• The Journal of the Korea Contents Association
• /
• v.18 no.8
• /
• pp.178-186
• /
• 2018

Subject classification of thesis units is essential to serve scholarly information deliverables. However, to date, there is a journal-based topic classification, and there are not many article-level subject classification services. In the case of academic papers among domestic works, subject classification can be a more important information because it can cover a larger area of service and can provide service by setting a range. However, the problem of classifying themes by field requires the hands of experts in various fields, and various methods of verification are needed to increase accuracy. In this paper, we try to classify topics using the unsupervised learning algorithm to find the correct answer in the unknown state and compare the results of the subject classification algorithms using the coherence and perplexity. The unsupervised learning algorithms are a well-known Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithm.

• The Korean Journal of Ecology
• /
• v.24 no.5
• /
• pp.267-271
• /
• 2001

A phytosociological study on the hierarchical classification system of the Rosetalia rugosae, developed at the coastal dunes in the cool-temperate region of Northeast Asia, was carried out. Currently, the Rosetalia rugosae is subordinated to the Rosetea multiflorae which is the highest rank of the mantle vegetation in Northeast Asia, however its hierarchical system is somewhat ambiguous. This study was accomplished by using the syntaxa and hierarchical system of the Rosetalia rugosae and Rosetea multiflorae, and by also using 197 homogeneous relevns of the Rosetalia rugosae in South Korea and Japan in terms of the Zbrich-Montpellier School. For the hierarchical analysis of the Rosetalia rugosae, the constancy, the frequency and the net contribution degree were evaluated. It is estimated that the Rosetalia rugosae and the Rosetea multiflorae are hardly related to reciprocally. Thus, the subordination of the Rosetalia rugosae to the Rosetea multiflorae is comparatively irrational. Accordingly, the syntaxonomical hierarchy of the Rosetalia rugosae must be reconsidered that is correspond to the Viticetea rotundifoliae of the warm-temperate coastal dune shrub vegetation.

• Journal of Internet of Things and Convergence
• /
• v.3 no.1
• /
• pp.9-11
• /
• 2017

Recently, with the widespread use of the Internet and mobile devices, the number of attacks by hackers using the network is increasing. When connecting a network, packets are exchanged and communicated, which includes various information. We analyze the information of these packets using hierarchical clustering analysis and classify normal and abnormal packets to detect attacks. With this analysis method, it will be possible to detect attacks by analyzing new packets.

• Journal of Information Technology Services
• /
• v.17 no.2
• /
• pp.165-177
• /
• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

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

• Proceedings of the KIEE Conference
• /
• 2008.11a
• /
• pp.360-362
• /
• 2008

Based on classification systems of power and IT standard dictionaries, scientific and technological standard, SPARK, power IT fields of IEC and organization units of corporations, we propose a new classification system for the standardization of power of terminologies. The classification system consists of a hierarchical structure with general classification, application fields and specific technologies while keeping the conventional matrix-type classification system. Interpretation work of the power of terminologies confirms that the proposed classification system is efficient.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.2
• /
• pp.35-44
• /
• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.521-524
• /
• 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
• /
• v.4 no.2
• /
• pp.205-210
• /
• 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 the Korean Institute of Telematics and Electronics B
• /
• v.31B no.1
• /
• pp.88-98
• /
• 1994

A new neural network architecture called the Parallel. Self-Organizing Hierarchical Neural Network (PSHNN) is presented. The new architecture involves a number of stages in which each stage can be a particular neural network (SNN). The experiments performed in comparison to multi-layered network with backpropagation training and indicated the superiority of the new architecture in the sense of classification accuracy, training time,parallelism.