• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.227-234
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• 1996

The discount survival model is proposed for the application of the Cox model on the analysis of survival data with time-varying effects of covariates. Algorithms for the recursive estimation of the parameter vector and the retrospective estimation of the survival function are suggested. Also the algorithm of forecasting of the survival function of individuals of specific covariates in the next time interval based on the information gathered until the end of a certain time interval is suggested.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.602-611
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• 1996

In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes's model and the radial basis function network by nonrecursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.3
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• pp.166-171
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• 2008

In this paper, the optimization design module for the brake system of a vehicle is developed. As using this module, design variables, that minimize an object function and satisfy nonlinear constraint conditions, can be found easily. Before an optimization is operated, Plackett-Burman design, one of the factorial design methods, is used to choose the design variables which affect a response function significantly. Using the response surface analysis, second order recursive model function, which informs a relation between design variables and response function, is estimated. In order to verify the reliability of the model function, analysis of variances(ANOVA) table is used. The value of design variables which minimize the model function and satisfy the constraint conditions is predicted through Sequential Quadratic-Programming (SQP) method. As applying the above procedure to a real vehicle simulation model and comparing the values of object functions of a current and optimized system, the optimization results are verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.8 no.3
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• pp.105-112
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• 1983

This paper discribes technique of the recursive restoration for the images degraded by linear space invariant blur and additive white Gaussian noise. The image is characterized statistically by tis mean and correlation function. An exponential autocorrelation function has been used to model neighborhood model. The vector model was used because of analytical simplicitly and capability to implement brightness correlation function. Base on the vector model, a two-dimensional discrete stochastic a 12 point neighborhood model for represeting images was developme and used the technique of moving window processing to restore blurred and noisy images without dimensionality increesing, It has been shown a 12 point neighborhood model was found to be more adequate than a 8 point pixel model to obtain optimum pixel estimated. If the image is highly correlated, it is necessary to use a large number of points in the neighborhood in order to have improvements in restoring image. It is believed that these result could be applied to a wide range of image processing problem. Because image processing thchniques normally required a 2-D linear filtering.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.143-150
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• 2004

In this paper, a recursive digital filter realization for an ionospheric channel model is proposed. This realization is in the form of a cascade of identical second-order all-pass filters, and is determined by only three parameters; two coefficients of an all-pass section, and the number of sections. The values of these parameters are optimized by a nonlinear optimization algorithm called the "downhill simplex method", so that the resulting time delay function closely approximates that of the ionospheric channel model. Comparing with the nonrecursive digital filter realization, it can be shown that the proposed recursive-digital-filter-realization is advantageous in points of view for the numbers of filter coefficients and the realization.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.650-655
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• 2016

In this paper, a novel RLS-based DOB (Recursive Least Squares Disturbance Observer) scheme is proposed to improve the performance of DOB for nominal model identification. A nominal model can be generally assumed to be a second order system in the form of a proper transfer function of an ARMA (Autoregressive Moving Average) model. The RLS algorithm for the model identification is proposed in association with DOB. Experimental studies of the balancing control of a one-wheel robot are conducted to demonstrate the feasibility of the proposed method. The performances between the conventional DOB scheme and the proposed scheme are compared.

• 전기의세계
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• v.31 no.3
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• pp.204-208
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• 1982

The objective of this paper is to present a digital method for improving the lateral resolution of the B-scan images in the medical applications of ultrasound. The method is based upon a mathematical model of the lateral blurring caused by the finite beam width of the transducers. This model provides a simple method of applying a recursive scheme for image restoration with fast computation time. The point spread function (P.S.F.) can be measured by the reflective signals after scanning the small pins located along the depth of interest. From the measured P.S.F., one can compute the coefficient matrices of the inverse discrete-time dynamic state variable equation of the blurring process. Then, a recursive scheme for deblurring is applied to the recorded B-scan to improve the lateral resolution. One major advantage of the present recursive scheme over the transform method is in its applicability for the space-variant imaging, such as in the case of the rotational movement of transducer.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.277-288
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• 1997

For the analysis of survival data including covariates whose effects vary in time, the multiprocess discount survival model is proposed. The parameter vector modeling the time-varying effects of covariates is to vary between time intervals and its evolution between time intervals depends on the perturbation of the next time interval. The recursive estimation of the parameter vector can be obtained at the end of each time interval. The retrospective estimation of the survival function and the forecasting of the survival function of individuals of the specific covariates also can be obtained based on the information gathered until the end of the time interval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.