• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.277-283
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• 2014

This paper proposes robot localization using Fuzzy-Extended Kalman Filter algorithm of the mobile robots equipped with least sensors. In order to improve the accuracy of the localization, we usually add the sensors or equipment. However, it increases the simulation time and expenses. This paper solves this problem using only the odometer and ultrasonic sensors to get the localization with the Fuzzy-Extended Kalman Filter algorithm method. By inputting the robot's angular velocity, sensor data variation, and residual errors into the fuzzy algorithm, we get the sensor weight factor to decide the sensor's importance. The performance of the designed method shows by the simulation and Pioneer 3-DX mobile robot test in the indoor environment.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.282-285
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• 2007

Satellite observed brightness temperature simulation using a radiative transfer model (here after, RTM) is useful for various fields, for example sensor design and channel selection by using theoretically calculated radiance data, development of satellite data processing algorithm and algorithm parameter determination before launch. This study is focused on elaborating the simulation procedure, and analyzing of difference between observed and modelled clear sky brightness temperatures. For the CMDPS (COMS Meteorological Data Processing System) development, the simulated clear sky brightness temperatures are used to determine whether the corresponding pixels are cloud-contaminated in cloud mask algorithm as a reference data. Also it provides important information for calibrating satellite observed radiances. Meanwhile, simulated brightness temperatures of COMS channels plan to be used for assessing the CMDPS performance test. For these applications, the RTM requires fast calculation and high accuracy. The simulated clear sky brightness temperatures are compared with those of MTSAT-1R observation to assess the model performance and the quality of the observation. The results show that there is good agreement in the ocean mostly, while in the land disagreement is partially found due to surface characteristics such as land surface temperature, surface vegetation, terrain effect, and so on.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4541-4554
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• 2019

This paper proposes a planar projection DV-hop location algorithm (PTCDV-hop) based on the LED semi-angle at half power (SAHP, which accounts for LED SAHP characteristics in visible light communication (VLC)) and uses the DV-hop algorithm for range-free localization. Distances between source nodes and nodes positioned in three-dimensional indoor space are projected onto a two-dimensional plane to reduce complexity. Circles are structured by assigning source nodes (projected onto the horizontal plane of the assigned nodes) to be centers and the projection distances as radii. The proposed PTCDV-hop algorithm then determines the position of node location coordinates using the trilateral-weighted-centroid algorithm. Simulation results show localization errors of the proposed algorithm are on the order of magnitude of a millimeter when three sources are used. The PTCDV-hop algorithm has higher positioning accuracy and stronger dominance than the traditional DV-hop algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.5
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• pp.405-413
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• 2015

This paper presents the motion accuracy simulation considering loads such as workpiece weight, cutting force, cogging force of a linear motor, and force caused by misalignment and runout error of a ballscrew in linear motion units. The transfer function method is basically utilized to estimate 5-DOF motion errors, together with the equilibrium equations of force and moment on the table. The transfer function method is modified in order to consider clearance changed according to the loads in the double sided hydrostatic/aerostatic bearings. Then, the analytic model for predicting the 5-DOF motion errors is proposed with the modified transfer function method. Motion errors were simulated under different loading conditions in the linear motion units using hydrostatic, aerostatic, and linear motion bearings, respectively. And the proposed analytic model was verified by comparing the estimated and measured motion errors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.574-592
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• 2016

Range-free localization algorithm computes a normal node's position by estimating the distance to anchors which know their actual position. In recent years, reliable anchor selection research has been gained a lot of attention because this approach improves localization accuracy by selecting the only subset of anchors called reliable anchor. The distance estimation accuracy and the geometric shape formed by anchors are the two important factors which need to be considered when selecting the reliable anchors. In this paper, we study the relationship between a relative position of three anchors and localization error. From this study, under ideal condition, which is with zero localization error, we find two conditions for anchor selection, thereby proposing a novel anchor selection algorithm that selects three anchors matched most closely to the two conditions, and the validities of the conditions are proved using two theorems. By further employing the conditions, we finally propose a novel range-free localization algorithm. Simulation results show that the proposed algorithm shows considerably improved performance as compared to other existing works.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1097-1112
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• 1989

An iterative learning control scheme is incorporated to the computed torque method as a means to enhance the accuracy and the flexibility. A learning rule is constructed by utilizing a gradient descent algorithm and data compressing techniques are illustrated. Computer simulation results show a good performance of the scheme under a relatively high speed and a heavy payload condition.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1293-1302
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• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Journal of Urban Science
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• v.6 no.2
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• pp.49-57
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• 2017

A new Fiber Bragg Grating (FBG) wavelength demodulation scheme is studied in the paper, which consists of an improved de-noising method and Gaussian fitting peak searching algorithm. The improved translational invariant wavelet without threshold adjust factor is proposed to get a better de-noising performance for FBG sensor signal and overcome the drawbacks of soft or hard threshold wavelets. In order to get a high wavelength demodulation precision of FBG sensor signal, this de-noising method is designed to combine with Gaussian fitting peak searching algorithm. The simulation results show that the wavelength maximum measurement error is lower than 1pm, and can get a much higher accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1349-1354
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• 1990

This paper deals with the control of autonomous vehicle in the production systems. Presently, there is a significant interest in autonomous vehicles which are capable of intelligent motion (and action) without requiring a guide track to follow. This paper describes a PI-F adaptive control algorithm, which is used to drive an experimental autonomous vehicle along a given trajectory. The simulation results characterizing the accuracy og the algorithm are presented.