• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1321-1327
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• 2009

In this paper, as the mobile communication service is widely used and the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem the oscillation due to feedback signal. We proposed a new hybrid interference canceller using the adaptive filter with CMA(Constant Modulus Algorithm)-Grouped LMS(Least Mean Square) algorithm in the adaptive interference canceller. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped LMS algorithm. The proposed detector uses the LMS algorithms with two different step size to reduce mean square error and to obtain fast convergence. This structure reduces the number of iterations for the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1806-1814
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• 1993

Efficient speech coders using tree coding combined with harmonic scaling are designed at the rate of 4.8 kilobitts/sec (kbps). A time domain harmonic scaling algorithm (TDHS) is used to compress input speech by a factor of two. This process allows the tree coder have 1.5 bits/sample for 4.8 kbps in the case of a 6.4 kHz sampling rate. In the backward adaptive tree coder, there are three components of the code generator, including a hybrid adaptive quantizer, a short-term predictor and a pitch predictor. The robustness of the tree coder is achieved by carefully choosing the input of the short term predictor adaptation. Also, inclusion of a smoother in the pitch predictor improves the error performance of tree coder in the noisy channel. Subjectively, tree coding combined with TDHS provides good quality speech at 4.8 kbps.

• ETRI Journal
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• v.36 no.5
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• pp.819-828
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• 2014

To solve the increasing popularity of video streaming services over the Internet, recent research activities have addressed the locality of content delivery from a network edge by introducing a storage module into a router. To employ in-network caching and persistent request routing, this paper introduces a hybrid content delivery network (CDN) system combining novel content routers in an underlay together with a traditional CDN server in an overlay. This system first selects the most suitable delivery scheme (that is, multicast or broadcast) for the content in question and then allocates an appropriate number of channels based on a consideration of the content's popularity. The proposed scheme aims to minimize traffic volume and achieve optimal delivery cost, since the most popular content is delivered through broadcast channels and the least popular through multicast channels. The performance of the adaptive scheme is clearly evaluated and compared against both the multicast and broadcast schemes in terms of the optimal in-network caching size and number of unicast channels in a content router to observe the significant impact of our proposed scheme.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.926-936
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• 2017

The nonlinear behavior and the high performance requirement are the main problems that appear in the design of manipulator robots and their controllers. For that reason, the simulation, real-time execution and comparison of the performance of controllers applied to a robot with three degrees of freedom are presented. Five controllers are prepared to test the robot's dynamic model: predictive; hyperbolic sine-cosine; sliding mode; hybrid composed of a predictive + hyperbolic sine-cosine controller; and adaptive controller. A redundant robot, a communication and signal conditioning interface, and a simulator are developed by means of the MatLab/Simulink software, which allows analyzing the dynamic performance of the robot and of the designed controllers. The manipulator robot is made to follow a test trajectory which, thanks to the proposed controllers, it can do. The results of the performance of this manipulator and of its controllers, for each of the three joints, are compared by means of RMS indices, considering joint errors according to the imposed trajectory and to the controller used.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.780_781
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.