• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.165-177
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• 2000

A fuzzy controller need membership functions and the control rules depend on heuristic knowledge of expertises entirely. On account of, it is possible that a desired performance of a fuzzy controller can not be guaranteed or easily degraded under some circumstances such as a change of plant parameter which exporters do not considered. Therefore, in this paper we tried to increase the controller's efficiency by adjusting the control rules and the parameters of the membership functions by using a genetic algorithm. We also proposed the Self-Organizing Fuzzy Controller which uses the Fusion Method in order to minimize the number of control rules and to construct the intuitive controller. For validation of the proposed algorithm, we design the Autonomous Guided Vehicle Controller, then apply to variant condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1593-1598
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• 2012

In this paper, we propose an intelligent signal control method based on fuzzy logic applicable in real time. We design membership functions to model occupied time and the number of vehicles for each lane. A priority for each signal phase is computed by the popular Max-Min fuzzy inference based on control rules and membership degrees of prepared two functions at any given time. A tie breaking scheme is considering weighted sum of the rate of occupied time per number of vehicles in that block and the standard deviation of these blocks. Only a signal phase with the highest priority is opened and all others are closed and the duration of the phase opening is computed proportional to the rate of number of weighting vehicles in that signal per all weighted vehicles. The simulation result shows that the proposed method is more efficient than the static control in all simulation conditions in $2{\times}3$ experimental designs with the number of vehicles in intersection and congestion degrees that have all three levels.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.317-326
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• 1995

The oater level control system of the steam generator in a pressurized water reactor and its control Problems are analysed. In this work a stable control strategy Particularly during low Power operation based on the fuzzy control method is studied. The control strategy employs substitutional information using the bypass valve opening instead of incorrectly measured signal at the low How rate as the fuzzy variable of the flow rate during low power operation, and includes the flexible scale adjusting method for fast response at a large transient. A self-tuning algorithm based on the control performance and the descent method is also suggested for tuning the membership function scale. It gives a practical way to tune the controller under real operation. Simulation was carried out on the Compact Nuclear Simulator set up at Korea Atomic Energy Research Institute and its result showed the good performance of the controller and effectiveness of its tuning.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.179-187
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• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.19-28
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• 2000

It is not so easy to control the wheeled mobile robot because of some causes like non-holonomic constraints. To overcome these problems, a controller that PD system is combined with fuzzy process is composed of several steps that have each separate algorithm and niche search algorithm and immune algorithm is applied partly. Output term set is changed by search that is performed to get optimal elements and then the rule base is also reformed. The fitness for the altered system is estimated and the surplus elements are removed. After the adjustment of output term set and rule base is finished, input and output membership functions is tuned.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.853-862
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• 2003

With the data base subject of first grade paper test of information handling technician, We proposed special method of evaluating learning ability directivity to judge that student can understand the contents of each chapter exactly or not, using assigned function and fuzzy deduction in this thesis. Using fuzzy logic, the proposed method of evaluating learning ability is dividing the presenting frequency of setting questions for examination about the subject of database into three rank and we can define this as the important. We applied the fuzzy assigned rate about the number of times of studying through the important of studying and the fuzzy assigned rate about formative evaluation to each of nine fuzzy deduction theories and than evaluated comprehension rate of learning. With the fuzzy grade about learning comprehension of each chapter and assigned rate about the score of generalized evaluation; We applied these two thing to the deduction rule of fuzzy and made it as defuzzifier and finally evaluated learning. We made that the result of eventual evaluating learning is very useful for learners to diagnosis learned contents by themselves and also it can be great material to judge that learners can get the goal of learning or not synthetically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.815-825
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• 2007

The existing web-based self-directed learning systems are in short for the ability of learning skills assessment. Even worse, hey only give test scores as an indicate for test skills, which is also not a good measure for learning skills assessment and makes it difficult to assess learning skills objectively and to present clear assessment criterion. In this paper, we proposed an improved self-directed learning system using fuzzy logic, which can be controlled by learners themselves and helps to evaluate their on learning process. We also implemented the system on the written examination of Engineer Information Processing. The purposed system lust calculates membership functions of learning tine, learning frequency, testing time, and test score. Using them the final membership functions of learning and test skills are calculated and presented in a graphical, i.e. mon understandable, way to user. The purposed system helps learners to assess their achievement and to plan future schedule, and the survey result on the students used the system also supports that.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.799-807
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• 2004

In this paper, we deal with an emotion recognition method using facial images and speech signal. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Emotion recognition using the facial expression is performed by using a multi-resolution analysis based on the discrete wavelet transform. And then, the feature vectors are extracted from the linear discriminant analysis method. On the other hand, the emotion recognition from speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and then the final recognition is obtained from a multi-decision making scheme.