• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.598-600
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• 2001

본 논문에서는 양상 뮤 논리를 위한 속성 명세 패턴 연구를 통해 시제 논리에 대한 패턴 기반의 단일한 프레임워크를 제시한다. 본 연구에서는 Dwyer의 속성 명세 패턴 분류를 상태(S)와 행동(A)으로 세분화하고 이를 다시 강함(A)와 약함(E)으로 다시 세분했다. 이러한 의미 기반의 계층적 패턴 분류 체계를 통해 양상 뮤 논리의 속성 명세 패턴을 분석했으며 실제 모형 검사기에서 사용된 예제들의 패턴 분류에 적용했다. 그 결과 기존의 분류 체계보다 더 정확한 분류가 가능했을 뿐만 아니라, 속성 명세의 작성 및 이해가 용이하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.489-491
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• 2012

본 논문은 교육용 서비스로 이용 가능한 카드 인식 시스템을 제안한다. 사용자는 자유자재로 카드 등록 및 삭제가 가능하며 카드의 회전 및 크기 변화에도 강건한 인식을 보인다. 본 논문에서는 카드 템플릿의 형태 정보와 Histogram of Oriented Gradients를 특징점으로 이용한다. 또한 최종 분류기에서 계층적인 구조를 적용하여 보다 정확한 카드 검출 및 인식을 제안한다.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1045-1054
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• 2001

To improve classification accuracy in this paper, we proposed an algorithm for the chromosome image reconstruction in the image preprocessing part and also proposed the pattern classification method using the hierarchical multilayer neural network(HMNN) to classify the chromosome karyotype. It reconstructed chromosome images for twenty normal human chromosome by the image reconstruction algorithm. The four morphological and ten density feature parameters were extracted from the 920 reconstructed chromosome images. The each combined feature parameters of ten human chromosome images were used to learn HMNN and the rest of them were used to classify the chromosome images. The experimental results in this paper were composed to optimized HMNN and also obtained about 98.26％ to recognition ratio.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.197-205
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• 2002

The objectives of this Paper is to implement a diagnostic classifier of differential laryngeal diseases from acoustic signals acquired in a noisy room. For this Purpose, the voice signals of the vowel /a/ were collected from Patients in a soundproof chamber and got mixed with noise. Then, the acoustic Parameters were analyzed, and hierarchical neural networks were applied to the data classification. The classifier had a structure of five-step hierarchical neural networks. The first neural network classified the group into normal and benign or malign laryngeal disease cases. The second network classified the group into normal or benign laryngeal disease cases The following network distinguished polyp. nodule. Palsy from the benign laryngeal cases. Glottic cancer cases were discriminated into T1, T2. T3, T4 by the fourth and fifth networks All the neural networks were based on multilayer perceptron model which classified non-linear Patterns effectively and learned by an error back-propagation algorithm. We chose some acoustic Parameters for classification by investigating the distribution of laryngeal diseases and Pilot classification results of those Parameters derived from MDVP. The classifier was tested by using the chosen parameters to find the optimum ones. Then the networks were improved by including such Pre-Processing steps as linear and z-score transformation. Results showed that 90% of T1, 100% of T2-4 were correctly distinguished. On the other hand. 88.23% of vocal Polyps, 100% of normal cases. vocal nodules. and vocal cord Paralysis were classified from the data collected in a noisy room.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.247-250
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• 2004

본 논문에서는 간단한 사각형 특징과 계층적 분류기를 이용하여 실시간으로 얼굴을 검출하는 방법을 제안하고자 한다. 우리는 다섯 가지 형태의 기본적인 특징 모델을 바탕으로 20*20 크기의 훈련 영상에 적용하여 많은 초기 특징 집합을 구성하였다. AdaBoost(Adaptive Boosting) 알고리즘을 이용한 학습을 통하여 초기 특징 집합 중에서 얼굴 검출하는데 강인한 집합들만을 선택하였다. 제안된 알고리즘을 이용한 실제 실험에서 90% 이상의 높은 검출율을 확인하였고 초당 10프레임의 실시간 검출에도 성공하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.109-112
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• 2006

본 논문에서는 PCA에 의한 특징추출과 k-NN과 SVM에 기반을 계층구조의 분류기에 의한 유도전동기의 고장진단 알고리즘을 제안한다. 제안된 방법은 k-NN에 의해 선형적으로 분류 가능한 고장패턴을 분류한 후, 분류가 되지 않는 부분을 커널 함수에 의해 고차원 공간으로 입력패턴을 매핑한 후 SVM에 의해 고장을 진단하는 계층구조를 갖는다. 실험장치를 구축한 후, 다양한 부하에 대하여 몇몇의 전기적 고장과 기계적 고장 하에서 획득한 데이터를 이용하여 제안된 방법의 타당성을 검증한다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.414-423
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• 2009

Folksonomy, which is collaborative classification created by freely selected keywords, is one of the driving factors of the web 2.0. Folksonomy has advantage of being built at low cost while its weakness is lack of hierarchical or systematic structure in comparison with taxonomy. If we can build classifier that is able to classify web resources from collective intelligence in taxonomy, we can build taxonomy at low cost. In this paper, targeting folksonomy in Slashdot.org, we define a general model and show that collective intelligence, which can build classifier, really exists in folksonomy using a stability value. We suggest method that builds SVM classifier using stability that is result from this collective intelligence. The experiment shows that our proposed method managed to build taxonomy from folksonomy with high accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.678-680
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• 2023

본 논문은 정확하면서도 효율적인 한국어 문장 분류 기법에 대해서 논의한다. 최근 자연어처리 분야에서 사전 학습된 언어 모델(Pre-trained Language Models, PLM)은 미세조정(fine-tuning)을 통해 문장 분류 하위 작업(downstream task)에서 성공적인 결과를 보여주고 있다. 하지만, 이러한 미세조정은 하위 작업이 바뀔 때마다 사전 학습된 언어 모델의 전체 매개변수(model parameters)를 학습해야 한다는 단점을 갖고 있다. 본 논문에서는 이러한 문제를 해결할 수 있도록 도메인 적응기(domain adapter)를 활용한 한국어 문장 분류 프레임워크인 DAKS(Domain Adaptation-based Korean Sentence classification framework)를 제안한다. 해당 프레임워크는 학습되는 매개변수의 규모를 크게 줄임으로써 효율적인 성능을 보였다. 또한 문장 분류를 위한 특징(feature)으로써 한국어 사전학습 모델(KLUE-RoBERTa)의 다양한 은닉 계층 별 은닉 상태(hidden states)를 활용하였을 때 결과를 비교 분석하고 가장 적합한 은닉 계층을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.187-190
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• 2001

Due to the recent proliferation of the internet, it is broadly granted that the necessity of the automatic document categorization has been on the rise. Since it is a heavy time-consuming work and takes too much manpower to process and classify manually, we need a system that categorizes them automatically as their contents. In this paper, we propose the automatic E-mail response system that is based on 2 hierarchical document clustering methods. One is to get the final result from the classifier trained seperatly within each class, after clustering the whole documents into 3 groups so that the first classifier categorize the input documents as the corresponding group. The other method is that the system classifies the most distinct classes first as their similarity, successively. Neural networks have been adopted as classifiers, we have used dendrograms to show the hierarchical aspect of similarities between classes. The comparison among the performances of hierarchical and non-hierarchical classifiers tells us clustering methods have provided the classification efficiency.