• Journal of the Korea Convergence Society
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• v.5 no.1
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• pp.35-41
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• 2014

An automatic speech recognition system is one of the popular research problems. There are many research groups working in this field for different language including Japanese. Japanese vowel recognition is one of important parts in the Japanese speech recognition system. The vowel classification system with the Mamdani fuzzy inference system was developed in this research. We tested our system on the blind test data set collected from one male native Japanese speaker and four male non-native Japanese speakers. All subjects in the blind test data set were not the same subjects in the training data set. We found out that the classification rate from the training data set is 95.0 %. In the speaker-independent experiments, the classification rate from the native speaker is around 70.0 %, whereas that from the non-native speakers is around 80.5 %.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.103-108
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• 2008

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.187-194
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• 1999

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2017.10a
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• pp.35-36
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• 2017

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.297-303
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• 2009

In this paper, we propose the biomedical spectral pattern classification techniques by the fusion scheme based on the SpPCA and MLP in extended feature space. A conventional PCA technique for the dimension reduction has the problem that it can't find an optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique in extended space which use the local patterns rather than whole patterns. In the classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, biomedical spectral patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.545-551
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• 2003

Henry System which is a traditional fingerprint classification model is difficult to apply to a modem Automatic Fingerprint Identification System (AFIS). To tackle this problem, this study is to apply algorithm for an An Ensemble Fingerprint Classroom System using changes of gradient of ridge in order to improve precise joining speed of a large volume of database. The existing classification system, Henry System, is useful in a captured fingerprint image of core point and delta point using paper and ink. However, the Henry System is unapplicable in modem Automatic Fingerprint Identification System (AFIS) because of problems such as size of input sensor and way of input. This study is to suggest an Ensemble Fingerprint Classroom System which can classify 5 basic patterns of Henry System in uncaptured delta image using changes of gradient of ridge. The proposed fingerprint classification technique will make an improvement of precise joining speed by reducing data volume.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.610-618
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• 2021

This thesis is based on the theory of multiple intelligences proposed by the american educator and psychologist Dr.Gardner. According to the definition and classification of children's intelligence development games by predecessors, 6 types of intelligence development suitable for children aged 3 to 6 are summarized games, fill in the questionnaire to understand children's personal preferences, the purpose is to understand whether children aged 3 to 6 have a preference for intelligent development games and whether the preference will be affected by gender and age, and to understand the reality of children aged 3 to 6 Preferences and intellectual development needs provide a factual basis for more scientifically launching intelligent development games.

• Annual Conference of KIPS
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• 2009.11a
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• pp.319-320
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• 2009

본 논문은 Open Quotient 정보와 Fundamental Frequency 정보를 이용한 성별 분류에 관한 것이다. 기존의 대표적인 성별 분류 특징정보로 Fundamental Frequency가 있으나, Fundamental Frequency 정보로는 이용하여 분류하는 데에 중점을 두어왔으나 이 정보는 노인 혹은 어린이에 대해서는 성별 분류 특징이 어렵다는 단점이 있었다. 한편 본 논문에서 제안하는 방법은 Open Quotient와 Fundamental Frequency의 연령대에 따른 차별 정보를 이용하여 학습시켜 성별분류를 보다 나은 성능으로 분류할 수 있다.