• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.500-504
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• 2011

In this paper, we propose an equivalent Wiener-Hopf equation. The proposed algorithm can obtain the weight vector of a TDL(tapped-delay-line) filter and the error simultaneously if the inputs are orthogonal to each other. The equivalent Wiener-Hopf equation was analyzed theoretically based on the MMSE(minimum mean square error) method. The results present that the proposed algorithm is equivalent to original Wiener-Hopf equation. The new algorithm was applied into the identification of an unknown system for evaluating the performance of the proposed method. We compared the Wiener-Hopf solution with the equivalent Wiener-Hopf solution. The simulation results were similar to those obtained in the theoretical analysis. In conclusion, our method can find the coefficient of the TDL (tapped-delay-line) filter where a lattice filter is used, and also when the process of Gram-Schmidt orthogonalization is used. Furthermore, a new cost function is suggested which may facilitate research in the adaptive signal processing area.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Structural Engineering and Mechanics
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• v.47 no.3
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• pp.401-419
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• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.549-560
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• 1989

In this paper, the method of determining the initial weighting vlaues fuctioning as a filter under the Directional Constrained Minimization of Power(DCMP) algorithm is presented. By analyzing the sideband beamformer with the Finite Impulse Response (FIR) filter concepts, the constraints of any desired directions are obtained and the initial weighing values with fast adaptation time are formulated from those constraints. By applying this proposed initial weighting values to the DCMP and the spatial averaging processor, the interference of a desired direction and the coherent noises are eliminated at the same time. The improvement of this method compared with the existing algorithm is confirmed by computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.375-382
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• 2004

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neuro-controller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. 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 LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.267-276
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• 1993

This paper discusses the neural network application in the study on the obstacle avoidance of robot manipulator during the trajectory planning. The collision problem of two robot manipulators which are simultaneously moving in the same workspace is investigated. Instead of the traditional modeling method, this paper processing based on the calculation of joint angle in the cartesian coordinate with constrained condition shows the possibility of real time control. The problem of the falling into the local minima is cleared by the adaptive weight factor control using the temperature adding method. Computer simulations are shown for the verification.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.1
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• pp.83-97
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• 2013

This study was carried out to suggest an experimental base in selecting the drought resistance of plants. Adopting the natural drought method, this paper studies the drought resistance of 12 kinds of ground cover plants. focusing on analyzing the changes of relative water content on leaf, relative electric conductivity and chlorophyll content in 12 kinds of plants, and and the relation between soil water content under drought stress. The drought resistance of the plants were subject to laboratory and rooftop drought resistance treatments. The Logistic model of nonlinear regression analysis was used to evaluate the lethal time that were predicted with the range of 10.4~30.1d on roof top, and 19.5~39.0d on hothouse. The result shows that with the increase of stress time, relative water content and chlorophyll content on leaf were in a downward trend; the relative electric conductivity was upward tendency. Among 12 species of ground cover plants, exclude Pulsatilla koreana, Ainsliaea acerifolia were selected for rooftop plants because they showed resist drought strongly and took adaptive ability.

• Journal of KSNVE
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• v.9 no.1
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• pp.121-130
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• 1999

Generally, A Dynamic Absorber by using Active viscous Damping is highlighted for effective suspension system, such as improved ride comfort and handling in the market. Lately, this system based on the Sky-Hook damper theory is introduced by the name of "Active Dynamic Absorber" to us. This system has an excellent performance in contrast to Passive. Adaptive Dynamic Absorber, besides having low cost components of system, low energy consumption. light weight of system. In this viewpoint. most of car-maker will adopt this system in the near future. For this reason, we developed Dynamic Absorber by using Active viscous Damping which is equipped with continuously variable Dynamic Absorber and Control logic consisting Filter and Estimator. control apparatus of Dynamic Absorber operated by 16-bit microprocessor of high performance. variable device of viscous Damping. G-sensor so on. In this paper. several important points of development procedure for realizing this system will be described with results in which is obtained from experiment by simulation and Full car test in Proving ground. respectively.pectively.

• Journal of Communications and Networks
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• v.5 no.3
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• pp.275-281
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• 2003

Random Early Detection (RED) [1] is an active queue management scheme which has been deployed extensively to reduce packet loss during congestion. Although RED can improve loss rates, its performance depends severely on the tuning of its operating parameters. The idea of adaptively varying RED parameters to suit the network conditions has been investigated in [2], where the maximum packet dropping probability $max_p$ has been varied. This paper focuses on adaptively varying the queue weight $\omega_q$ in conjunction with $max_p$ to improve the performance. We propose two algorithms viz., $\omega_q$-thresh and $\omega_q$-ewma to adaptively vary $\omega_q$. The performance is measured in terms of the packet loss percentage, link utilization and stability of the instantaneous queue length. We demonstrate that varying $\omega_q$ and $max_p$ together results in an overall improvement in loss percentage and queue stability, while maintaining the same link utilization. We also show that $max_p$ has a greater influence on loss percentage and queue stability as compared to $\omega_q$, and that varying $\omega_q$ has a positive influence on link utilization.