• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.52-62
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• 2006

In a fuzzy control system to process fuzzy data in high-speed for intelligent systems, one of the important problems is the improvement of the execution speed in the fuzzy inference and defuzzification stages. Especially, it is more important to have high-speed operations in the consequent part and defuzzification stage. Therefore, in this paper, to improve the speedup of the fuzzy controllers for intelligent systems, we propose an integer line mapping algorithm using only integer addition to convert [0,1] real values in the fuzzy membership functions in the consequent part to integer grid pixels $(400{\times}30)$. This paper also shows a novel defuzzification algorithm without multiplications. Also we apply the proposed system to the truck backer-upper control system. As a result, this system shows a real-time very high speed fuzzy control as compared as the conventional methods. This system will be applied to the real-time high-speed intelligent systems such as robot arm control.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.14-22
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• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.92-102
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• 2004

Performance assessment was introduced to improve self-directed learning and method of assessment for differenced learning when the seventh educational curriculum was enforced. Written examinations often fail to properly assess students higher thinking abilities ad problem solving abilities. Performance assessment addresses this drawback and also allows normalization of class and school quality. However, performance assessment also has drawbacks that could lead to faulty assessment due to lack of fairness, reliability and validity of grading, ambiguity of grading standard etc. This study proposes a fuzzy performance assessment system to address the drawbacks of the conventional performance assessment. This paper presents in objective and reliable performance assesment method through fuzzy reasoning, design of fuzzy membership function. We define a fuzzy rule analyzing factor that influences in each sacred ground of performance assessment and accounts for the principle subject The proposed performance assessment method divides into three categories, namely, formation estimation subject estimation and design of membership function. Performance assessment result that is worked through fuzzy performance assessment system can reduce the burden of appraisal's fault and provide. We fair and reliable assessment results through grading that have correct standard mid consistency to students.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.262-268
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• 2010

In this paper, this study restores the distorted image to its original image by compensating for the distortion of image from a fixed-focus camera lens. The various developments and applications of the imaging devices and the image sensors used in a wide range of industries and expanded use, but due to the needs of the small size and light weight of the camera, the distortion from acquiring images of the distorted curvature of the lens tends to affect many. In particular, the three-dimensional imaging camera, each different distortion of left and right lens cause the degradation of three-dimensional sensitivity and left-right image distortion ratio. we approached the way of generalizing the approximate equations to restore each part of left-right camera images to the coordinators of the original images. The adaptive Neuro-Fuzzy Inference System is configured for it. This system is divided from each membership function and is inferred by 1st order Sugeno Fuzzy model. The result is that the compensated images close to the left, right original images. Using low-cost and compact imaging lens by which also determine the exact three-dimensional image-sensing capabilities and will be able to expect from this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1509-1513
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• 2003

The autopilot system of vessel is proposed to take service safety and security, to elevate service efficiency, to decrease labor and to improve working environment. Ultimate purpose of the proposed system is to minimize the number of crew by guaranteeing economical efficiency of shipping service. Recently, the research is being achieved to compensate various nonlinear parameters of vessel and apply it to course keeping control, track keeping control, roll-rudder stabilization, dynamic ship positioning and automatic mooring control etc. using optimizing control technique. Relation between rudder angle controlled by steering machine of vessel and ship-heading angle, and load condition of ship is nonlinear, which affects various parameters of shipping service. The speed and direction of waves, velocity and quantity of wind, which also cause the non-linearity of it. Therefore the autopilot system of ship requires the robust control algorithm can overcome various non-linearity. On this paper, we design the autopilot system of ship, which overcomes nonlinear Parameters and disturbance of it using Fuzzy Algorithm, evaluate the proposed algorithm and its excellence through simulation.