• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2178-2198
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• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.502-509
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• 2013

In Information age, The academic liberal art computer education course set up goals to promote computer literacy and develop the ability to cope with changes in information society and improve productivity and national competitiveness. In this paper, we analyze on discovering of decisive variable and satisfaction index to have a influence on computer education on university students. As a preprocessing course, the proposed method selects optimum variable using correlation based feature selection(CFS) of machine learning tool based on Java and we calculate weighted value for each variable and then, we generate the optimal variable using weighted value based on fuzzy decision making method. we proposed Fuzzy decision making method in analysis of the academic liberal art computer education satisfaction index data and checked the accuracy of the satisfaction evaluation by using recall and precision.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.1-8
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• 2009

In this paper, we propose a home medical system that integrates a self-diagnosing disease classification system and a tele-consulting system by communication technology. The proposed disease classification system supports to self-diagnose the health condition based on oriental medical science using fuzzy neural network algorithm. The prepared database includes 72 different diseases and their associated symptoms based on a famous medical science book "Dong-eui-bo-gam". The proposed system extracts three most prospective diseases from user's symptoms by analyzing disease database with fuzzy neural network technology. Technically, user's symptoms are used as an input vector and the clustering algorithm based upon a fuzzy neural network is performed. The degree of fuzzy membership is computed for each probable cluster and the system infers the three most prospective diseases with their degree of membership. Such information should be sent to medical doctors via our tele-consulting system module. Finally a user can take an appropriate consultation via video images by a medical doctor. Oriental medical doctors verified the accuracy of disease diagnosing ability and the efficacy of overall system's plausibility in the real world.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.745-749
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• 1998

Mathematical morphology (MM) has been introduced as a powerful tool for studying the geometrical properties of images, MM is a good approach to digital image processing , which is based on the shape feature. The MM operators such as dilation, erosion, closing and opening have been applied successfully to image noise reduction. The MM filters can easily filter the noise when the noise factors are known. However it is very difficult to reduce the noise when images are ambiguous, because the boundary between the noise and object is vague. In this paper, we propose a new method to reduce noise from ambiguous images by using Fuzzy Mathematical Morphology (FMM) operators. Performance evaluation via simulations show that the FMM filters efficiently reduce the image noise. Furthermore, the FMM filters show a good performance compared with the conventional filters.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.89-102
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• 2013

The paper presents a fuzzy based impulse noise filter for both gray scale and color images. The proposed approach is based on the technique of boundary discriminative noise detection. The algorithm is a multi-step process comprising detection, filtering and color correction stages. The detection procedure classifies the pixels as corrupted and uncorrupted by computing decision boundaries, which are fuzzified to improve the outputs obtained. In the case of color images, a correction term is added by examining the interactions between the color components for further improvement. Quantitative and qualitative analysis, performed on standard gray scale and color image, shows improved performance of the proposed technique over existing state-of-the-art algorithms in terms of Peak Signal to Noise Ratio (PSNR) and color difference metrics. The analysis proves the applicability of the proposed algorithm to random valued impulse noise.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.211-214
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• 1996

This paper propose a method for pattern recogniton using spectrum analyzer and fuzzy ARTMAP. Contour sequences obtained from 2-D planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The Fourier transform of contour sequence and spectrum analyzer are used as a means of feature selection and data reduction. The three dimensional spectral feature vectors are extracted by spectrum analyzer from the FFT spectrum. These Spectral feature vectors are invariant to shape translation, rotation, and scale transformations. The fuzzy ARTMAP neural network which is combined with two fuzzy ART modules is trained and tested with these feature vectors. The experiments include 4 aircrafts and 4 industrial parts recognition process are presented to illustrate the high performance of this proposed method in the ion problems of noisv shapes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• Journal of KIISE:Information Networking
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• v.31 no.3
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• pp.289-297
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• 2004

State-of-the-art wireless communication networks allow dynamic relocation of mobile terminals. A location management mechanism is required to keep track of a mobile terminal for delivering incoming calls. In this paper, we propose a fuzzy logic-based selective paging method to reduce paging cost. In the proposed fuzzy logic-based location management method, the location update uses the area-based method that uses direction-based together with movement-based methods, and the location search uses the fuzzy logic-based selective paging method based on the mobility information of mobile terminals. A partial candidate paging area is selected by fuzzy control rules, then the fuzzy logic-based selective paging method pages only the cells within the partial candidate paging area. The performance of proposed fuzzy logic-based location management method is to be evaluated by both an analytical model and a simulation, and is compared with those of LA and BVP methods. From these evaluation results, we know that the proposed fuzzy logic-based location management method provide better performance than other location management methods.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.3-8
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• 2009

In an effort to improve speech recognition, we investigated the effect of the number of clusters. In usual LBG clustering, the number of codebook clusters is doubled on each bifurcation and hence cannot be chosen arbitrarily in a natural way. To have the number of clusters at our control, we combined adaptive resonance theory (ART2) with LBG and perform the clustering in two stages. The codebook thus formed was used in subsequent processing of fuzzy vector quantization (FVQ) and HMM for speech recognition tests. Compared to conventional LBG, our method was shown to reduce the best recognition error rate by 0${\sim$}0.9% depending on the vocabulary size. The result also showed that between 400 and 800 would be the optimal number of clusters in the limit of small and large vocabulary speech recognitions of isolated words, respectively.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.99-100
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• 2016

본 연구는 경제적 가치가 있는 돈, 상품 등을 입자로 대체하여, 관람객의 행동에 대해 보상을 제공하는 "3D interactive reward system"을 만들었다. 관람객의 움직임과 속도에 따라 보상물의 종류, 생성량을 변화시켜 보상심리를 자극함으로써 보다 관람객의 관심과 적극적인 참여를 유도하고 동시에 자연스럽게 가상의 Fuzzy현상을 체험하고 소통할 수 있도록 시도하였다.