• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.1
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• pp.50-59
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• 1987

So far a variety of RAM-based approaches including the Filtering Method have been suggested to shorten the rule seletion time in production systems, but this paper presents a somewhat different approach based on the use of CAM. This paper suggests a proper use of CAM bits respect to their characteristics and describes data stsuctures for basic Artificial Intelligence symbolic list processing, and finally compares the simulation results from the CAM-based approach to those from RAM-based approaches.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1235-1238
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• 2003

A rule-based CAPP(computer aided process planning) system is proposed in this research to develop integrated manufacturing process of machining and inspection using OMM(On-Machine Measurement) device. Generally workpiece composed of many primitive form features. This features are determined optimum inspection sequence by analyzing the feature information such as features-relationship, probe approach direction and etc. Proposed paper is more efficient method of CAIP(computer aided inspection planning) considered machining process

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.98-105
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• 2000

In this study, we developed rotating machine vibration condition monitoring system based upon Windows NT and DSP Board. Developed system includes signal analysis module, trend monitoring and simple diagnosis using threshold value. Trend analysis and report generation are offered with database management tool which was developed in MS-ACCESS environment. Post-processor, based upon Matlab, is developed for vibration signal analysis and fault detection using statistical pattern recognition scheme based upon Bayes discrimination rule and neural networks. Concerning to Bayes discrimination rule, the developed system contains the linear discrimination rule with common covariance matrices and the quadratic discrimination rule under different covariance matrices. Also the system contains k-nearest neighbor method to directly estimate a posterior probability of each class. The result of case studies with the data acquired from Pyung-tak LNG pump and experimental setup show that the system developed in this research is very effective and useful.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.633-634
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• 2006

Recently we started a development of the digital meister expert system for the product design supporting in manufacturing industry. Knowledge representation is of major importance in digital moister expert system. This rule-based expert system-knowledges are designed for a certain type of knowledge representation such as rules or logic. The way in which a rule-based expert system represents knowledge affects the development, efficiency, speed, and maintenance. Eventually, this digital moister system is used to the engineer in manufacturing industry for the process control, production management and system management. In this paper, we propose the digital moister system knowledge representation method for product design supporting in manufacturing industry and we present introduction and contents of rule-based knowledge representation supporting tool.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.73-82
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• 2018

We often experience complex presence of files within the compute environment. It could be an end-user's desktop environment or a professional part of file management. We would like to suggest one way to manage these different cases of duplicate files. It is a rule-based file management tool, and we want to use it as a tool to consolidate facility management log files. In this paper, we will describe the rule-based file management tools and show examples of how files are managed using them. We are interested in the management of the disaster environment and would like to apply this method to the management of log data related to facilities to be considered in the event of a disaster.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.3-8
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• 1997

Three new classification methods for multi temporal images are proposed. They are named as a likelihood addition method, a likelihood majority method and a Dempster-Shafer's rule method. Basic strategies using these methods are to calculate likelihoods for each temporal data and to combine obtained likelihoods for final classification. These three methods use different combining algorithms. From classification experiments, following results were obtained. The method based on Dempster-Shafer's rule of combination showed about 12% improvement of classification accuracies compared to a conventional method. This method needed about 16% more processing times than that of a conventional method. The other two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two methods are almost the same with that of a conventional method. Among the newly proposed three methods, the Dempster-Shafer's rule method showed the highest classification accuracies with more processing time than those of other methods.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.224-227
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• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• Journal of IKEEE
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• v.17 no.3
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• pp.332-338
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• 2013

BPEL is a standard executable language for specifying actions within business processes with Web services. BPEL workflow requires rule engine to describe application process in BPEL, as the requirements increase. It is needed to develop new BPEL's engine or modify BPEL's engine in order to add the functionality of rules to BPEL engines, but this method is not easy to implement and it requires very high cost. In this paper, we present an R4BPEL document, which uses the original BPEL grammar and includes a rule document. With this method, it is possible to build rule-based BPEL environment easily by adding web services with rules and R4BPEL document analyzer to the general-purpose Web services. In this paper, we campared a BPEL's document in proposed system with a BPEL's document in the existing system. And we demonstrated the simplicity of the rule-based system.