• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.521-524
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• 2002

This paper deals with the rule generation from data for control system and data mining using rough set. If the cores and reducts are searched for without consideration of the frequency of data belonging to the same equivalent class, the unnecessary attributes may not be discarded, and the resultant rules don't represent well the characteristics of the data. To improve this, we handle the inconsistent data with a probability measure defined by support, As a result the effect of uncertainty in knowledge reduction can be reduced to some extent. Also we construct the rule base in a hierarchical structure by applying core as the classification criteria at each level. If more than one core exist, the coverage degree is used to select an appropriate one among then to increase the classification rate. The proposed method gives more proper and effective rule base in compatibility and size. For some data mining example the simulations are performed to show the effectiveness of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.205-210
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• 2004

This paper deals with rule extraction from data using rough set theory. We construct the rule base in a hierarchical granulation structure by applying core as a classification criteria at each level. When more than one core exist, the coverage is used for the selection of an appropriate one among them to increase the classification rate and accuracy. In Addition, a probabilistic approach is suggested so that the partially useful information included in inconsistent data can be contributed to knowledge reduction in order to decrease the effect of the uncertainty or vagueness of data. As a result, the proposed method yields more proper and efficient rule base in compatability and size. The simulation result shows that it gives a good performance in spite of very simple rules and short conditionals.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.101-118
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• 2016

For the effective classification of complex subjects, a library classification scheme should adopt multiple division principles (or facets). Each of the multiple principles adopted for the division of complex subjects is sequentially applied at each stage of division. The order of application of these multiple principles during the process of division of complex subjects is called citation order. In order for a classification scheme to be consistent and logical, the citation order of division principles applied to classify complex subjects should be concrete and consistent. Especially, in case of enumerative classification system, decisions on citation order to represent complex subjects significantly affect the structure and organization of the classification system. There are basic principles and theoretical canons of the classification theory on the citation order and its application, but they cannot be applied solidly in the process of classification system development for practical reasons. Therefore, this paper first reviews previous works on classification theories regarding citation order, then explores the conditions and circumstances for the application of citation order.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.11-18
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• 2009

As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.

• Korean Journal of Child Studies
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• v.18 no.2
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• pp.73-85
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• 1997

The purpose of the present study was to examine the role of semantic and syntactic knowledge in the first language acquisition of Korean classifiers. The elicited classifiers production test(EPT) was conducted to 105 children aged from 2 to 7. EPT consisted of 16 classifiers and two items for each classifier. 32 items were divided into 2 major semantic features: animacy and inanimacy. The semantic features of inanimacy were subcategorized into 3 features such as neutral, shape and function. The results revealed that; 1) children produced the correct structure of classification from the very early age with correct word order of the noun phrase showing early fundamental syntactic knowledge; 2) The earliest response pattern was to respond to all nouns in the same way using a neutral classifier showing no apparent semantic basis for their choice; 3) Children didn't show any preference for animate, shape, or function classifiers.

• Journal of The Korean Association For Science Education
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• v.25 no.6
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• pp.678-688
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• 2005

The main objective of this study was to investigate Korean university students' understanding of the structure of the atom based on a Lakatosian view. In this study, we examined twenty-three Korean university students' understandings of atomic structure using an open-ended questionnaire. The participants were all junior students majoring in chemistry education in Korea. The characteristics of students' understanding were categorized into three philosophical stances based on the classification criteria. Assertions were constructed concerning students' written descriptions of the development of scientific knowledge with respect to atomic structure: (a) characteristics of positivist response; (b) characteristics of transitional response; (c) characteristics of Lakatosian response; and (d) tendencies in students' responses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.

• New & Renewable Energy
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• v.7 no.1
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• pp.36-44
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• 2011

Technology is developed from the efficient interaction with other technology files while building up its own research field. This study analyzes the structure of solar cell research area and describes its paths of the technology development in terms of interdisciplinary diversity using the Co-Classification method during 1979-2009. As a results, 1,380 studies are determined as the interdisciplinary among the 2,605 studies. It shows that 52.98% of the solar cell researches have interdisciplinary relationships with two or more research fields. In addition, we show that the research area of solar cell technology is composed by Material Science, Multidisciplinary and Energy & Fuel, Physics, Applied, Chemistry, Physical from the Co-Classification matrix and network analysis. It means the complexity of the technological knowledge production increased with the concept of interdisciplinary. The results can be used for the planning of the efficient solar cell technology development.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.