• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.29-38
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• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.05a
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• pp.265-268
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• 2004

최근 디지털 지식기반 사회에 대응하는 교육의 형태로 e-Learning이 교육적 대안으로 급부상하면서, 시스템의 상호 운영성 및 컨텐츠 명세, 활용을 지원하기 위한 표준화에 따른 연구가 국내외에서 급속도로 확산되고 있다. 특히, 국제표준기관에서 제시한 e-Learning 개발 환경을 위한 Learning Technology Standard Architecture(LTSA)와 Sharable Content Object Reference Model(SCORM)을 제 정하여 컨텐츠의 사용과 상호 호환을 가능하게 함으로써 e-Learning의 효율성을 증대시키고 산업 시장의 확장을 이룰 수 있다. 또한, 현재 많은 교육관련 업체에서는 SCORM 체계를 기반으로 한 학습 컨텐츠들을 개발하여 제공하고 있다. 따라서, 본 논문에서는 국제 표준 기술인 SCORM을 기반으로 개발된 학습 컨텐츠를 체계적으로 지원하기 위해 컨텐츠 관리 시스템 개발에 대한 기술을 정의하고, 다양한 관점의 컨텐츠 메타 데이터를 식별, 분류함으로써 컨텐츠의 생성과 저장, 검색 나아가 형상관리를 위한 기본 정보로 이용 가능하다. 또한 이들 메타 데이터를 기반으로 한 학습 컨텐츠 관리 시스템의 프로토타이핑을 제시함으로써 재사용성과 유지보수성 향상을 통해 컨텐츠 개발의 용이성과 품질 및 생산성을 높일 수 있다.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.27-34
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• 2010

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.637-639
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• 2021

인공지능 기반의 생활폐기물의 인식 및 선별에서, 선별 정확도의 저하는 인식 대상의 형태적 다양성과 학습데이터 부족 및 불균등성에 기인한다. 본 연구에서는 비전 인공지능 기반의 효과적인 폐기물 선별을 위한 인식 시스템 및 감독학습 기반의 인공지능 학습 기법을 제안한다. 생활폐기물 중 순환자원적 가치가 높은 CAN, PET, 그리고 이와 형상적으로 유사한 폐기물에 대해 본 연구에서 제안된 시스템에서 물체원형 및 훼손된 형태의 총 18 종 이미지 데이터를 대상으로, 감독학습기반의 인공지능 모델 제작에서 최적의 데이터 레이블링을 위한 분류체계를 제시한다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.287-292
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we confirmed advantages/disadvantages of four algorithms and identified application methods of few algorithms.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.49-56
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• 2011

Because urban areas change rapidly, it is necessary to reflect urban changes in a digital map database in a timely manner. To address these issues, LiDAR data was used to detect changes in urban area buildings. The purpose of this study is to detect object-based building change using LiDAR data and existing digital maps, and classify change types. In the study, we classified change type using overlay and shape comparison with building layer of the digital maps and point-based extracted building outline from the LiDAR data. When applying the overlay method, we were able to increase the accuracy and objectivity of the change detection process throughout an adaptive threshold applied to each object. In the experiments, it was demonstrated that classifying and detecting changes in urban areas using the proposed method can provide superior classification accuracy compared with the existing methodology.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.278-282
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• 2008

U-국토 및 U-도시의 건설 및 관리를 위해 정밀 국토공간정보의 수요가 점증하고 있고 보다 신속한 갱신이 요구되고 있는 가운데 유비쿼터스 기술들과 융복합된 u-GIS기술에 대한 요구 또한 증가하는 추세다. 특히 건설 분야에 있어서의 이러한 수요를 보다 심층적으로 파악하기 위해, 건설공사 표준품셈 등을 이용하여 건설공사 공종/공정을 분류하고 이에 대한 수요의 우선순위를 도출하는 연구를 수행하였다. 본 논문은 u-GIS 기술을 필요로 하는 건설 현장에 위치/형상 정보 수요에 대한 조사, 분석을 통해 건설 인력, 자재, 장비의 u-GIS 기술의 적용 우선순위를 도출하는 것이다.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.25-32
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• 2016

With the change and development of modem architecture, architectural configurations are increasingly diversified and irregular. However, the building configurations without proper seismic considerations may cause severe damages under earthquake loads. Therefore, it is necessary to establish and implement more properly classified, specific and advanced conceptual seismic design strategies. This study explores the relationship between building configurations and seismic performance by adopting several horizontal building configurations with various re-entrant corners. For the clear comparison of five different horizontal configuration models, almost aspects of structural properties are equalized. The equivalent static analyses are conducted with the aim of understanding the characteristics of various re-entrant comers under standard earthquake loads. The seismic advantages of regular configuration model are clearly approved and the structural weak points at the re-entrant comers are investigated numerically and graphically.