• Communications of Mathematical Education
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• v.31 no.1
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• pp.71-84
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• 2017

Taylor series has a complicated structure comprising of various concepts in college major mathematics. This subject is a strong tool which has usefulness and applications not only in calculus, analysis, and complex analysis but also in physics, engineering etc., and other study. However, students have difficulties in understanding mathematical structure of Taylor series convergence correctly. In this study, after classifying students' mathematical characteristic into three categories, we use structural image of Taylor series convergence which associated with mathematical structure and operation acted on that structure. Thus, we try to analyze the understanding of Taylor series convergence and present the results of this study.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.251-258
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• 2003

A support vector machine (SVM) is supposed to provide a good generalization performance, but the actual performance of a actually implemented SVM is often far from the theoretically expected level. This is largely because the implementation is based on an approximated algorithm, due to the high complexity of time and space. To improve this limitation, we propose ensemble of SVMs by using Bagging (bootstrap aggregating) and Boosting. By a Bagging stage each individual SVM is trained independently using randomly chosen training samples via a bootstrap technique. By a Boosting stage an individual SVM is trained by choosing training samples according to their probability distribution. The probability distribution is updated by the error of independent classifiers, and the process is iterated. After the training stage, they are aggregated to make a collective decision in several ways, such ai majority voting, the LSE(least squares estimation) -based weighting, and double layer hierarchical combining. The simulation results for IRIS data classification, the hand-written digit recognition and Face detection show that the proposed SVM ensembles greatly outperforms a single SVM in terms of classification accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.685-694
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• 1993

A progressive image transmission (PIT) scheme based on the classified transform vector quantization (CVQ) technique using the lapped orthogonal transform (LOT) and human visual system (HVS) weighting is proposed in this paper. Conventional block transform coding of images using DCT produces in general undesirable block-artifacts at low bit rates. In this paper, image blocks are transformed using the LOT and classified into four classes based on their structural properties and further divided adaptively into subvectors depending on the LOT coefficient statistics with HVS weighting to improve the reconstructed image quality by adaptive bit allocation. The subvectors are vector quantized and transmitted progressively. Coding tests using computer simulations show that the LOT/CVQ based PIT of images is a effective coding scheme. The results are also compared with those obtained using PIT/DCTVQ. The LOT/CVQ based PIT reduces the block-artifacts significantly.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1345-1346
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• 2015

본 논문에서는 보행자 및 배경 이미지로부터 HOG-PCA 특징을 추출하고 다항식 기반 RBFNNs(Radial Basis Function Neural Network) 패턴분류기과 최적화 알고리즘을 이용하여 보행자를 검출하는 시스템 설계를 제안한다. 입력 영상으로부터 보행자를 검출하기 위해 전처리 과정에서 HOG(Histogram of oriented gradient) 알고리즘을 통해 특징을 추출한다. 추출된 특징은 고차원이므로 패턴분류기 분류 시 많은 연산과 처리속도가 따른다. 이를 개선하고자 PCA (Principal Components Analysis)을 사용하여 저차원으로의 차원 축소한다. 본 논문에서 제안하는 분류기는 pRBFNNs 패턴분류기의 효율적인 학습을 위해 최적화 알고리즘인 PSO(Particle Swarm Optimization)을 사용하여 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시킨다. 사용된 데이터로는 보행자 검출에 널리 사용되는 INRIA2005_person data set에서 보행자와 배경 영상을 각각 1200장을 학습 데이터, 검증 데이터로 구성하여 분류기를 설계하고 테스트 이미지를 설계된 최적의 분류기를 이용하여 보행자를 검출하고 검출률을 확인한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.11-19
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• 2020

This study seeks to measure inter-industry convergence systematically and quantitatively using structural holes theory. ICT industries were classified into ICT manufacturing and ICT service then efficiency and constraints were calculated using input-output tables. The results of the study revealed both ICT industries have very high information and control benefits in the process of industrial convergence, proving to be key industries with competitive advantage. Further implications were presented based on comparative analysis between ICT manufacturing and service and trend analysis over the past 15 years.

• Information and Communications Magazine
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• v.29 no.5
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• pp.62-70
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• 2012

대량의 인명 피해가 발생하는 도심 내 급속 확산형 재난 상황이 전 세계적으로 발생함에 따라, 재난 대응을 위한 응급 운송 및 시뮬레이션 연구에 대한 관심이 증가하고 있다. 본 연구에서는 최근 다양한 방법으로 연구되고 있는 재난 대응 응급 운송 및 시뮬레이션 연구의 동향을 구조적인 내용 분석 기법을 이용하여 정리하였다. 우선, 재난 발생 전/후에 발생하는 응급 운송에 관한 문제들을 분류하고 문제의 특징을 분석하였다. 그리고 응급 자원의 최적할당 및 피해 지역 사람들의 효과적인 이송에 관한 의사 결정을 지원하기 위한 시뮬레이션 연구를 정리하였다. 최종적으로, 이러한 구조적 내용 분석을 바탕으로 자율 적응형 재난 대응 체계 구축을 위한 미래의 연구방향을 제시하였다.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.215-220
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• 2012

This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.473-475
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• 1999

본 논문은 구조적 매칭 접근 방법이 회전과 변이에 얼마나 효과적인가를 보여준다. 이는 지문에서 보여주는 특징적 요소들 (코아, 델타 그리고 분기점) 사이의 거리와 각도들을 이용한다. 실제로 이 접근 방법은 회전과 변이가 허용된 한 입력 지문에 대해서 짧은 시간 내에 간단한 연산만으로도 높은 매칭 성공률을 보여준다. 또한 현 자동화된 지문인식 시스템에서처럼 한 입력지문에 대해서 데이터베이스에서 최종 유력한 지문 10개를 검색하는 것을 목적으로 한다. 표본은 600명의 서로 다른 사람으로부터 채취된 지문을 4가지로 (궁상문, 우제상문, 좌제상문, 와상문) 분류한 각각에 대해서 약 98%의 매칭 성공률을 가진다. 실험은 150MHz, 586 퍼스널 컴퓨터에서 실행되었다.