• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.10-19
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• 1999

Cellular Automata Neural Systems(CANS) are neural networks based on biological development and evolution. Each neuron of CANS has local connection and acts as a form of pulse according to the dynamics of the chaotic neuron. CANS are generated from initial cells according to the CA rule. In the previous study, to obtain the useful ability of CANS, we make the pattern of initial cells evolve. However, it is impossible to represent all solution space, so we propose an evolving method of CA rule to overcome this defect in this paper. DNA coding has the redundancy and overlapping of gene and is apt for the representation of the rule. In this paper, we show the general expression of CA rule and propose translation method from DNA code to CA rule. The effectiveness of the proposed scheme was verified by applying it to the navigation problem of autonomous mobile robot.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.569-577
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• 2014

The various services are required to user in time and space. It is important to provide suitable service to user according to user's circumstance. Therefore it is need to provide services to user through mining by latest information of user activity and service history. In this paper we propose a mining method to search association rule using service history based on spatiotemporal information and service ontology. In this method, we find the associative service pattern using level-cross tree on service ontology. The proposed method is to be a basic research to find the service pattern to provide high quality service to user according to season, location and age under the same context.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3061-3080
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• 2012

To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.

• Journal of the Korean Institute of Educational Facilities
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• v.10 no.3
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• pp.27-36
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• 2003

This paper aims to analyse facilities and characteristics of management for 21 model schools' open education system. So, we analyzed the class management and the methods of open education, teacher's mind, the shape and form of study group and we investigated teachers' satisfaction rate about the physical space. As a result, it is considered that the change for structure of learning space unit is needed because of the limitation in standard class size by the rule for construction. Considering the decrease of real using space by the space of learning materials or learning furniture, it should enlarge the structure of learning space unit or decrease the number of students. And to use multipurpose space practically as a place of study, it need that the multiple support of study program for teachers by government, support in course of study, giving training opportunity to teachers, distribution of personal management.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose 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.

• MALSORI
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• no.64
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• pp.137-153
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• 2007

The pronunciation lexicon of a continuous speech recognition system should contain enough pronunciation variations to be used for building a search space large enough to contain a correct path, whereas the size of the pronunciation lexicon needs to be constrained for effective decoding and lower perplexities. This paper describes a procedure for selecting pronunciation variations to be included in the lexicon based on the frequencies of the corresponding phonetic rules observed in the training corpus. Likelihood of a phonetic rule's application is estimated using the observation frequency of the rule and is used to control the construction of a pronunciation lexicon. Experiments with various pronunciation lexica show that the proposed method is helpful to improve the speech recognition performance.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.252-254
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• 1992

This paper proposes a new algorithm that finds fuzzy control law of the system in which little knowledge has been known. In view or conventional fuzzy method, making control law needs the sense and the knowledge of the system which are provided by expert. But fuzzy control using proposed algorithm needs no expert for hating control law. After construction of the 1st order approximated ARMA model using input-output pairs, new defuzzification method is applied. The deduced rule is stored in fuzzy input space and updated by the proposed algorithm adaptively. To show the validity and effectiveness of proposed control method. simulation result is presented.