• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.1014-1022
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• 2012

This paper proposes a modified recursive sliding DFT to measure the phase of a single-tone. The modification is to provide a self error-cancelling mechanism so that it can significantly reduce the numerical error, which is generally introduced and accumulated when a recursive algorithm is implemented in finite wordlength arithmetic. The phase measurement error is analytically derived to suggest optimized distributions of quantization bits. The analytic derivation and the robustness of the algorithm are also verified by computer simulations. It shows that the maximum phase error of less than $5{\times}10^{-2}$ radian is obtained even when the algorithm is coarsely implemented with 4-bit wordlength twiddle factors.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.283-289
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• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2701-2710
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• 1997

Recursive circulant graph has recently developed as a new model of multiprocessors, and drawn considerable attention to supercomputing, In this paper, we investigate the routing of a message i recursive circulant, that is a key to the performance of this network. On recursive circulant network, we would like to transmit m packets from a source node to a destination node simultaneously along paths, where the ith packet will traverse along the ith path $(o{\leq}i{\leq}m-1)$. In oder for all packets to arrive at the destination node quickly and securely, the ith path must be node-disjoint from all other paths. For construction of these paths, employing the Hamiltonian Circuit Latin Square(HCLS), a special class of $(n{\times}n)$ matrices, we present $O(n^2)$ parallel routing algorithm on recursive circulant network.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.1
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• pp.101-111
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• 2001

Recursive formulas have been effective in solving the equations of motion for large scale constratined mechanical sys-tems. However, derivation of the formulas has been limited to individual terms in the equations of motion, such as veloci-ty, acceleration. and generalized forces. The recursive formulas are generalized in this paper. The velocity transformation method is employed to transform the equations of motion from Cartesian to the joint spaces. Computational structure of the equations of motion in the joint space is carefully examined to classify all necessary computational operations into sev-eral categories. The generalized recursive formula for each category is then developed and applied whenever such a cate-gory of computation is encountered. Since the velocity transformation method yields the equations of motion in a compact form and computational efficiency is achieved by generalized recursive formulas, the proposed method is not only easy to implement but is also efficient. A library of generalized recursive formulas is developed to implement a dynamic analysis algorithm using backward difference.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1204-1209
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• 1990

This paper offers the theory and method for regression analysis of the regression model with operational parameter variables based on the fundamentals of mathematical statistics. Regression coefficients are usually constants related to the problem of regression analysis. This paper considers that regression coefficients are not constants but the functions of some operational parameter variables. This is a kind of method of two-step fitting regression model. The second part of this paper considers the experimental step numbers as recursive variables, the recursive identification with unknown operational parameter variables, which includes two recursive variables, is deduced. Then the optimization and the recursive identification are combined to obtain the sequential experiment optimum design with operational parameter variables. This paper also offers a fast recursive algorithm for a large number of sequential experiments.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• The Journal of the Acoustical Society of Korea
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• v.16 no.3
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• pp.81-85
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• 1997

In this paper, a recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. To obtain the Overdetermined Recursive Instrumental Variable(ORIV) method type algorithm, we transform the 3'th and 4'th order cumulant relationship to a certain matrix form which is consist of only output data. From the matrix form, we induce the proposed algorithm procedure following the ORIV method. The proposed algorithm provides improved estimation accuracy with smaller data and can be applied to a time varying system as well. In addition, it reduces the estimation error due to the additive Gaussian noise compared to conventional 2'rd order based algorithms since it only uses higher than 2'rd order cumulant. Simulation results are presented to compare the performance with other HOS-based algorithms.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.271-286
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• 2011

In this paper, we propose efficient algorithms for parallel prefix computation and sorting on a recursive dual-net. The recursive dual-net $RDN^k$(B) for k > 0 has $(2n_o)^{2K}/2$ nodes and $d_0$ + k links per node, where $n_0$ and $d_0$ are the number of nod es and the node-degree of the base-network B, respectively. Assume that each node holds one data item, the communication and computation time complexities of the algorithm for parallel prefix computation on $RDN^k$(B), k > 0, are $2^{k+1}-2+2^kT_{comm}(0)$ and $2^{k+1}-2+2^kT_{comp}(0)$, respectively, where $T_{comm}(0)$ and $T_{comp}(0)$ are the communication and computation time complexities of the algorithm for parallel prefix computation on the base-network B, respectively. The algorithm for parallel sorting on $RDN^k$(B) is restricted on B = $Q_m$ where $Q_m$ is an m-cube. Assume that each node holds a single data item, the sorting algorithm runs in $O((m2^k)^2)$ computation steps and $O((km2^k)^2)$ communication steps, respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.537-544
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• 2013

This paper proposes a recursive localized Normalized Mutual Information(NMI) algorithm to overcome shortcomings of the conventional NMI algorithm and the localized NMI algorithm which proposed before. The localized NMI algorithm divides images into few fixed size segments and applies NMI algorithm to each segments. By extension, the proposed algorithm uses variable size segments using its characteristic. Dividing each segment recursively, the algorithm selects a suitable segment size and improves a performance of the image registration. Experimental result shows the performance of the proposed algorithm.