• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.10-19
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• 2008

In this paper, we propose an automated silhouette extraction method that can effectively extract inner-patterns and silhouettes from 3D scan data of cultural asset. First of all, after creating the edge list of 3D vector data, we decide contour edge and crease edge according to viewpoint. In the next step, after extracting surface silhouette by investigating the vector variation of adjacent faces in crease edge, we finally extract the contour silhouette and surface silhouette for generating the blueprint of cultural asset. To evaluate the performance of the proposed silhouette extraction method, we performed experiments of silhouette extraction using a traditional tile model, a car model and a stone monument model. Comparing with the conventional threshold-based silhouette extraction method, the proposed method extracted more distinct and clear surface silhouettes and inner-patterns by effectively removing meaningless edges, such as noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.492-497
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• 2005

Feature extraction is important to classify data with large dimension such as image data. The representative feature extraction methods lot feature extraction ate PCA, ICA, LDA and MLP, etc. These algorithms can be classified in two groups: unsupervised algorithms such as PCA, LDA, and supervised algorithms such as LDA, MLP. Among these two groups, supervised algorithms are more suitable to extract the features for classification because of the class information of input data. In this paper we suggest a new feature extraction algorithm PCA-FX which uses class information with PCA to extract ieatures for classification. We test our algorithm using Yale face database and compare the performance of proposed algorithm with those of other algorithms.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Korean Institute of Interior Design Journal
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• v.17 no.5
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• pp.99-106
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• 2008

Based on extraction items derived from previous related studies on viewing experiences in art museums, this study is conducted to investigate extraction factors that affect viewing satisfactions and to suggest a guideline for an effective viewing environment by clarifying a hierarchy among the extraction factors. For this study, a survey was given to museum visitors, and statistical analyses were conducted on data obtained from the survey. The results of this study are summarized as follows; 1. From an analysis of extraction items that affect overall viewing satisfaction, space and art works were found to be relatively satisfactory. 2. From correlation analyses of extraction items, a degree of concentration on art works was found to most 'affect the viewing satisfactions of art museums. 3. From factor analyses, extraction items were reduced to 11 extraction factors, and a simple extraction structure affecting the viewing satisfactions in art museums. 4. From multiple regression analyses, a extraction factors were derived, and a relative hierarchy among the factors was found.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.99-112
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• 1994

This paper describe the development of a systematic method of slicing solid parts based on a data structure called Sliced Profile Data Structure(SPDS). SPDS is an augmented polygon data structure that allows multiple layers of sliced profiles to be connected together. The method consists of five steps: (1) Selection of slicing directions, (2) Determination of slicing levels, (3) Creation of sliced profiles, (4) Connection of sliced profiles, and (5) Refinement. The presented method is aimed at enhancing the applicability of CSG for manufacturing by overcoming the problem of non-uniqueness and global nature. The SPDS-based method of feature extraction is suitable for recognizing broad scope of features with detailed information. The method is also suitable for identifying the global relationships among features and is capable of incorporating the context dependency of feature extraction.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.366-368
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• 2007

In this paper, the Time-varying Failure Rate (TFR) of power distribution system equipment is extracted from the recorded failure data of Korea Electric Power Corporation (KEPCO). For TFR extraction, it is used that the fault data accumulated by KEPCO during 10 years. The TFR is approximated to bathtub curve using the exponential (random failure) and Weibull (aging failure) distribution function. In addition, Kaplan-Meier estimation is applied to TFR extraction because of incomplete failure data of KEPCO. Finally, Probability plot and regression analysis is applied. It is presented that the extracted TFR is more effective and useful than Mean Failure Rate (MFR) through the comparison between TFR and MFR.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.7
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• pp.549-556
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• 2001

Heat flow characteristics during hot water extraction process was studied experimentally. Data were taken at various outlet port type for the fixed inlet port type, inlet-outlet temperature differences and mass flow rates. In this study, the temperature distribution in a storage tank and an outlet temperature were measured to predict a flow pattern in the storage tank, and a hot water extraction efficiency was analysed with respect to the variables dominating a extraction process. Experimental results show that the extraction efficiency is high in a low flow rate in case of using modified distributor I(MDI) as a outlet port type.

• The Korean Journal of Food And Nutrition
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• v.13 no.1
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• pp.45-52
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• 2000

In order to select the optimum ethanol concentration for extraction of volatile components in cinnamon, the dried cinnamon was extracted with water and 30∼90% ethanol. The volatile components of cinnamon extracts were isolated by the simultaneous distillation extraction method using Likens and Nickerson's extraction apparatus, and analyzed by GC-MS. In cinnamon bark powder 45 components were detected and 21 components were identified. The major component of cinnamon bark powder was cinnamic aldehyde. In water extract of cinnamon, volatile components were not extracted sufficiently. The volatile components of cinnamon were increased with the increment of ethanol concentraction upto 70%. The volatile component of 70% ethanol extract showed similar pattern and amount to cinnamon bark powder. But in 90% ethanol extracts, the number and amount of volatile component were reduced. The above data suggested that 70% ethanol was the most effective solvent for volatile components extraction of cinnamon.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.113-124
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• 2022

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.