• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.63-70
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• 1990

This paper presents a new temporal reasoning scheme based on explicit expression of time intervals. The proposed scheme deals with the general problem of temporal knowledge representation and temporal reasoning and may be used in rule-based systems and qualitative models. Time intervals, not time points, are defined in terms of orders and/or numbers in a quantity space. As a result, the system behavior is represented in the form of partially ordered networks. Such explicit and qualitative description of temporal quantities enables both reduction of ambiguity and parsimonious used of temporal information. Based on the proposed temporal reasoning scheme, a new rule-based qualitative simulation system is being built and evaluated.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.263-270
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• 2002

Most conventional systems of fire fighting control in a ship have been based on rule-based system in which expert knowledges are expressed with production rules. Renewing and adding of rules is needed continuously for the improvement of the system capability in an already build-up system and such adding and renewing procedures could hinder users from fluent utilization of a system. The author proposes an advanced fire fighting control intelligent system (A-FFIS) using rule-based and carte-based reasoning by clustering to implement conventional hybrid system (H-FFIS). Compared with H-FFIS, new approach with A-FFIS shows that the system proposed here improves fire detection rate and reduces fire detection time.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: 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 gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.560-574
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• 2011

In this paper, we propose a hybrid method of Machine Learning (ML) algorithm and a rule-based algorithm to implement a lightweight Named Entity (NE) extraction system for Korean SMS text. NE extraction from Korean SMS text is a challenging theme due to the resource limitation on a mobile phone, corruptions in input text, need for extension to include personal information stored in a mobile phone, and sparsity of training data. The proposed hybrid method retaining the advantages of statistical ML and rule-based algorithms provides fully-automated procedures for the combination of ML approaches and their correction rules using a threshold-based soft decision function. The proposed method is applied to Korean SMS texts to extract person's names as well as location names which are key information in personal appointment management system. Our proposed system achieved 80.53% in F-measure in this domain, superior to those of the conventional ML approaches.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.60-69
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• 2010

Lookup-Table (LUT) based fuzzy controller for obstacle avoidance enhances operations faster in multiple obstacles environment. An LUT based fuzzy controller with Positive/Negative (P/N) fuzzy rule base consisting of 18 rules was introduced in our paper$^1$ and this paper shows a 50-rule P/N fuzzy controller for enhancing performance in obstacle avoidance. As a rule, the more rules are necessary, the more buffers are required. This paper suggests LUT sharing method in order to reduce LUT buffer size without significant degradation of performance. The LUT sharing method makes buffer size independent of the whole fuzzy system's complexity. Simulation using MSRDS(MicroSoft Robotics Developer Studio) evaluates the proposed method, and in order to investigate its performance, experiments are carried out to Pioneer P3-DX in the LabVIEW environment. The simulation and experiments show little difference between the fully valued LUT-based method and the LUT sharing method in operation times. On the other hand, LUT sharing method reduced its buffer size by about 95% of full valued LUT-based design.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1791-1800
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• 2012

This paper presents effective design schemes for a photovoltaic (PV) and battery hybrid system that includes state-of-the-art technologies such as maximum power point tracking scheme for PV arrays, an effective charging/discharging circuit for batteries, and grid-interfacing power inverters. Compared to commonly-used PV systems, the proposed configuration has more flexibility and autonomy in controlling individual components of the PV-battery hybrid system. This paper also proposes an intelligent coordination scheme for the components of the PV-battery hybrid system to improve the efficiency of renewable energy resources and peak-load management. The proposed algorithm is based on a rule-based expert system that has excellent capability to optimize multi-objective functions. The proposed configuration and algorithms are investigated via switching-level simulation studies of the PV-battery hybrid system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.106-112
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• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skiful engineers. grinding operations include a large number of functional parameters since there are several ways of coping with ginding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop the other is the quantitative method which utilizes the experimental data obtained by sensor. But they are all difficult to accomplish from the grinding trouble-shooting system The reason is that grinding troubles are not accomplish from the grinding trouble-shooting system,. The rason is that grinding troubles are not easily controlled in the quantitative method and therefore trouble-shooting has mainly relied on the knoledge of skiful engineers. Thus there is an important issue of how a grinding touble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper basic strategy to develop the grinding database by taking rule-based model which is strongly depended upon experience and intuition is described.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.315-323
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inpired 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 nechanism. The DNA coding method has no limitation in expressing the produlation the rule of L-system. Evolutionary algotithms motivated by Darwinaian 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 one step ahead prediction of Mackey-Glass time series, Sunspot data and KOSPI data.