• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.298-300
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• 2001

본 논문에서는 구조적응 자기구성 지도 모델을 사용하여 인간 행동의 성별을 분류하는 인식기를 제안하였다. 26명의 사람이 '화난 상태' 혹은 '보통 상태'의 두가지 정서 하에서 '문 두드리기', '손 흔들기', '물건 들어올리기'의 세가지 동작을 수행하는 동안, 행위자 관절점의 속도나 위치 정보로부터 성별을 분류하였다. 또한 SASOM의 성능 비교 분석을 위하여 전통적인 SOM, 다층 퍼셉트론과 거의 두 가지 결합 모델, SASOM와 의사결정트리 결합 모델, 단일 의사 결정트리, $textsc{k}$-최근접 이웃 등의 인식기를 구현하여 성능을 비교분석 하였다. 실험 결과 SASOM 분류기가 가장 높은 이식률을 보였으며 분류기로서 유용함을 알 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.643-645
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• 2022

많은 온라인 쇼핑몰에서 색상 기반 필터링 서비스나 추천 시스템을 제공하지만, 수동 분류는 많은 시간이 들고 오류 위험이 있다. 본 연구의 실험에서는 먼저 분석할 의류 이미지를 실루엣 분석으로 수행한 경우와 수행하지 않는 경우의 k-평균 군집화 알고리즘으로 가장 우세한 색상 군집의 중심값을 도출하는데, 만약 군집 개수가 2개 이상이면 보다 큰 군집의 중심값만을 고려한다. 이 중심값을 이용해 사전 학습한 k-최근접 이웃 알고리즘으로 색상 클래스를 분류한다. 실험 결과 실루엣 분석을 수행하지 않은 k-평균 군집화 알고리즘을 사용한 분류 방식이 정확도와 수행 시간 모두 매우 준수하였으나, 배경색이 존재하여 의류 색 분석에 영향을 줄 수 있는 경우 잘못 분류한다는 문제도 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.292-294
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• 2005

대사증후군은 당뇨병, 고혈압, 복부 비만, 고지혈증 등의 질병이 한 개인에게 동시에 발현하는 것을 말한다. 미국에서는 $25\%$ 이상의 성인이 대사성 증후군인 것으로 알려져 있으며, 경제 여건의 향상 및 식생활 습관의 변화와 함께 최근 우리나라에서도 심각한 문제가 되고 있다. 한편 불확실성의 처리를 위해 많이 사용되고 있는 베이지안 네트워크는 사람이 분석 가능한 확률 기반의 모델로 최근 의학 분야에서 지식 발견, 데이터 마이닝을 위한 도구로 유용하게 사용되고 있다. 본 논문에 서 는 대사증후군을 예측하는 문제를 다루며, 베이지안 네트워크와 의학 지식을 이용한 대사증후군의 예측 모델을 제안한다. 제안하는 모델을 통해 1993년의 데이터를 가지고 1995년의 상태를 예측하는 분류 실험을 수행하였으며, 실험 결과 다층 신경망, k-최근접 이웃 등의 분류기 보다 높은 $81.5\%$의 예측율을 보였다.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.63-70
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• 2018

Biomedical signal measurement technology using images has been developed, and researches on respiration signal measurement technology for maintaining life have been continuously carried out. The existing technology measured respiratory signals through a thermal imaging camera that measures heat emitted from a person's body. In addition, research was conducted to measure respiration rate by analyzing human chest movement in real time. However, the image processing using the infrared thermal image may be difficult to detect the respiratory organ due to the external environmental factors (temperature change, noise, etc.), and thus the accuracy of the measurement of the respiration rate is low.In this study, the images were acquired using visible light and infrared thermal camera to enhance the area of the respiratory tract. Then, based on the two images, features of the respiratory tract region are extracted through processes such as face recognition and image matching. The pattern of the respiratory signal is classified through the k-nearest neighbor classifier, which is one of the statistical classification methods. The respiration rate was calculated according to the characteristics of the classified patterns and the possibility of breathing rate measurement was verified by analyzing the measured respiration rate with the actual respiration rate.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.733-735
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• 2003

DNA Microarray 기술은 유전자의 발현여부를 매우 빠르게 검사할 수 있는 도구이며 각종 질병의 발생여부를 예측하기 위한 정보를 제공한다. 유전자 발현 데이터로부터 암의 발생 여부를 예측하기 위해서는 기존의 접근방법과 다른 기계학습 기법이 요구된다. 일반적으로 샘플의 개수가 극히 적은 반면에 특징의 개수는 수천에서 수만 개가 존재하기 때문에 문제의 특성에 맞는 분류기의 구조를 결정하는 것이 매우 어려운 일이기 때문이다. 진화 신경망은 신경망의 구조와 가중치를 동시에 학습하며 사용자는 각 개체의 적합도를 평가할 수 있는 방법만 제공해 주면된다. 특히 신경망의 구조를 사전에 고정하지 않아도 되는 장점이 있기 때문에 전문적인 지식이 없는 사용자라도 이용가능하다. 대장암 데이터에 대한 실험결과 제안하는 분류기 모델이 다층 퍼셉트론, SVM (support vector machine), 최근접 이웃 방법에 비해 향상된 성능을 보였다.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.80-88
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• 2006

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED illuminator and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of hot rolled steel strip are used to develop KNN (k- Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.379-383
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• 2005

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED light and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of cold roll steel strips are used to develop KNN (k-Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.