• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2485-2492
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• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.

• Smart Structures and Systems
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• v.14 no.4
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• pp.679-697
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• 2014

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of $10^5$, $10^2$ and $10^0$ sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of $10^{-2}$, $10^{-1}$ and $10^0$ Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.277-280
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• 2010

There is a need to convert the old low- or medium-resolution satellite image-based thematic mapping to the high-resolution satellite image-based mapping of GSD 1m grade or lower. There is also a need to generate middle- or large-scale thematic maps of 1:5,000 or lower. In this study, the DEM and orthoimage is generated with the KOMPSAT-2 stereo image of Yuseong-gu, Daejeon Metropolitan City. By utilizing the orthoimage, automatic extraction experiments of land cover information are generated for buildings, roads and urban areas, raw land(agricultural land), mountains and forests, hydrosphere, grassland, and shadow. The experiment results show that it is possible to classify, in detail, for natural features such as the hydrosphere, mountains and forests, grassland, shadow, and raw land. While artificial features such as roads, buildings, and urban areas can be easily classified with automatic extraction, there are difficulties on detailed classifications along the boundaries. Further research should be performed on the automation methods using the conventional thematic maps and all sorts of geo-spatial information and mapping techniques in order to classify thematic information in detail.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.105-123
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• 1992

A study was made on the feature extraction for non-metallic one deposits and their related geology using the Remote Sensing and Airborne Radiometric data. The area chosen is around the Haenam area, where dickite and Quarzite mines are distributed in. The geology of the area consists mainly of Cretaceous volcanics and PreCambrian metamorphic. The methods applied are study on the reflectance characteristics of minerals and rocks sampled in the study area, and the feature extraction extraction of histogram normalized images for Landsat TM and Airborne Radiometric data, and finally evaluation of applicability of some useful pattern recognition techniques for regional lithological mapping. As a result, reflectances of non-metallic minerals are much higher than rock samples in the area. However, low grade dickites are slightly higher than rock samples, probably due to their greyish colour and also their textural features which may scatter the reflectance and may be capable of capturing much hychoryl ions. The reflectances of rock samples may depend on the degree of whiteness of samples. The outcrops or mine dumps in the study area were most effectively extracted on the histogram normalized image of TM Band 1, 2 and 3, due to their high reflectivity. The Masking technique using the above bands may be the most effective and the natural colour composite may provide some success as well. The colour composite image of PCA may also be effective in extracting geological features, and airborne radiometric data may be useful to some degree as an complementary tool.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.37-44
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• 2019

In this study, we propose the automatic extraction method of Rescue Requests from Drone Images. A central object is extracted from each image by using central object extraction method[7] before classification. A central object in an images are defined as a set of regions that is lined around center of the image and has significant texture distribution against its surrounding. In this case of artificial objects, edge of straight line is often found, and texture is regular and directive. However, natural object's case is not. Such characteristics are extracted using Edge direction histogram energy and texture Gabor energy. The Edge direction histogram energy calculated based on the direction of only non-circular edges. The texture Gabor energy is calculated based on the 24-dimension Gebor filter bank. Maximum and minimum energy along direction in Gabor filter dictionary is selected. Finally, the extracted rescue requestor object areas using the dominant features of the objects. Through experiments, we obtain accuracy of more than 75% for extraction method using each features.

• Journal of KIISE
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• v.44 no.6
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• pp.581-586
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• 2017

For automatic web scraping, unnecessary components such as menus and advertisements need to be removed from web pages and main contents should be extracted automatically. A content block tends to be located in the middle of a web page. In particular, Korean web documents rarely include metadata and have a complex design; a suitable method of content extraction is therefore needed. Existing content extraction algorithms use the textual and structural features of content blocks because processing visual features requires heavy computation for rendering and image processing. In this paper, we propose a new content extraction method using the tag positions in HTML as a quasi-visual feature. In addition, we develop a tag rank position, a type of tag position not affected by text length, and show that gradient boosting with the tag rank position is a very accurate content extraction method. The result of this paper shows that the content extraction method can be used to collect high-quality text data automatically from various web pages.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.569-579
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• 2013

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.585-592
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• 2005

One of the most important things in the researches on diseases is to attach more importance to prevention of a disease and preservation of health than to treatment of a disease, also to foods rather than to medicines. In this context, the most significant concern in examining a patient is to find the presence of disease, and, if any, to diaguose the type of disease, after which a pharmacotherapy is followed. In this paper, various diagnosis methods of Oriental medicines are discussed. And ocular inspection, the most important method among the 4 disease diagnoses of Oriental medicines, is studied. Observing a person's shape and color has been the major method for ocular inspection, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for ocular inspection. As the first stage, we applied the signal processing techniques to automatic feature extraction of faces for ocular inspection. Firstly, facial regions are extracted from the point of frontal view, which was followed by extraction of their features. The experiment applied to 20 persons showed that frontal face regions are perfectly extracted, as well as their features, such as eyes, eyebrows, noses and mouths. Future work will seek to address the issues of morphological operation for a few unfinished extraction results, such as combined hair and eyebrows.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.624-627
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• 2003

To achieve accurate and efficient extraction of the fractal feature, a progressive extraction method is developed. After establishing the boundaries of the targeted surface by enclosing it with internal and external covers, it determines the features of the surface by calculating the characteristics of such covers.