• Cross-Cultural Studies
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• v.39
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• pp.485-499
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• 2015

Initially, the word 'usage' in the rule of Korean orthography was used to indicate the whole grammatical knowledge to separate between stems and inflectional affixes and nominals and case markers. Nowadays the word 'usage' in the rule of Korean orthograph is understood to indicate both 'usage' as the principles of the orthographic rule and 'grammar.' Even though 'usage' and 'grammar' can be understood as two different words, the discrepancy between them is not clear. In fact, if examining the rule of Korean orthography, it is not difficult to find that the principles of the orthography is written based on the grammar rules. Thus, the original principle is damaged because the rule of Korean orthography depends on the grammar rules too much. In addition, the rule of Korean orthography forces to change the grammar rules when describing them. Incorrect description of the grammar rules often causes the spelling mistakes. Therefore, it is necessary to divide two areas such as 'usage' and 'grammar' when dealing with 'the orthographic rules' and describing them.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.41-51
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• 2015

The purpose of this research is to investigate the relationship between job creation and ICT usage in service industries. This research is particularly important because job creation is one of the most critical issues for the country. With technology innovation and system integration, manufacturing industries do not create enough employment, therefore, the society relies on service industries for creating job opportunities. Until now, due to limitations, analysis of job creation in service industries through ICT usage has not been investigated in depth. Thus, development of an effective ICT support policy was impossible. In this research, we used seven methods to investigate overall relationship between ICT usage level and job creation. A rule of large numbers has been applied to reach conclusion. Through this research, the government can focus its investment on service industries where job creation rate is the highest. We can expect both reinforcement in competitiveness of our service industry and continuous creation in employment, eventually leading to a virtuous cycle.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.321-331
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• 2003

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• ETRI Journal
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• v.37 no.3
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• ETRI Journal
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• v.25 no.6
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• pp.423-436
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• 2003

This paper describes our design of a contents distribution framework that supports transparent distribution of digital contents on the Internet as well as copyright protection of participants in the contents distribution value chain. Copyright protection must ensure that participants in the distribution channel get the royalties due to them and that purchasers use the contents according to usage rules. It must also prevent illegal draining of digital contents. To design a contents distribution framework satisfying the above requirements, we first present four digital contents distribution models. On the basis of the suggested distribution models, we designed a contract system for distribution of royalties among participants in the contents distribution channel, a license mechanism for enforcement of contents usage to purchasers, and both a packaging mechanism and a secure client system for prevention of illegal draining of digital contents.

• Architectural research
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• v.18 no.4
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• pp.165-170
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• 2016

This study investigates the course of transformation of the capital city of the Korean Empire into a colonial city during the Japanese rule by focusing on state-owned lands at and near #193, 2nd jeongmok(丁目), Hwanggeum-jeong(黃金町) in Keijo(京城). The study reveals that although the colonial rulers had made it apparent that they acted in the benefit of the Korean Empire, in reality, they had taken dexterous and gradual steps to change the purpose of the lands in order to utilize them as desired. Briefly, the usage of the lands was changed several times from Daedong-gurakbu(大同俱樂部) to Gyeongseong Exposition(京城博覽會) and to Nongsanggongbu Office(農 商工部) up until the Japan-Korea Annexation Treaty. Following this, the lands were bestowed upon the pro-Japanese, including Guijokhoigwan(貴族會館), as a means of Japan's assimilation policy. The changes in the usage of the buildings on the lands and the land use show how the rulers' intentions were reflected in the space of the ruled.