• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1115-1127
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• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.32-38
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• 2018

Studies on classification of national roads have been continued, but there is little research on the classification of urban arterial roads. Due to the increase of traffic volume, urban arterial roads do not perform well as main roads. In this paper, the function of urban arterial road was established by using cluster analysis using traffic characteristics. Traffic characteristics such as traffic volume, weekend coefficient and speed coefficient were used to establish the functions of 55 main arterial roads in Seoul. The results of this paper are compared with those of the method using AADT. The method using AADT classifies the characteristics according to the traffic volume of the whole lane. In this paper, however, the results are derived using the traffic volume per lane reflecting the actual traffic volume. In addition, the functional classification of the arterial roads in Seoul was compared with the results of this paper to verify that the traffic characteristics were reflected. As a result, the method presented in this paper is more effective in showing traffic characteristics than the current highway functional classification method, and the functional classification system will be helpful for road extension and planning design.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.499-517
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• 2008

In this paper, we propose an effective emotion feature extraction method for Korean and evaluate their availability in sentiment classification. Korean emotion features are expanded from several representative emotion words and they play an important role in building in an effective sentiment classification system. Firstly, synonym information of English word thesaurus is used to extract effective emotion features and then the extracted English emotion features are translated into Korean. To evaluate the extracted Korean emotion features, we represent each document using the extracted features and classify it using SVM(Support Vector Machine). In experimental results, the sentiment classification system using the extracted Korean emotion features obtained more improved performance(14.1%) than the system using content-words based features which have generally used in common text classification systems.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.333-336
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• 2001

The future industry, intangible assets, like expertise, customer satisfaction, and employee's volition and capability, create more company value than any other components. The company's outcome mostly depends on managing these intangible knowledge assets. Construction industry is trying to adapt knowledge management system to manage their knowledge assets, but Hey do not build up knowledge assets definition and knowledge assets classification as much as other industries do. Most researches related knowledge assets classification are not concentrated on construction industry so it is need to define knowledge assets and establish knowledge assets classification of construction based on construction characteristics and informations. With this research result, construction knowledge assets classification can be the basis of knowledge asscts evaluation and knowledge map for knowledge management system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.127-132
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• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• ICROS
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• v.18 no.1
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• pp.20-25
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• 2012

자연계에 존재하는 생물체는 오랜 시간에 걸쳐 지구의 가혹한 환경에 적응하면서 다듬어져온 최적화된 작품이다. 이러한 동 식물의 생체 특징 모방은 기존 기술의 한계를 돌파하면서 가장 활발한 연구 분야로 자리 잡고 있다. 생체모방 로봇은 생체모방 기술의 한 분야로써 곤충, 새, 물고기, 그리고 여타 동물들을 연구해 생명체의 우수한 특성을 로봇기술에 접목함으로써 기존의 로봇 시스템이 극복하지 못했던 수많은 난제 해결에 도전중이다. 생체모방 로봇 기술의 최신 동향을 크게 세 줄기로 나누면 실용화, 생체분석법의 다양화, 모방대상의 다양화로 분류될 수 있다. 이 글에서는 이상에서 언급한 생체모방 로봇의 최신 동향에 대해서 소개한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.119-122
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• 2003

멀티미디어의 시대가 도래함에 따라 셀 수도 없이 많은 영상매체들이 범람하고 있다. 과거에는 영상 매체들의 수는 셀 수 있는 대상으로 분류되는 소수의 보조 매체이었던 것에 반해 지금의 영상매체 특히 동영상을 이용한 전달방식은 과거의 인간 인지의 보조수단이 아닌 인지의 주류로 떠오르고 있는 실정이다. 이에 이 수많은 영상매체를 얼마나 빠르게 인지한 수 있는 가가 중요한 관점으로 떠오르고 있다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.315-316
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• 2000

유해한 작업 환경지역, 공장밀집 지역 또는 주변 주거 지역의 대기에서 발생하는 악취성분 및 휘발성유기화합물은 환경규제 물질로 분류되고 국내적으로도 이들의 검출 및 정확한 정량적 분석을 위한 연구가 활발하게 진행되고 있다. 일반적으로 이들 성분들은 대기중에 미량으로 존재하기 때문에 흡착관 및 canister등에 의한 시료의 포집을 행하고 이들 분석시료를 실험실에서 저온농축과정을 거쳐 GC/FID 혹은 GC/MS등을 사용하여 정량분석을 실시한다. (중략)