• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.322-327
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• 2003

The water level of a steam generator of pressurized light water nuclear Power generator is known as a subject whose control is difficult because of a shrinking and swelling effect that is been mutually contradictory in a variation of feed water. In this paper, a neural network model selects first coordinative controller by a inappropriate gain of two PI controllers and the selected controller＇s gain is tuned by a fuzzy self-tuner. Model inputs consist of the water level, the feed water, and the stream flow. One controller of both coupling controllers whose gain is handled firstly is decided based upon above data. The proposed method can analyze patterns of signals using the characteristic of neural networks and select one controller that needs to be tuned through the observed result in this paper. If one controller between both the water level controller and the feed water controller is selected by the neural network model then a gain of the PI controller is suitably tuned by the fuzzy self-tuner. Rules of the fuzzy self-tuner drew from the pattern of input and output data. In the summary, the goal of this Paper is to select the suitable controller and tune the control gain of the selected controller suitably through such two processes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2083-2090
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• 2008

In this paper, we propose a method for control of temperature and the direction of wind in an air-cooler using thermal images and fuzzy inference rules in order to achieve energy saving. In a simulation for controlling temperature, a thermal image is transformed to a color distribution image of $300{\times}400$ size to analyze the thermal image. A color distribution image is composed of R, G and B values haying temperature values of Red, Magenta, Yellow, Green, Cyan and Blue. Each color has a temperature value from $24.0^{\circ}C$ to $27.0^{\circ}C$ and a color distribution image is classified into height hierarchies from level 1 to level 10. The classified hierarchies have their peculiar color distributions and temperature values are assigned to each level by temperature values of the peculiar colors. The process for controlling overall balance of temperature and the direction of wind in an indoor space is as follows. Fuzzy membership functions are designed by the direction of wind, duration time, and temperature and height values of a color distribution image to calculate the strength of wind. After then, the strength of wind is calculated by membership values of membership functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1588-1593
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• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.949-963
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• 2017

This study present an efficient way of operating drainage pump station as part of nonstructural measures for reducing urban flood damage. The water level in the drainage pump station was forecast using Neuro-Fuzzy and then operation rule of the drainage pump station was determined applying the genetic algorithm method based on the predicted inner water level. In order to reflect the topographical characteristics of the drainage area when constructing the Neuro-Fuzzy model, the model considering spatial parameters was developed. Also, the model was applied a penalty type of genetic algorithm so as to prevent repeated stops and operations while lowering my highest water level. The applicability of the development model for the five drainage pump stations in the Mapo drainage area was verified. It is considered to be able to effectively manage urban drainage facilities in the development of these operating rules.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.125-134
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• 2000

In this paper, we proposed a new circuit construction method that reduced the number of CMOS devices of singleton fuzzy controller(SFC) through the proposing a new membership function circuit(MFC) which uses the language variable selecting and the coefficient selecting circuit. According to the range of input values, we can choose the language variables beforehand which will be used in the inference. So we proposed the new MFC which generates the only necessary language variables. Also, we removed all rules of which adapting degree of their antecedents is zero through proposing the coefficient selecting circuit which beforehand selects the coefficients which will influence the inference result. Though this method, we simplified the structure of SFC and reduced the size of hardware. And to solve the problem in the current mode with respect to the restriction of the fan-out number, voltage-input and current-out membership function circuits are constituted of operational transconductance amplifiers. A membership function circuit which includes the language variable selecting circuit, a minimum operation circuit we implemented by current mode CMOS devices. As a result of applying proposed method, total numbers of blocks and devices wave decreased. If the number of variables and antecedents are getting larger, this method is more efficient.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.226-232
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• 2016

The research for the improvement in math and science scores is active by the brain exercises, stress reliefs, and emotion sensitized illuminations. This principle is based on the following facts that the most effective brain turns are supported with the circumstances not only when the brain wave should keep stability and comfort in science criticism, but also when minimized stress and comfortable illumination should be adjusted in solving math problem. In this paper, in order to effectively learn mathematics and science, the most optimized simulating tests in learning conditions are conducted by using a stress relief. However, depending on the users' tastes, the effectiveness on favorite music or colors therapy have no convergency but many differentiations. Therefore, in this paper, in order to solve this problem, the proposed optimal illumination and music therapy treatment using fuzzy inference method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.73-83
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• 2004

With increasing numbers of vehicles on restricted roads, It happens that we have much wasted time and decreased average car speed. This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, franc volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart electro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules md neural network as a preprocessing. Also, we developed an Intelligent PLC(Programmable Logic Controller) for real time traffic forecasting as a postprocesing about unexpectable conditions. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive franc light system does not consider coordinating green time.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1401-1408
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• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.