• Agribusiness and Information Management
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• v.4 no.2
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• pp.1-13
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• 2012

This study aims to subdivide consumers by attributes determined mainly by consumers of fresh ginseng. It is to compare and analyze the characteristics by cluster, and to deduce the implications on distribution and marketing. For this study, a survey was conducted targeting 250 consumers of fresh ginseng. The factors were deduced through performing the exploratory factor analysis on the results of the survey, and the consumers of fresh ginseng were classified through cluster analysis. As a result of the study, the attributes considered for the purchase of fresh ginseng were condensed to the three factors: physical characteristic factor, safety factor, and cultivation indication information factor. With these as the standard, the consumers of fresh ginseng were subdivided into the three clusters: safety-oriented consumption type, label-centered consumption type, and high involvement consumption type. It was found that there were differences in demographic characteristics and attributes considered for purchase of fresh ginseng by cluster analysis. This study suggests the implications for revitalization of the fresh ginseng industry by subdividing consumers of fresh ginseng and suggesting the characteristics by cluster.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.177-182
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• 2010

This paper proposes a new document clustering method using PCA and fuzzy association. The proposed method can represent an inherent structure of document clusters better since it select the cluster label and terms of representing cluster by semantic features based on PCA. Also it can improve the quality of document clustering because the clustered documents by using fuzzy association values distinguish well dissimilar documents in clusters. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.293-300
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• 2018

We propose a moving object extraction and tracking method for improvement of animal identification and tracking technology. First, we propose a method of merging separated moving objects into a moving object by using FCM (Fuzzy C-Means) clustering algorithm to solve the problem of moving object loss caused by moving object extraction process. In addition, we propose a method of extracting data from a moving object and a method of counting moving objects to determine the number of clusters in order to satisfy the conditions for performing FCM clustering algorithm. Then, we propose a method to continuously track merged moving objects. In the proposed method, color histograms are extracted from feature information of each moving object, and the histograms are continuously accumulated so as not to react sensitively to noise or changes, and the average is obtained and stored. Thereafter, when a plurality of moving objects are overlapped and separated, the stored color histogram is compared with each other to correctly recognize each moving object. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.239-246
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• 2010

This paper proposes a new document clustering method using NMF and fuzzy relationship. The proposed method can improve the quality of document clustering because the clustered documents by using fuzzy relation values between semantic features and terms to distinguish well dissimilar documents in clusters, the selected cluster label terms by using semantic features with NMF, which is used in document clustering, can represent an inherent structure of document set better. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Phonetics and Speech Sciences
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• v.7 no.2
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• pp.77-83
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• 2015

The aim of this paper is to investigate Japanese speakers' perception of Korean tense consonants. In a range of perceptual experiments Japanese participants were directed to label Korean stimuli using Japanese katakana characters. The analysis of the results showed a strong influence of Japanese phonology in the responses. Japanese perception of sokuon was increased, (1) when the tense consonants were in word medial position, (2) when tense consonants were other than /s/, (3) when the tense consonant followed voiceless consonants, (4) when the consonants were part of a cluster sharing their point of articulation, (5) when preceding vowel were other than /u/, (6) when following vowel were /u/. This result, showing preference for phonology, is in harmony with previous research on the Japanese sokuon perception using Japanese (Takeyasu 2009, Matsui 2011), and Italian (Tanaka & Kubozono 2008) stimuli.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.202-203
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• 2009

In this paper, system divide into the edge extraction, labeling, clustering, and South Korea's traditional color combination and identifying to identify Korea's traditional colors. Edge is extracted using Canny operator. And given the label, and clustering to ensure the quality of the cluster. Finally, we identify color harmony by organizing and comparing primary color with secondary color configuration table Korea traditional color.