• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.9-12
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• 2002

This paper proposes a dynamic moment algorithm to control oscillaion before the convergence of the KR(Kernel Relaxation). The proposed dynamic moment algorithm can be controlled to convergence speed and performance according to the change of the dynamic moment by teaming training. we used SONAR data that is a neural network classifier standard evaluation data in order to do impartial performance evaluation. The proposed algorithm has been applied to the KP (kernel perceptron), KPM(kernel perceptron with margin) and KLMS(kernel lms) as the kernel method presented recently. The simulation results of proposed algorithm have better the convergence performance than those using none and static moment.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.293-309
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• 2011

The purpose of this article is to prove strong convergence theorems for weak relatively nonexpansive mapping which is firstly presented in this article. In order to get the strong convergence theorems for weak relatively nonexpansive mapping, the monotone CQ iteration method is presented and is used to approximate the fixed point of weak relatively nonexpansive mapping, therefore this article apply above results to prove the strong convergence theorems of zero point for maximal monotone operators in Banach spaces. Noting that, the CQ iteration method can be used for relatively nonexpansive mapping but it can not be used for weak relatively nonexpansive mapping. However, the monotone CQ method can be used for weak relatively nonexpansive mapping. The results of this paper modify and improve the results of S.Matsushita and W.Takahashi, and some others.

• Journal of the Korean Mathematical Society
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• v.51 no.4
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• pp.679-702
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• 2014

Investigation of the numerical solution of singularly perturbed turning point problems dates back to late 1970s. However, due to the presence of layers, not many high order schemes could be developed to solve such problems. On the other hand, one could think of applying the convergence acceleration technique to improve the performance of existing numerical methods. However, that itself posed some challenges. To this end, we design and analyze a novel fitted operator finite difference method (FOFDM) to solve this type of problems. Then we develop a fitted mesh finite difference method (FMFDM). Our detailed convergence analysis shows that this FMFDM is robust with respect to the singular perturbation parameter. Then we investigate the effect of Richardson extrapolation on both of these methods. We observe that, the accuracy is improved in both cases whereas the rate of convergence depends on the particular scheme being used.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.384-393
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• 2015

This paper proposes an insect behavior-inspired routing algorithm for large-scale wireless mesh networks. The proposed algorithm is adapted from the behavior of an insect called Bombyx mori, a male silkmoth. Its unique behavior is its flying technique to find the source of pheromones. The algorithm consists of two steps: the shortest-path algorithm and the zigzag-path algorithm. First, the shortest-path algorithm is employed to transmit data. After half of the total hops, the zigzag-path algorithm, which is based on the movement of the male B. mori, is applied. In order to adapt the biological behavior to large-scale wireless mesh networks, we use a mesh topology for implementing the algorithm. Simulation results show that the total energy used and the decision time for routing of the proposed algorithm are improved under certain conditions.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.478-493
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• 2020

We designed a new meta-heuristic algorithm named Photon Search Algorithm (PSA) in this paper, which is motivated by photon properties in the field of physics. The physical knowledge involved in this paper includes three main concepts: Principle of Constancy of Light Velocity, Uncertainty Principle and Pauli Exclusion Principle. Based on these physical knowledges, we developed mathematical formulations and models of the proposed algorithm. Moreover, in order to confirm the convergence capability of the algorithm proposed, we compared it with 7 unimodal benchmark functions and 23 multimodal benchmark functions. Experimental results indicate that PSA has better global convergence and higher searching efficiency. Although the performance of the algorithm in solving the optimal solution of certain functions is slightly inferior to that of the existing heuristic algorithm, it is better than the existing algorithm in solving most functions. On balance, PSA has relatively better convergence performance than the existing metaheuristic algorithms.

• Korean Journal of Organic Agriculture
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• v.27 no.2
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• pp.133-146
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• 2019

This study analyze the certified management bodies for rural convergence industry promotion in Jeonnam province. The main results are as follows. Firstly, both certified management bodies and committee members are positive about the prospects of the 6^th industry, and they also agree on the need for 6^th Industrial accreditation system. Secondly, The 6^th industry affects "creation of value added" and "linked to regional agriculture." In other words, we can see that the 6^th industry can contribute to creating new values and linking regional agriculture. Finally, in order to foster certified management bodies for the 6^th industry, the management bodies put joint promotion, marketing and sales support on a priority basis, and was interested in supporting commercialization, such as on-site coaching, while the expert committee put priority on fostering the search for profit models, placing importance on supporting commercialization such as customized coaching.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.319-323
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• 2012

In this paper, we present the possibility of quantification analysis for $A{\beta}$ captured by micro beads using Near-filed detection. In order to evaluate detection efficiency, detected signals were compared with different sizes of micro beads and a varied number of micro beads. Also, $A{\beta}$ deposits and $A{\beta}$ binding to micro beads were measured, therefore, we observed the $A{\beta}$ deposit and light scattering around the surface of micro beads induced by attached $A{\beta}$. This method can be used for quantitative analysis for not only the number of $A{\beta}$, but also the binding ratio of $A{\beta}$ to micro beads.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1235-1243
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• 2004

In this paper, a new blind equalizer algorithm is proposed which operates on dual mode and combines the benefits of both the Sign-Godard algorithm and the radius-directed algorithm The proposed algorithm has both the properties of good initial convergence of the Sign-Godard algorithm and low residual errors after convergence of the radius-directed algorith High order statistics are used for blind phase recovery and gor avoiding local minima. Simulation results show that the new algorithm has not only faster convergence rated but also lower residual errors than those of the conventional algorithms.

• Journal of Microbiology and Biotechnology
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• v.27 no.2
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• pp.412-415
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• 2017

Prebiotics improve the growth or activities of specific microbial genera and species in the gut microbiota in order to confer health benefits to the host. In this study, we investigated the effect of poly-gamma-glutamate (${\gamma}-PGA$) as a prebiotic on the gut microbiota of mice and the organ distributions of ${\gamma}-PGA$ in mice. Pyrosequencing analysis for 16S rRNA genes of bacteria indicated that oral administration of ${\gamma}-PGA$ increased the abundance of Lactobacillales while reducing the abundance of Clostridiales in murine guts. It is suggested that oral administration of ${\gamma}-PGA$ can be helpful for modulating the gut microbiota as a prebiotic.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.