• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.88-95
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• 2017

As the safety fields are expanding to a variety of industrial fields, safety technology has been developed by convergence between industrial safety fields such as mechanics, ergonomics, electronics, chemistry, construction, and information science. As the technology convergence is facilitating recently advanced safety technology, it is important to explore the trends of safety technology for understanding which industrial technologies have been integrated thus far. For studying the trends of technology, the patent is considered one of the useful sources that has provided the ample information of new technology. The patent has been also used to identify the patterns of technology convergence through various quantitative methods. In this respect, this study aims to identify the convergence patterns and fields of safety technology using association rule mining(ARM)-based patent co-classification(co-class) analysis. The patent co-class data is especially useful for constructing convergence network between technological fields. Through linkages between technological fields, the core and hub classes of convergence network are explored to provide insight into the fields of safety technology. As the representative method for analyzing patent co-class network, the ARM is used to find the likelihood of co-occurrence of patent classes and the ARM network is presented to visualize the convergence network of safety technology. As a result, we find three major convergence fields of safety technology: working safety, medical safety, and vehicle safety.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.59-65
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• 2002

Recently, lots of researchers work focused on the intrusion detection system. Pattern matching technique is commonly used to detect the intrusion in the system, However, the method requires a lot of time to match between systems rule and inputted packet data. This paper proposes a new intrusion detection system based on the pattern matching technique. Proposed system reduces the required time for pattern matching by using classified system rule. The classified rule is implemented with a general tree for efficient pattern matching. Thereby, proposed system could perform network intrusion detection efficiently.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.59-65
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• 2002

Recently, lots of researchers work focused on the intrusion detection system. Pattern matching technique is commonly used to detect the intrusion in the system, However, the method requires a lot of time to match between systems rule and inputted packet data. This paper proposes a new intrusion detection system based on the pattern matching technique. Proposed system reduces the required time for pattern matching by using classified system rule. The classified rule is implemented with a general tree for efficient pattern matching. Thereby, proposed system could perform network intrusion detection efficiently.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.26-33
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• 2006

The potential needs as well as visible needs of customer should be considered in order to research and analyze of the customer data. The methods to analyze customer data is classified into customer segmentation, clustering analysis model, forecasting customer response probability model, analysis of the customer break rate model and new customer analysis model by the purpose. In this study, we developed the CW-CLV (Correlation Weight Customer Lifetime Value)method that used AHP(Analytic Hierarchy Process)rule for enhance the reliability of customer data and quantitative analysis of the customer segmentation, based on CLV(Customer Lifetime Value). We suggest to new variables and methodology from determined CW-CLV coefficients, because all of companies respect to the diversified customers classification and complexity of consumers needs. Finally, we unfolded any company's scheduling added new methodology using simulation and leaded conclusion about the new methodology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.515-522
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• 2015

Through the correspondence works with IEC in the smart grid fields and power IT fields, we set up the interpretation work procedure and defined the work rule for correspondence by analyzing the work results. In addition, we suggest cases for discussion of terms and definitions in the IEC and analyze them and then propose a matrix classification system for standardization to solve the cases for discussion. The matrix classification system with 3-axes of classification has been applied to newly emerging terminologies followed by smart gird. We drew the usefulness in search of terms in application fields and showed the cases of applying the matrix classification. The IEC Electropedia classification standard is unclear and the classification is mixed with principle, application and product areas. We proposed a new working group in IEC TC1 for research on the matrix classification system and then TC 1 decided to organize a new WG titled in the "IEV structure and supporting tools".

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1337-1339
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• 2003

In this article, a new remote sensing image fusion model based on modified Kohonen networks is given. And a new fusion rule based on modified voting rule was established. Select Shaoxing City as the study site, located at Zhejiang Province, P.R.China. The fusion experiment between Landsat TM data (30m) and IRS-C Pan data (5.8m) was performed using the given fusion method. The fusion results show that the new method can gain better result in apply ing to the lower hill area, and the whole classification accuracy was 10% higher than the basic Kohonen method. The confusion between the woodlands and the waterbodies was also diminished.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.120.6-120
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• 2002

This paper presents an application of rule based expert system to GDS. Expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that normally require the abilities of human experts. Expert system can be classified as synthesis expert system, classification expert system or combination of these. The Expert system of GDS belongs to the synthesis type of expert system. GDS is a graphic design environment that automates complex and time consuming processes in the design phase of the grating production. There are methodical procedures in the design phase of gratings and each process needs the know-how of a skillful designer. User has to know...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3855-3867
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• 2022

Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network-based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.79-89
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• 2004

Since the amount of E-mail messages has increased , we need a new technique for efficient e-mail classification. E-mail classifications are grouped into two classes: binary classification, multi-classification. The current binary classification methods are mostly spm mail classification methods which are based on rule driven, bayesian, SVM, etc. The current multi- classification methods are based on clustering which groups e-mails by similarity. In this paper, we propose a novel method for e-mail classification. It combines the automatic category generation method based on the vector model and the dynamic category hierarchy construction method. This method can multi-classify e-mail automatically and manage a large amount of e-mail efficiently. In addition, this method increases the search accuracy by dynamic reclassification of e-mails.