• Nonlinear Functional Analysis and Applications
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• 제28권1호
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• pp.135-173
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• 2023

The purpose of this paper is to introduce and study a method for solving the split equality of variational inequality and f, g-fixed point problems in reflexive real Banach spaces, where the variational inequality problems are for uniformly continuous pseudomonotone mappings and the fixed point problems are for Bregman relatively f, g-nonexpansive mappings. A strong convergence theorem is proved under some mild conditions. Finally, a numerical example is provided to demonstrate the effectiveness of the algorithm.

• Nonlinear Functional Analysis and Applications
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• 제29권1호
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• pp.69-81
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• 2024

In this paper, we provide certain fixed point results for a generalized 𝛼-nonexpansive mapping, as well as a new iterative algorithm called SRJ-iteration for approximating the fixed point of this class of mappings in the setting of CAT(0) spaces. Furthermore, we establish strong and ∆-convergence theorem for generalized 𝛼-nonexpansive mapping in CAT(0) space. Finally, we present a numerical example to illustrate our main result and then display the efficiency of the proposed algorithm compared to different iterative algorithms in the literature. Our results obtained in this paper improve, extend and unify results of Abbas et al. [10], Thakur et al. [22] and Piri et al. [19].

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• Journal of Magnetics
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• 제21권3호
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• pp.442-449
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• 2016

In this paper, an effective numerical analysis method is proposed for calculating dosimetry of the wireless power transfer system operating low-frequency ranges. The finite-difference time-domain (FDTD) method is widely used to analyze bio-electromagnetic field problems, which require high resolution, such as a heterogeneous whole-body voxel human model. However, applying the standard method in the low-frequency band incurs an inordinate number of time steps. We overcome this problem by proposing a modified finite-difference time-domain method which utilizes a quasi-static approximation with the surface equivalence theorem. The analysis results of the simple model by using proposed method are in good agreement with those from a commercial electromagnetic simulator. A simulation of the induced electric fields in a human head voxel model exposed to a wireless power transmission system provides a realistic example of an application of the proposed method. The simulation results of the realistic human model with the proposed method are verified by comparing it with the conventional FDTD method.

• Journal of Power Electronics
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• 제16권4호
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• pp.1438-1454
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• 2016

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

• 대한수학회지
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• 제36권6호
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• pp.1133-1143
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• 1999

Chaterji strengthened version of a theorem for martin-gales which is a generalization of a theorem of Marcinkiewicz proving that if $X_n$ is a sequence of independent, identically distributed random variables with $E{\mid}X_n{\mid}^p\;<\;{\infty}$, 0 < P < 2 and $EX_1\;=\;1{\leq}\;p\;<\;2$ then $n^{-1/p}{\sum^n}_{i=1}X_i\;\rightarrow\;0$ a,s, and in $L^p$. In this paper, we probe a version of law of large numbers for double arrays. If ${X_{ij}}$ is a double sequence of random variables with $E{\mid}X_{11}\mid^log^+\mid X_{11}\mid^p\;<\infty$, 0 < P <2, then $lim_{m{\vee}n{\rightarrow}\infty}\frac{{\sum^m}_{i=1}{\sum^n}_{j=1}(X_{ij-a_{ij}}}{(mn)^\frac{1}{p}}\;=0$ a.s. and in $L^p$, where $a_{ij}$ = 0 if 0 < p < 1, and $a_{ij}\;=\;E[X_{ij}\midF_[ij}]$ if $1{\leq}p{\leq}2$, which is a generalization of Etemadi's marcinkiewicz-type SLLN for double arrays. this also generalize earlier results of Smythe, and Gut for double arrays of i.i.d. r.v's.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1477-1485
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• 2016

본 연구에서는 한국프로야구에서 팀의 득점과 실점을 가지고 시즌 승률을 예측하는 야구의 피타고라스 정리에 의한 기대승률의 수렴특성을 살펴보았다. 사용한 자료는 2005년부터 2014년까지의 한국프로야구 정규시즌 초부터 정규시즌 말까지의 팀대 팀 전체기록이며, 그 결과 야구 팀의 특징 중에서 팀의 순위와 경기진행률이 수렴특성에 영향을 주는 것으로 나타났다. 팀의 순위는 하위 팀들의 기대승률이 최종 기대승률에 빨리 수렴하였으며, 경기진행률은 20% 이하에는 최종 기대승률과 많은 차이를 보였으나 70% 이상부터는 통계적으로 최종 기대승률과 유의한 차이가 발생하지 않았다.

• Journal of information and communication convergence engineering
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• 제16권2호
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• pp.130-134
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• 2018

Wireless capsule endoscopy (WCE) is considered as recent technology for the detection cancer cells in the human digestive system. WCE sends the captured information from inside the body to a sensor on the skin surface through a wireless medium. In WCE, the design of low-power consumption devices is a challenging topic. In the Shannon-Nyquist sampling theorem, the number of samples should be at least twice the highest transmission frequency to reconstruct precise signals. The number of samples is proportional to the power consumption in wireless communication. This paper proposes compressive sensing as a method to reduce power consumption in WCE, by means of a trade-off between samples and reconstruction accuracy. The proposed scheme is validated under channel constraints, expressed as the realistic human body path loss. The results show that the proposed scheme achieves a significant reduction in WCE power consumption and achieves a faster computation time with low signal error reconstruction.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2004년도 학술대회지
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• pp.179-182
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• 2004

This paper deals with a new mathematical formulation of nonlinear wave profile based on Banach fixed point theorem. As application of the formulation and its solution procedure, some numerical solutions was presented in this paper and nonlinear equation was derived. Also we introduce a new operator for iteration and getting solution. A numerical study was accomplished with Stokes' first-order solution and iteration scheme, and then we can know the nonlinear characteristic of Stokes' high-order solution. That is, using only Stokes' first-oder(linear) velocity potential and an initial guess of wave profile, it is possible to realize the corresponding high-oder Stokian wave profile with tile new numerical scheme which is the method of iteration. We proved the mathematical convergence of tile proposed scheme. The nonlinear strategy of iterations has very fast convergence rate, that is, only about 6-10 iterations arc required to obtain a numerically converged solution.