• Smart Structures and Systems
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• v.15 no.3
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.185-188
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• 1998

In this paper, we propose a feedback adaptive controller which need not adjustment of the scale factor. Numerical examples are included to illustrate the procedure of a adaptive control and to show the performance of the control system. We can observe that the output of control system, converges toward the reference of response.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.83-88
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• 1989

This paper is a study on the design of adptive filter for QRS complex detection. We propose a simple adaptive algorithm to increase capability of noise cancelation in QRS complex detection with two stage adaptive filter. At the first stage, background noise is removed and at the next stage, only spectrum of QRS complex components is passed. Two adaptive filters can afford to keep track of the changes of both noise and QRS complex. Each adaptive filter consists of prediction error filter and FIR filter The impulse response of FIR filter uses coefficients of prediction error filter. The detection rates for 105 and 108 of MIT/BIH data base were 99.3% and 97.4% respectively.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.65-67
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and ($1+{\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to set a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6MW BLDC motor.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.701-702
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and (1+${\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to get a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6 MW BLDC motor, and the cogging torque is reduced to 19% of the initial one.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.3
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• pp.311-319
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• 1992

In this paper, a direct adaptive pole placement controller for an unknown linear time-invariant single-input single-output nonminimum phase plant is proposed. To design this direct adaptive pole placement controller, the auxiliary signals are introduced. Consequently, a linear equation error model is formulated for estimating both the controller parameters and the additional auxiliary parameters. To estimate the controller parameters and the additional auxiliary parameters, the exponentially weighted least-squares algorithm is implemented, and a method of selecting the characteristic polynomials of the sensitivity function filters is proposed. In this method, all the past measurement data are weighted exponentially. A series of simulations for a nonminimum phase plant is presented to illustrate some features of both the parameter estimation and the output response of this adaptive pole placement controller.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.561-581
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• 2017

Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

• Earthquakes and Structures
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• v.12 no.3
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• pp.271-284
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• 2017

This paper presents a multimodal adaptive nonlinear static method of analysis that, differently from the nonlinear static methods suggested in seismic codes, does not require the definition of the equivalent Single-Degree-Of-Freedom (SDOF) system to evaluate the seismic response of structures. First, the proposed method is formulated for the assessment of r.c. plane frames and then it is extended to 3D framed structures. Furthermore, the proposed nonlinear static approach is re-elaborated as a displacement-based design method that does not require the use of the behaviour factor and takes into account explicitly the plastic deformation capacity of the structure. Numerical applications to r.c. plane frames and to a 3D framed structure with inplan irregularity are carried out to illustrate the attractive features as well as the limitations of the proposed method. Furthermore, the numerical applications evidence the uncertainty about the suitability of the displacement demand prediction obtained by the nonlinear static methods commonly adopted.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.8
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• pp.936-941
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• 2011

This paper deals with the issue of motion control of a single rod cylinder-load system using simple adaptive control (SAC) method. Prior to controller design, the experiment of open-loop response has been performed. Based on it, design parameters of transfer function are obtained and used for controller design. The effect of parallel feedforward compensator has been investigated by computer simulation, suppressing the oscillatory motion. Through experiments it is conformed that the SAC method gives good tracking performance compared to the PD control method.

• Geotechnical Engineering
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• v.26 no.9
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• pp.33-47
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• 2010

Underground constructions continue to provide challenges to Geotechnical Engineers yet they pose the best opportunities for development and deployment of advance technologies for analysis, design and construction. The reason for this is that, by virtue of the nature of underground constructions, more data and information on ground characteristics and response become available as the construction progresses. However, due to several barriers, these data and information are rarely, if ever, utilized to modify and improve project design and construction during the construction stage. To enable the use of evolving realtime data and information, and adaptively modify and improve design and construction, the paper presents an analysis and design system, called AMADEUS, for underground projects. AMADEUS stands for Adaptive, real-time and geologic Mapping, Analysis and Design of Underground Space. AMADEUS relies on recent advances in IT (Information Technology), particularly in digital imaging, data management, visualization and computation to significantly improve analysis, design and construction of underground projects. Using IT and remote sensors, real-time data on geology and excavation response are gathered during the construction using non-intrusive techniques which do not require expensive and time-consuming monitoring. The real-time data are then used to update geological and geomechanical models of the excavation, and to determine the optimal, construction sequences and stages, and structural support. Virtual environment (VE) systems are employed to allow virtual walk-throughs inside an excavation, observe geologic conditions, perform virtual construction operations, and investigate stability of the excavation via computer simulation to steer the next stages of construction.