• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.6-11
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• 2018

With the development of the Internet of Things technology, a system that ensures the self-adaptability of an environment that includes various IoT devices is attracting public attention. The rules for determining behavior rules in existing self-adaptation systems are based on the assumption of changes in system members and environment. However, in the IoT environment, flexibility is required to determine the behavior rules of various types of IoT devices that change in real time. In this paper, we propose a rule configuration in a self-adaptive system using SWRL based on OWL ontology. The self-adaptive system using the OWL - SWRL rule configuration has two advantages. The first is based on OWL ontology, so we can define the characteristics and behavior of various types of IoT devices as an integrated concept. The second is to define the concept of a rule as a specific language type, and to add, modify and delete a rule at any time as needed. Through the rule configuration in the adaptive system, we have shown that the rule defined in SWRL can provide flexibility and deeper concept expression function to adaptability to IoT environment.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.161-175
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• 2001

In this paper, a detection method of contradiction between input informations is proposed when the inference is processed in rule-based systems. The proposed method is accomplished by improving the label representation and the label management scheme in a conventional ATMS(Assumption-based Truth Maintenance System). The Proposed method also can represent and process input informations having uncertainty values.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1949-1954
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• 2005

One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy energy region method was proposed. In this approach, some partly stable controllers are designed by the computed torque method, and a switching rule is based on fuzzy energy regions. Design parameters related to boundary curves of fuzzy energy regions are optimized offline by a genetic algorithm (GA). In this paper, we discuss on parameters obtained by GA. The effectiveness of the switching fuzzy energy method is demonstrated with some simulations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.2 s.7
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• pp.1-11
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• 2005

Automatic tracking of vehicles is important to accurately estimate the vehicle speeds in video-based traffic measurement systems and to analyze traffic flows for road construction. This paper proposes a carefully designed rule-based tracking scheme that considers the possible cases that can be appeared in the video-based vehicle racking. The proposed scheme is fast and outperforms the Mean-Shift scheme in terms of accuracy. The accuracy and the speed of the scheme would be increased by combining it with color-based searching and Kalman filters.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.151-173
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• 2005

We developed a methodology of rule acquisition from texts such as Web pages which utilizes ontology in identification of rule components. We expect that the proposed methodology can reduce the bottleneck of rule acquisition and contribute to the utilization of rule based systems. As parts of our research, we designed an ontology for rule acquisition named OntoRule and proposed a rule acquisition methodology through OntoXRML which is an acquisition tool using OntoRule. Also, we evaluated our approach by calculating missed recommendations and wrong recommendations of rule components in rule acquisition experiments over three online bookstores.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.11a
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• pp.51-57
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• 2003

In operating distributed systems, proactive management is one of the major concerns for better quality of service and future capacity planning. In order to handle this management problem effectively, it is necessary to analyze performances of the distributed system and events generated by components in the system. This paper provides a rule-based event parsing engine for proactive management. Our event parsing engine uses object hooking-based and event-token approaches. The object hooking-based approach prepares new conditions and actions in Java classes and allows dynamically exchange them as hook objects in run time. The event-token approach allows the event parsing engine consider a proper sequence and relationship among events as an event token to trigger an action. We analyze the performance of our event parsing engine with two different implementations of rule structure; one is table-based and the other is tree-based.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2299-2308
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• 1997

While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.286-289
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• 1989

The purpose of this paper is to design a job scheduling algorithm utilizing intelligent control technique. Rulebase is built through the evaluation of rule-set scheduling. 24 scheduling rule-sets and meta-rules are employed. An appropriate scheduling rule-set is selected based on this rulebase and current manufacturing system status. Six criteria have been used to evaluate the performance of scheduling. The performance of sheduling is dependent on random breakdown of the major FMS components during simulation.