• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.6
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• pp.98-104
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• 2002

In this study, we have developed an algorithm by attaching a camera at the end-effector of industrial six-axis robot in order to determine position and orientation of the camera system from cartesian coordinates. Cartesian coordinate as a starting point to evaluate for suggested algorithm, it was easy to confront increase of orientation vector for a linear line point that connects two points from coordinate space applied by recursive least square method which includes previous data result and new data result according to increase of image point. Therefore, when the camera attached to the end-effector has been applied to production location, with a calibration mask that has more than eight points arranged, this simulation approved that it is possible to determine position and orientation of cartesian coordinates of camera system even without a special measuring equipment.

• ETRI Journal
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• v.29 no.5
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• pp.596-606
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• 2007

It is well known that an OFDM receiver is vulnerable to synchronization errors. Despite fine estimations used in the initial acquisition, there are still residual synchronization errors. Though these errors are very small, they severely degrade the bit error rate (BER) performance. In this paper, we propose a residual error elimination scheme for the digital OFDM baseband receiver aiming to improve the overall BER performance. Three improvements on existing schemes are made: a pilot-aided recursive algorithm for joint estimation of the residual carrier frequency and sampling time offsets; a delay-based timing error correction technique, which smoothly adjusts the incoming data stream without resampling disturbance; and a decision-directed channel gain update algorithm based on recursive least-squares criterion, which offers faster convergence and smaller error than the least-mean-squares algorithms. Simulation results show that the proposed scheme works well in the multipath channel, and its performance is close to that of an OFDM system with perfect synchronization parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.335-339
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• 1998

This paper presents a closed loop identification algorithm in time domain. This algorithm can be used for identification of unstable system and for model validation of system which is difficult to derive analytical model. In time domain, projection filter, which projects a finite number of input output data of a system into its current space, is used to relate the state space model with a finite difference model. Then recursive relations between the Markov parameters and the ARX model coefficients are derived to identify the system, controller and Kalman filter Markov parameters recursively, which are finally used to identify the system, controller and Kalman filter gains. The NASA LAMSTF is used to validate the algorithms developed.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.8-12
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• 1984

Algorithms for solving a set of nonlinear simultaneous equations, which is frequently required in problems of controlling nonlinear processes, are proposed. Here the equation variables are first parameterized and a recursive identification technique is utilized. The forms and characteristics of the resultant algorithms are vary similar to the Broyden's quasi-Newton method, but their derivations and final recursion equations are different. Our methods possess almost all the merits of the Broyden's and numerical comparisons show our methods to be more efficient and reliable for some difficult problems.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.8-18
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• 2016

In this paper, we propose a number of blind equalization approaches for time-varying andmulti-path channels. The approaches employ cost reference particle filter (CRPF) as the symbol estimator, and additionally employ either least mean squares algorithm, recursive least squares algorithm, or $H{\infty}$ filter (HF) as a channel estimator such that they are jointly employed for the strategy of "Rao-Blackwellization," or equally called "mixture filtering." The novel feature of the proposed approaches is that the blind equalization is performed based on direct channel estimation with unknown noise statistics of the received signals and channel state system while the channel is not directly estimated in the conventional method, and the noise information if known in similar Kalman mixture filtering approach. Simulation results show that the proposed approaches estimate the transmitted symbols and time-varying channel very effectively, and outperform the previously proposed approach which requires the noise information in its application.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.121-128
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• 1997

This paper proposes a class of rearrangeable networks, called generalized rearrangeable networks(GRNs). GRNs are obtained from the Benes network by rearranging the connections between states and the switches within each stage. The GRNs constitute all of the rearrangeable networks which have the recursive decomposition structure and can be routed by the outside-in decomposition of permutations as the Bene network. This paper also presents a necessary condition for a network to be a GRN and a network labeling scheme to check if a network satisfies the condition. the general routing algorithm for the GRNs is given by modifying slightly the looping algorithm of the Benes network.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1102-1109
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• 2001

This paper presents the new architecture of an adaptive predistortion linearizer using the polynomial type predistorter. In the proposed linearizer, most of the processes, including the predistortion, are performed with a digital signal processor(DSP). The recursive least squares(RLS) algorithm is employed for the optimization process to minimize the errors between the predistorter and postdistorter output signals. Simulation results demonstrate that the adjacent channel power ratio(ACPR) is improved by greater than 40 dB at the band edge with linearization. The convergence and reconvergence performance of the linearizer is also satisfactory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.475-481
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• 1999

This paper presents a recursive least square estimation algorithm to estimate parameters of the vector controlled induction machine based on measurements of the stator voltage, curents and slip frequency. Due to its recursive structure, this algorithm has the potential to be used for on-line estimation and adaptive control. The algorithm is designed using regression model derived from the motor electrical equation. This model is valid when there is a tittle-scale separation between vector control system and adaptive system. Vector control performed at fast stage and slow stage is in charge of parameters estimation. The performance of tile algorithm is illustrated by means of simulation results and experiment.