• Kyungpook Mathematical Journal
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• v.59 no.2
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• pp.341-352
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• 2019

Let M be an n-dimensional closed Riemannian manifold with metric g, $d{\mu}=e^{-{\phi}(x)}d{\nu}$ be the weighted measure and ${\Delta}_{p,{\phi}}$ be the weighted p-Laplacian. In this article we will study the evolution and monotonicity for the first nonzero eigenvalue problem of the weighted p-Laplace operator acting on the space of functions along the Yamabe flow on closed Riemannian manifolds. We find the first variation formula of it along the Yamabe flow. We obtain various monotonic quantities and give an example.

• Korean Journal of Mathematics
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• v.27 no.3
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• pp.803-817
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• 2019

In this paper, we provide a new version of Steffensen inequality for p-calculus analogue in [17, 18] which is a generalization of previous results. Also, the conditions for validity of reverse to p-Steffensen inequalities are given. Lastly, we will obtain a generalization of p-Steffensen inequality to the case of monotonic functions.

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.50-60
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• 2004

Recently, most of the embedded system is required not only many functions but also real-time characteristics in purpose. In the hard real-time system, especially, strict deadline of periodic task can affect the performance of the system. In this paper, we design and implement the scheduler based on RM(Rate-Monotonic) rule. This scheduler makes feasible patterns based on EDF(Earliest deadline first) rule with extended schedulability inspection before execution, for periodic task-set that has high CPU utilization and then, execute periodic task-set depended on feasible patterns. The feasible pattern formed into EDF rule is capable of the efficiency of CPU up to 100 percentage and by the referenced execution of the feasible pattern is possible of removing the red-time scheduling overhead that is the defect of the order of dynamic assignment rule.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.1-7
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• 2008

This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the system.

• Steel and Composite Structures
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• v.44 no.4
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• pp.587-602
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• 2022

In this study, the classical J₂ flow theory is explicitly proved to be inappropriate to describe the plastic behaviour of structural steels under different stress states according to the reported test results. A numerical framework of the characterization of the strain hardening and ductile fracture initiation involving the effect of stress states, i.e., stress triaxiality and Lode angle parameter, is proposed based on the mechanical response of structural steels under monotonic loading. Both effects on strain hardening are determined by correction functions, which are implemented as different modules in the numerical framework. Thus, other users can easily modify them according to their test results. Besides, the ductile fracture initiation is determined by a fracture locus in the space of stress triaxiality, Lode angle parameter, and fracture strain. The numerical implementation of the proposed model and the corresponding code are provided in this paper, which are also available on GitHub. The validity of the numerical procedure is examined through single element tests and the accuracy of the proposed model is verified by existing test results.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.60.2-60.2
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• 2011

We introduce the preliminary results of flux monitoring of BL Lac object 0716+714 with the KVN Ulsan 21m radio telescope. This radio source is well known as the intraday variable (IDV) source which is characterized by the rapid flux variation on the time scale of a day or less. In general, the IDV phenomenon is interpreted as the effect of refractive scintillation in the interstellar medium or the evidence of intrinsic flux variation. In previous observations that took a few days, however, it had not been detected the flux variation of short time scale but the monotonic increase and decrease. Therefore, to investigate the longer time scale of 0716+714, we had the flux variation monitoring at 22GHz and 43GHz simultaneously for 9 months from October 2010 to June 2011. We present here the structure functions and the cross correlation functions between different frequencies as well as the light curves.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.99-105
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• 2011

This paper presents a method to design a centralized repetitive controller that is robust to variations in the multiple system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the centralized repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. Furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the multiple system.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1118-1120
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• 1993

Fuzzy logic places a considerable burden on an inference engine for applications such as control or approximate reasoning. Various neural network architectures have been proposed to deal with the computational task, and yet, maintain flexibility in the desired traits of the final system. Recently, we introduced a trainable network architecture whose nodes implement weighted Yager additive hybrid operators for fuzzy logic inference in an approximate reasoning setting. In this paper we examine the utility of such networks for control situations. We show that they are capable of learning control functions which are piece-wise monotonic in each of the variables. The learning ability is demonstrated through an example.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.