• East Asian mathematical journal
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• 제28권5호
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• pp.597-604
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• 2012

Let C be a nonempty closed convex subset of real Hilbert space H and F = $\{S(t):t{\geq}0\}$ a nonexpansive self-mapping semigroup of C, and $f:C{\rightarrow}C$ is a fixed contractive mapping. Consider the process {$x_n$} : $$\{{x_{n+1}={\beta}_nx_n+(1-{\beta}_n)z_n\\z_n={\alpha}_nf(x_n)+(1-{\alpha}_n)S(t_n)P_C(x_n-r_nAx_n)$$. It is shown that {$x_n$} converges strongly to a common element of the set of fixed points of nonexpansive semigroups and the set of solutions of the variational inequality for an inverse strongly-monotone mapping which solves some variational inequality.

• 대한수학회논문집
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• 제17권1호
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• pp.37-51
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• 2002

Let X be a reflexive Banach space with a uniformly Gateaux differentiable norm, C a nonempty bounded open subset of X, and T a continuous mapping from the closure of C into X which is locally pseudo-contractive mapping on C. We show that if the closed unit ball of X has the fixed point property for nonexpansive self-mappings and T satisfies the following condition: there exists z $\in$ C such that ∥z-T(z)∥<∥x-T(x)∥ for all x on the boundary of C, then the trajectory tlongrightarrowz$_{t}$$\in$C, t$\in$[0, 1) defined by the equation z$_{t}$ = tT(z$_{t}$)+(1-t)z is continuous and strongly converges to a fixed point of T as t longrightarrow 1￣.ow 1￣.

• 전자공학회논문지B
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• 제28B권9호
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• pp.739-746
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• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• 대한화학회지
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• 제61권1호
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• pp.12-15
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• 2017

We presented a mapping the work function of the vanadium pentoxide ($V_2O_5$) nanonet structures by scanning Kelvin probe microscopy (SKPM). In this measurement, the $V_2O_5$ nanonet was self-assembled via dropping the solution of $V_2O_5$ nanowires (NWs) onto the $SiO_2$ substrate and drying the solvent, resulting in the networks of $V_2O_5$ NWs. We found that the SKPM signal as a surface potential of $V_2O_5$ nanonet is attributed to the contact potential difference (CPD) between the work functions of the metal tip and the $V_2O_5$ nanonet. We generated the histograms of the CPD signals obtained from the SKPM mapping of the $V_2O_5$ nanonet as well as the highly ordered pyrolytic graphite (HOPG) which is used as a reference for the calibration of the SKPM tip. By using the histogram peaks of the CPD signals, we successfully estimated the work function of ~5.1 eV for the $V_2O_5$ nanonet structures. This work provides a possibility of a nanometer-scale imaging of the work function of the various nanostructures and helps to understand the electrical characteristics of the future electronic devices.

• 한국지능시스템학회논문지
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• 제11권9호
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• pp.849-854
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• 2001

Bishop이 제안한 Generative Topographic Mapping(GTM)은 Kohonen이 제안한 자율 학습 신경망인 Self Organizing Maps(SOM)의 확률 버전이다. GTM은 데이터가 생성되는 확률 분포를 잠재 변수, 혹은 은닉 변수를 사용하여 모형화한다. 이것은 SOM에서는 구현될 수 없는 GTM만의 특징이며, 이러한 특징으로 인하여 SOM의 한계들을 극복할 수 있게 된다. 본 논문에서는 이러한 GTM 모형에 베이지안 학습(Bayesian learning)을 결합하여 작은 오분류율을 가지는 분류 알고리즘인 베이지안 GTM(Bayesian GTM)을 제안한다. 이 알고리즘은 기존의 GTM의 빠른 계산 처리 능력과 데이터에 대한 확률 분포, 그리고 베이지안 추론의 정확성을 이용하여 기존의 분류 알고리즘보다 우수한 결과를 얻게 된다. 본 논문에서는 기존의 분류 알고리즘에서 많이 실험하였다. 학습 데이터를 통하여 이를 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• International Journal of Advanced Culture Technology
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• 제10권3호
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• pp.164-173
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• 2022

This study aimed to explore the themes and parameters of environmental health behaviors based on Rogers' protection motivation theory through a systematic mapping review. Using a systematic approach, a literature review was conducted of articles that adopted Rogers' protection motivation theory. A total of 11 articles on protection motivation theory using participants and environmental health as outcomes were identified in a search of CINAHL, Cochrane Library, EMBASE, Eric, PsycARTICLES, PubMed, and RISS between September 1 and September 8, 2021. Themes related to the environment and personal behaviors between 2002 and 2021 were extracted. The parameters based on protection motivation theory were identified through systematic mapping as fear appraisal, rewards of maladaptive response, severity, vulnerability, costs of adaptive response, response efficacy, self-efficacy, and intention. Self-efficacy and response efficacy considerably affected environmental health behaviors. Emotional fear appeal related to environmental hazards motivates an internal process that alters the threat appraisal and their coping appraisal. Environmental behavior perception and intention influenced on environmental health behaviors with small effect sizes. Therefore, a deeper understanding of the severity of environmental health issues could lead to the development of helpful, effective, and intensive interventions to promote healthcare among the vulnerable population.

• 대한수학회논문집
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• 제27권1호
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• pp.175-183
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• 2012

Let $\mathcal{P}_n$ be the set of all monic integral self-reciprocal poly-nomials of degree n whose all zeros lie on the unit circle. In this paper we study the following question: For P(z), Q(z)${\in}\mathcal{P}_n$, does there exist a continuous mapping $r{\rightarrow}G_r(z){\in}\mathcal{P}_n$ on [0, 1] such that $G_0$(z) = P(z) and $G_1$(z) = Q(z)?.

• 한국초등과학교육학회지:초등과학교육
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• 제28권3호
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• pp.292-303
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• 2009

In this study, we investigated the characteristics of the analogies, the mapping understanding, and the mapping errors on saturated solution of scientifically-gifted and general elementary students. Fifth graders (n=60) at four scientifically-gifted education institutes in Seoul and/or Gyeonggi province and fifth graders (n=91) at three elementary schools in Seoul were selected and assigned to the scientifically-gifted group and the general group respectively. After the students of each group performed the experiment and were taught about the target concept in the first class, they administered the test on the self-generating analogies on the target concept in the second class. The results revealed that the students in the scientifically-gifted group made more analogies, especially verbal/pictorial, structural/functional, enriched, and higher systematic ones, and had deeper understanding of the analogy than those in the general group. The numbers of the shared attributes included in the student-generated analogies and the scores of the mapping understanding of the students in the scientifically-gifted group were significantly higher than those in the general group. The students in the scientifically-gifted group had fewer mapping errors than those in the general group. However, not a few students in the scientifically-gifted group had at least one mapping error. Educational implications of these findings are discussed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.287-290
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• 2001

Bishop이 제안한 generative Topographic Mapping(GTM)은 Kohonen이 제안한 자율 학습 신경망인 Self Organizing Maps(SOM)의 확률적 버전이다. 본 논문에서는 이러한 GTM 모형에 베이지안 추론을 결합하여 작은 오분류율을 가지는 분류 알고리즘인 베이지안 GTM(Bayesian GTM)을 제안한다. 이 방법은 기존의 GTM의 빠른 계산 처리 능력과 베이지안 추론을 이용하여 기존의 분류 알고리즘보다 우수한 결과가 나타남을 실험을 통하여 확인하였다.