• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.119-130
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• 2012

Recently, the software system is getting complicating and the customers are requiring faster development. For the traditional sequential approach can't against this problem iterative approach is used instead. For the representative iterative approach, there is RUP (Rational's Unified Process). However, RUP standard practical methods are phase, iteration, and disciplines, sequentially. As a result, there's some waste of manpower when a discipline is executed in an iteration, it has to wait till the next same discipline is executed. There are linear approach, sequential approach, overlapped iteration approach, and time-boxed iteration for the efficient execution of RUP. However, they have some problems such as waste of manpower or difficulty in the project management. This paper suggests a method about how to execute the disciplines as a concurrent type. The concurrent approach prevents the waste of manpower and solves the difficulty of project management.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.62-70
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• 1996

Image restoration is the process which estimates the original image form the blurred image observed by the non-ideal imaging system with additivenoise. According to the regularized approach, the resotred image can be obtained by iterative methods or the constrained least square error(CLS) filter. Among those retoratin methods, despite of many advantages, iterative iamge restoration is limited in use because of slow convergence. In the present paper, fast iterative image restoration algorithms based on preconditoning are proposed. The preconditioner can be obtained by using the characteristics finite impulse response (FIR) filter structure.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.48.2-48.2
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• 2021

We have developed a frequentist approach for model selection which determines consistency of a cosmological model and the data using the distribution of likelihoods from the iterative smoothing method. Using this approach, we have shown how confidently we can distinguish different models without comparison with one another. In this current work, we compare our approach with conventional Bayesian approach based on estimation of Bayesian evidence using nested sampling for the purpose of model selection. We use simulated future Roman (formerly WFIRST)-like type Ia supernovae data in our analysis. We discuss limits of the Bayesian approach for model selection and display how our proposed frequentist approach, if implemented appropriately, can perform better in falsification of individual models.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.534-540
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• 1993

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.444-447
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• 2004

A new approach to compute the control moves in Extended Dynamic Matrix Control (EDMC) is presented. In this approach, the number of variables, determined in the inner loop of the control algorithm using iterative methods, is reduced from P , the prediction horizon to M , the control horizon. Since M is usually much smaller than P , this modifies the control algorithm from computational point of view. To justify the modification, the computational requirements are compared to those of the existing EDMC algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.455-458
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations arc made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.176-182
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations are made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.152-152
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• 2001

Purpose： The most common instrumental approach to automatic shimming has been based on iterativ. optimization routine(e.g., simplex) to adjust shim settings to maximize the envelope of the FID. Disadvantage of iterative method, however, is very long to compute shim values. Thi paper supposes a non-iterative method that uses 2-D field map to adjust shim settin rapidly.

• 연구논문집
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• s.25
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• pp.147-154
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• 1995

In this paper, we discussed the convergence and the properties of an iterative algorithm method in order to improve a bimean clustering algorithm. This algorithm that we have discussed choose automatically an optimum threshold as a result of an iterative process, successive iterations providing increasingly cleaner extractions of the object region, The iterative approach of a proposed algorithm is seen to select an appropriate threshold for the low contrast images.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.584-587
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• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.