• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.119-124
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• 2014

In this paper, we propose the active rule system applying with emotional margin for power saving control. The proposed system in this paper is a part of the system which derives smart power saving by adjusting the illuminance using active rules within compromising the user's emotion. For this, we set the specific range of standard illuminance and the lower bound of user's emotional margin of illuminance based on measurements and analysis, and use these data in design of active rules. And then, we design and implement the active rule system using mobile agent. The mobile agent in the proposed system migrates to the destination sensor nodes, acquires and transmits sensor data according to the purpose and needs through the active rules, and directly executes the actions corresponding to the optional events(changed sensor data and/or time etc.). And then, we show the potential applicability of the proposed active rule system in various active sensor network applications through the interaction with the rule base and mobile sensor network middleware system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1154-1160
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• 1999

A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.63-73
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• 2003

Insurance system has a lot of decision factors which are affected by the kinds of insurance products. and has the features of many exceptions. Since in applying the product attributes to the current system the value definition through tables and the exception treatment logic (if then else) are used in parallel, the cost of a product change and a new product development becomes increased and the prompt market reaction is difficult. In this paper, we propose the well formed rule base system which makes data for the business logic of insurance attributes and discuss the benefit of application of this system to the real project.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.527-530
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• 1998

In this paper, a transformation-based English part of speech tagging system is designed and implemented. The tagging system tags raw corpus at first and the transformation rule correct the errors. Apart from traditional rule based tagging system, this system makes rules automatically. Using 60,000 words of corpus as a training corpus, the transformation rules are generated automatically by iterative training. The idea how to calculate positive effect of transformation and select transformation rules is proposed to generate more effective and correct transformations. In this paper, part of the Brown corpus and English text is used for experimental data. And the performance of transformation based tagging system is demonstrated by the calculation of accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.703-707
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• 1991

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for reaserch in the applications of fuzzy set theory. A key component of the fuzzy controller is a rule-based system which provides a linguistic description of control strategy. This strategy has the form of a collection of fuzzy conditional statements which are implemented and manipulated using fuzzy set theory. In this paper, we propose the rule-based controller for DC motor speed control. The result of performance compare with PID controller to verify the validity of proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.88-97
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• 2018

Chronic diseases management can be effectively achieved through early detection, continuous treatment, observation, and self-management, rather than a radar approach where patients are treated only when they visit a medical facility. However, previous studies have not been able to provide integrated chronic disease management services by considering generalized services such as hypertension and diabetes management, and difficult to expand and link to other services using only specific sensors or services. This paper proposes clinical rule flow model based on medical data analysis to provide personalized care for chronic disease management. Also, we implemented that as Rule-based Smart Healthcare System (RSHS). The proposed system executes chronic diseases management rules, manages events and delivers individualized knowledge information by user's request. The proposed system can be expanded into a variety of applications such as diet and exercise service in the future.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.100-107
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• 2002

The advanced bilateral control algorithm, which can enlarge a reflected force by combining force reflection and compliance control, greatly enhances workability in teleoperation. In this scheme the maximum boundaries of a compliance controller and a force reflection gain guaranteeing stability and good task performance greatly depend upon characteristics of a slave arm, a master arm, and an environment. These characteristics, however, are generally unknown in teleoperation. It is, therefore, very difficult to determine such maximum boundary of the gain. The paper presented a novel method for design of an advanced bilateral controller. The factors affecting task performance and stability in the advanced bilateral controller were analyzed and a design guideline was presented. The neurofuzzy compliance model (NFCM)-based bilateral control proposed herein is an algorithm designed to automatically determine the suitable compliance for a given task or environment. The NFCM, composed of a fuzzy logic controller (FLC) and a rule-learning mechanism, is used as a compliance controller. The FLC generates compliant motions according to contact forces. The rule-learning mechanism, which is based upon the reinforcement learning algorithm, trains the rule-base of the FLC until the given task is done successfully. Since the scheme allows the use of large force reflection gain, it can assure good task performance. Moreover, the scheme does not require any priori knowledge on a slave arm dynamics, a slave arm controller and an environment, and thus, it can be easily applied to the control of any telerobot systems. Through a series of experiments effectiveness of the proposed algorithm has been verified.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1329-1332
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• 1987

This paper describes a design of the expert system for diagnosis of abnormal gait patients. This system makes the rule-based representation that can easily extend the knowledge-base and naturally represent the uncertainty, and the inference engine that uses forward chaining which covers the reasoning from the first condition to the goal. The results of inferring various maladies using this system are as follows: 1) In cases of progressive muscular dystrophy, cerebral vascular accident, peripheral neuropathic lesion and peroneal nerve injury, the result of inference is the same as that of medical specialists' with 100% accuracy. 2) In cases of Neuritis, Paralysis agitan and Brain tumor, the accuracy of inference is less than 50% compared to that of medical specialists. With above results, we decide that the rule-based representations of some maladies ard accurate relatively, but that the correction and the extention of some rules and some methods of problem solving are required in order to construct the complete expert system for diagnosis of abnormal gait patients.