forms. In this paper we profuse a method in which XML Schema and XMDR_Query sent to each station are generated through XMDR(extended Meta-Data Registry) and the generation of products and XML conversion occur in each station wrapper.