• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.54-60
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• 2002

In this paper, we proposed a hybrid neural network-fuzzy controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of loaming a inverse model neural network of Plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed speed controller get a good response compare with a neural network controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Ocean Systems Engineering
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• v.5 no.3
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• pp.161-179
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• 2015

An emergency response system (ERS) for vessel oil spills is a complex and dynamic system comprising a number of subsystems and activities. Failures may occur during the emergency response operations, this has negative impacts on the effectiveness of the ERS. Of the classes of problems in analyzing failures, the lack of quantitative data is fundamental. In fact, most of the empirical data collected via questionnaire survey is subjective in nature and is inevitably associated with uncertainties caused by the human being's inability to provide complete judgement. In addition, incomplete information and/or vagueness of the meaning about the failures add difficulties in evaluating the effectiveness of the system. Therefore this paper proposes a framework to evaluate the ERS effectiveness by using the combination of fuzzy reasoning and evidential synthesis approaches. Based on analyzing the procedure of ERS for oil spills, the failures in the system could be identified, using Analytic Hierarchy Process(AHP)to determine the relative weight of identified failures. Fuzzy reasoning combined with evidential synthesis is applied to evaluate the effectiveness of ERS for oil spills under uncertainties last. The proposed method is capable of dealing with uncertainties in data including ignorance and vagueness which traditional methods cannot effectively handle. A case study is used to illustrate the application of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. 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 pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of Korean Society of Forest Science
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• v.89 no.1
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• pp.33-40
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• 2000

This study was carried out to evaluate the fill slope failure possibility of forest road in Metamorphic rock area using fuzzy theory which is non-linear model. The results were summarized as follows. The potential slope failure by nine factors was mainly occurred under the such conditions as the total road width ranging from 4m to 5m, longitudinal gradients below $2^{\circ}$, fill slope length greater than 8m, fill slope gradients steeper than $40^{\circ}$, road on ridge position, soil types with weathered rock, slope gradients steeper than $40^{\circ}$, aspect of NW, and longitudinal slope form in convexity. The weight of importance by factors on fill slope failure was ranked in the order of fill slope length, fill slope gradient, road position, soil type, aspect and longitudinal slope form. The analysis showed that the fill slope failure possibility was low with less than 0.485 of the fuzzy integral value and high with more than 0.620 of the value. And the discriminant accuracy was 74.6%. The analysis with six out of nine factors indicated that the possibility was low with less than 0.441 of the fuzzy integral value and high with more than 0.583 of the value. In this case, the discriminant accuracy was slightly increased to 78.0%.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.45-62
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• 2004

Any business activities may have impact on environment to a certain extent. Enterprises must find appropriate approaches to measure the impact on these environmental aspects, which can be used as the basis to direct enterprises' efforts to improve the environmental impact. The method used to evaluate significant factors in life cycle assessment standards is the one most commonly used by enterprises in general to measure environmental impact. By this method, the decisive factors of each environmental aspect are given scores according to the preset scoring standard of the organization. The scores are added up for each aspect and ranked to assess major environmental aspects. The drawback of this assessment method, that is, it ignores the degree to which each of these factors affects the environment, results in poor credibility. Therefore, this study attempts to solve some qualitative problems by applying to fuzzy theory, in particular, by identifying appropriate fuzzy numbers through fuzzy sets and membership function. Moreover, the study seeks to obtain a crisp value in the process of defuzzifization in order to make up for the shortfall of the original method in dealing with relative weight of decisive factors and thus increase its applicability and credibility. The department of light production of an electronics company is used as an example in this study to measure environmental aspects by employing both the traditional significant factor method and the fuzzy environmental impact assessment model proposed in this study. Based on verification and comparison of results, the model proposed in this study is more feasible as it reduces partiality in decision-making by taking the relative weights of decisive factors into consideration.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.46-52
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• 2001

This study describes the precision control of the torque standard machine using a self-tuning fuzzy controller. The torque standard machine should generate the accurate torque for calibrating a torque sensor. In order to reduce the relative expanded uncertainty of the torque standard machine, when a weight is hanged to the end of the torque arm for generating the torque, the sloped torque arm should be accurately controlled to the horizontal level. If the slope of the torque arm is larger from the inaccurate control, the uncertainty of the torque standard machine due to control will be larger. This applies the inaccurate torque to a torque sensor to calibrate, and the measuring error of the torque sensor generate from it. Therefore the torque arm of the torque standard machine is accurately controlled. In this paper, the self-tuning fuzzy controller was designed using a fuzzy theory, and the torque arm of the torque standard machine was accurately controlled. The control gain of the fuzzy controller, that is the membership function size of the error, the membership function size of the error change and the membership function size of the controller were determined from the self-tuning. The control results of the torque standard machine were the overshoot within 0.0076mm, the rise time within 16.70sec and the steady state error within 0.0076mm.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.395-401
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• 2014

As social network service becomes common, the consumers use many discount coupons with which they can purchase goods via social commerce. Although, the quantities of coupons offered from social commerce are currently on the sharp increase, customized coupon service that reflects user preference is not offered. This paper proposes a coupon service method reflecting user's subjective inclination targeting food coupons to offer customized coupon service for social commerce. Towards this end, this paper conducts hierarchization of the factors that become standard in selecting coupons including food types, food prices, discount rates and the number of buyers. And then, this study classifies, extracts and offers the coupons using Fuzzy-AHP, a decision making support method that reflects subjective inclination. From the user satisfaction results on the extracted coupons, the users are generally satisfied: very satisfactory with 45%, satisfactory with 33% and fair with 22%, and there was no experiment participant, who was dissatisfied.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3883-3891
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• 1996

Performance and productivity of robot manipulator can be improved by increasing its working speed and extending its link length. But heavy weght of the commercial robot links, considered as "rigid body", limits its mazimum working speed and the weght of the links can be reduced for high speed operation. But this light-weight link or long link for special use cannot be consideredas "rigid" structure and vibration of the link due to its flexibility causes errors in end-effector position and orientation. Thus the elastic behaviro of the flexible link should be taken care of for increasing work speed and getting smaller error of end-effector position. In this paper, the fuzzy control theory is selected to design the controller which controlos the joint positions of the robot manipulator and suppress the vibration of flexible link. In the forst place, for the 1 DOF flexible link system, the fuzzy control theory is implemented. The contdroller for the 1 DOF flexible link system is designed. Experimental research is carried out to examine the controllability and the validity of the fuzzy control theory based controller. Next, using the extended desing schemes for the case of the 1 DOF flexible link system and usign the experimental phenomena of the 3 DOF flexible link system, the fuzzy controller for the 3 DOF flexible link system is desinged and experimented.ed and experimented.