• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.677-687
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• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.814-816
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• 2004

본 논문에서는 웨이블릿을 이용한 알고리즘으로 얼굴을 검출하고 검출된 얼굴을 움직이는 Pan-Tilt 카메라상에서 추적하는 방법을 제안하고자 한다. 우리는 얼굴 검출을 위해 다섯 종류의 간단한 웨이블릿을 사용하여 특징을 추출하였고 AdaBoost(Adaptive Boosting) 알고리즘을 이용한 계층적 분류기를 통하여 추출된 특징들 중에서 얼굴을 검출하는데 강인한 특징들만을 모았다. 이렇게 만들어진 특징집합들을 이용하여 입력받은 영상에서 초당 20프레임의 실시간으로 얼굴을 검출하였고 영상에서 얼굴 위치와 Pan-Tilt 카메라 위치를 계산하여 실시간으로 움직임을 추적하는데 성공하였다.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.481-491
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• 2014

The power production using renewable energy is more important because of a limited amount of fossil fuel and the problem of global warming. A concentrative photovoltaic system comes into the spotlight with high energy production, since the rate of power production using solar energy is proliferated. These systems, however, need to sophisticated tracking methods to give the high power production. In this paper, we propose a hierarchical tracking system using modular Bayesian networks and a naive Bayes classifier. The Bayesian networks can respond flexibly in uncertain situations and can be designed by domain knowledge even when the data are not enough. Bayesian network modules infer the weather states which are classified into nine classes. Then, naive Bayes classifier selects the most effective method considering inferred weather states and the system makes a decision using the rules. We collected real weather data for the experiments and the average accuracy of the proposed method is 93.9%. In addition, comparing the photovoltaic efficiency with the pinhole camera system results in improved performance of about 16.58%.

• Journal of radiological science and technology
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• v.26 no.1
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• pp.71-82
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• 2003

In this paper, I proposed a classifier of liver cirrhotic step using T1-weighted MRI(magnetic resonance imaging) and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were obtained in Pusan National University Hospital from June 2001 to december 2001. And the number of data was 46. We extracted liver region and nodule region from T1-weighted MR liver image. Then objective interpretation classifier of liver cirrhotic steps in T1-weighted MR liver images. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier teamed through error back-propagation algorithm. A classifying result shows that recognition rate of normal is 100%, 1type is 82.3%, 2type is 86.7%, 3type is 83.7%. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered, this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Journal of the Korean Society of Child Welfare
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• no.59
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• pp.183-208
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• 2017

The purpose of this study was to identify groups depending on the change trajectory of child maltreatment in childhood and early adolescence. For this study, the data from waves two through six (2011-2015) of the Korean Children and Youth Panel Survey (KCYPS) were used. Participants included first-grade (n=2,300) and fourth-grade (n=2,325) elementary school students. A latent class growth model (LCGM) using Mplus 7.21 was adopted to classify the types of developmental trajectories of child maltreatment. The main results were as follows: First, in physical abuse, childhood from the second to the sixth grades was classified into four groups: decreased, low maintenance, increased, and no maltreatment type. In addition, early adolescence from the fifth grade of elementary school to the third grade of middle school was also classified into the same types. Second, in emotional abuse, childhood was classified into three groups: decreased, increased, and no maltreatment type. Early adolescence was classified into four groups: decreased, low maintenance, increased, and no maltreatment type. Third, in neglect, childhood was classified into four groups: maintenance, low decreasing, low increasing, and no maltreatment type. Early adolescence was classified into three groups: maintenance, low increasing, and no maltreatment type. According to the change of child maltreatment by developmental period, physical abuse continued from childhood to early adolescence, whereas emotional abuse and neglect increased in early adolescence compared to childhood. This study is meaningful in classifying latent classes depending on maltreatment types. Theoretical and practical implications were suggested based on the study results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.321-324
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• 2004

본 논문에서는 유도전동기 고장진단을 위하여 계층적인 하이브리드 뉴럴네트웍을 제안하였다. 시스템의 입출력 데이터에 근거하여 패턴을 분류하고자 할 때 직접적인 분류가 어렵거나 성능이 좋지 않을 경우 적절한 방법을 통하여 변환을 하거나 또는 패턴 분류기의 특성에 맞도록 변환하여 패턴 분류 성능을 향상하는 등 단계별 변환 및 분류 기법을 이용하였다. 제안된 방법에서는 실험에 의해 측정된 전류값을 주기별로 주성분분석(PCA) 기법을 이용하여 입력차원을 축소한 후 이를 조건부 FCM으로 방사기저함수의 초기치를 최적화하여 학습을 하였다. 이는 주성분분석이 가지는 특성을 이용하여 데이터의 특징을 나누었으며 이를 뉴럴네트웍의 학습 기능을 이용하여 모델의 최종 성능을 개선하는 것이다. 각각의 알고리즘이 가지는 특징을 활용하면서도 단점을 계층적으로 보안하여 유도 전동기 고장 진단 성능을 개선하였다. 이를 실제 계측된 유도전동기 데이터를 이용하여 제안된 방법의 유용성을 보이고자 한다.

• ETRI Journal
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• v.8 no.2
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• pp.75-82
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• 1986

소프트웨어의 개발환경에 관련하여 개발과정, work methods, 업무구조를 논하였다. TDX-1 소프트웨어를 성격에 따른 분류와 기능에 따른 분류로 대별하여 설명한 후 중점적으로 교환기(Exchange) 소프트웨어에 대하여 그 특성과 구조를 논하였다. 특성에서는 계층분산 구조, 분산다중처리 등 4개의 특성을 논하였으며 구조에서는 프로세서의 구성, 각 프로세서의 기능, 논리적 구조로 나누어 설명하였다.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.10-18
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• 2010

Due to a large number of classes in Hangul character recognition, it is usual to use the six-type preclassification stage. After the preclassification, the first consonent, vowel, and last consonent can be classified separately. Though each of three components has a few of classes, classification errors occurs often due to shape similarity such as 'ㅔ' and 'ㅖ'. So this paper proposes a hierarchical recognition method which adopts multi-stage tree structures for each of 6-types. In addition, to reduce the interference among three components, the method uses the recognition results of first consonents and vowel as features of vowel classifier. The recognition accuracy for the test set of PHD08 database was 98.96%.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.555-558
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• 2001

본 논문에서는 인간 행동의 성별 인식문제를 해결하기 위해 여러 개의 전문가(expert) 신경망의 앙상블로 이루어진 결합 신경망 분류기를 제안한다. 하나는 여러 개의 modular 다층퍼셉트론을 계층형으로 결합한 모텔이고, 다른 하나는 modular 다층퍼셉트론들의 출력값을 의사결정트리로 결합하는 모델이다. 데이터 베이스는 남녀 각 13 명의 데이터로 이루어져 있고, 문 두드리기, 손 흔들기, 물건 들어올리기의 세 가지 동작을, 보통 상태 혹은 화난 상태하에서 10 회씩 반복 수행하여 저장하였다. 행위자의 움직임은 몸에 부착된 6 개의 적외선 센서를 사용하여 기록 되었으며, 2 차원 혹은 3 차원 속도 및 좌표가 그 특징값으로 사용되었다. 앙상블 분류기의 성능을 비교하기 위하여 단일 다층퍼셉트론, 의사결정트리, 자기구성지도 및 support vector machine 을 사용한 실험 결과를 보였다. 실험 결과, 신경망 앙상블 모델이 다른 전통적인 분류기 및 사람에 비하여 훨씬 우수한 성능을 보였음을 알 수 있었다.