• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.553-560
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• 2003

Since Wavelet Transform decomposes a video frame into subbands with various frequencies and resolutions, the reconstructed video qualify at a receiver fluctuates according to the location of transmission errors within frames. This deteriorates the whole visual duality of the video. Specifically, for a wavelet based video which exploits the motion estimation prediction scheme, the transmission errors of a subband not only have a bad effect on other subbands within a same frame but also propagates to the subsequent frames. In this paper, we propose BDP(Block Based Dispersive Packetization) scheme, for a wavelet based video stream, which maintains constant video quality despite packet location that a transmission error occurs. To evaluate the performance of the proposed scheme, we use MRME(Multi-Resolution Motion Estimation) scheme to compress a video in Inter coding mode and Gilbert´s error model to generate the error patterns in wireless network environment. The simulation results show that BDP is more efficient than BP (Block based Packetization) or DP (Dispersive Packetization) in both PSNR and visual quality.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.133-140
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• 2016

In active sonar field, a target detection and localization based on a distributed sensor network has been much studied for the underwater surveillance of the coast. Zhou et al. proposed a target localization method utilizing the positions of target-detected sensors in distributed sensor network which consists of detection-only sensors. In contrast with a conventional method, Zhou's method dose not require to estimate the propagation model parameters of detection signal. Also it needs the lower computational complexity, and to transmit less data between network nodes. However, it has large target localization error. So it has been modified for reducing localization error by Ryu. Modified Zhou's method has better estimation performance than Zhou's method, but still relatively large estimation error. In this paper, a target localization method based on modified Zhou's method is proposed for reducing the localization error. The proposed method utilizes the geometry of the positions of target-detected sensors and a line that represents the bearing of target, a line can be found by modified Zhou's method. This paper shows that the proposed method has better target position estimation performance than Zhou's and modified Zhou's method by computer simulations.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.449-456
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• 2004

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.472-478
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The error back-propagation method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• Computers and Concrete
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• v.13 no.2
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• pp.255-270
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• 2014

Impact performance of high-performance concrete (HPC) and SFRC at 28-day and 56-day under the action of repeated dynamic loading was studied. Silica fume replacement at 10% and 15% by mass and crimped steel fiber ($V_f$ = 0.5%- 1.5%) with aspect ratios of 80 and 53 were used in the concrete mixes. Results indicated that addition of fibers in HPC can effectively restrain the initiation and propagation of cracks under stress, and enhance the impact strengths and toughness of HPC. Variation of fiber aspect ratio has minor effect on improvement in impact strength. Based on the experimental data, failure resistance prediction models were developed with correlation coefficient (R) = 0.96 and the estimated absolute variation is 1.82% and on validation, the integral absolute error (IAE) determined is 10.49%. On analyzing the data collected, linear relationship for the prediction of failure resistance with R= 0.99 was obtained. IAE value of 10.26% for the model indicates better the reliability of model. Multiple linear regression model was developed to predict the ultimate failure resistance with multiple R= 0.96 and absolute variation obtained is 4.9%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.6
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• pp.441-448
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• 2018

Recently, measurements on clutter loss due to buildings in urban/suburban areas at 3, 6, 10, 18, and 24 GHz have been performed by the Radio Research Agency with the purpose of predicting the clutter loss close to actual urban/suburban propagation for 5G mobile communication. In this work, we have compared the urban clutter loss to suburban clutter loss for a transmit antenna height of 85 m. Furthermore, we have estimated the error between the predicted loss as per ITU-R P.2108 and the measured clutter loss. Our results indicate that for higher frequencies, the measured clutter loss in urban/suburban areas is higher and so lower than the predicted clutter loss. In conclusion, it is necessary to improve the prediction model for clutter loss by taking into account the measured clutter loss in urban/suburban areas in the prediction model.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.731-741
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• 2010

In this paper, we propose the methodology of prediction of field strength for a digital television (DTV) receiver by virtue of Recommendation ITU-R P.1546. The curves shown in this recommendation represent the point-to area field strength for 1.0 kW effective radiated power in the 30 MHz ~ 3000 MHz. Based upon the procedures described in this Recommendation, computation results are presented here from the derived formulation of field strength for DTV receiver. To show the validity of this method, some results are compared with the analysis by Okumura-Hata model and it was shown that the error of field strength is in the range of 6.9 ~ 11.5 %. The presented method provides not only the predicted values of field strength for DTV receiving area to check the quality of transmitted signal, but also an appropriate site selection for obtaining good propagation environment. In addition, it can be directly used for analyzing the protection ratio or separated distance for frequency sharing in the same band.