• Journal of Community Nutrition
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• v.2 no.2
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• pp.135-140
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• 2000

The objectives of this study were to determine whether older Americans would provide valid energy intake information using a 24-hr recall method and to determine which characteristics were predictive of under-report of energy intake. We conducted 24-hour recalls on 83 male and 105 female community-dwelling older adults(66-87y) in the USA to assess energy(EI) and nutrient intakes. Basal metabolic rate(BMR) was calculated from age-and gender-specific equations of Schofield. Under-reporting was defined a priori as EI : BMR<0.9. Subjects volunteered demographic information, underwent depression and cognition exams, and completed a Level II Nutrition Risk Screen. Differences between under- and adequate-reporters were assessed using t-tests for characteristics and macro-nutrient profile. Stepwise regression analyses were used to predict under-reporting status. Under-reporting of EI occurred in 34% of the sample. Neither geriatric depression scale(GDS) score, nor self-reported weight loss were related to under-reporting. On average, under-reporters had higher body mass indices. The most significant variables for the main effect to predict the ratio of energy intake to estimated basal metabolic rate(EI : BMRest) were BMI and age. Using a standard cut-off of 76% of the recommended dietary allowances for Americans, under-reporters were consistently more likely to be classified as having inadequate nutrient, as well as energy, intakes. (J Community 2(2) 135∼140, 2000)

• Current Optics and Photonics
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• v.5 no.4
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• pp.421-430
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• 2021

The multifocal multiphoton microscopy (MMM) enables high-speed imaging by the concurrent scanning and detection of multiple foci generated by lenslet array or diffractive optical element. The MMM system mainly suffers from crosstalk generated by scattered emission photons that form ghost images among adjacent channels. The ghost image which is a duplicate of the image acquired in sub-images significantly degrades overall image quality. To eliminate the ghost image, the photon reassignment method was established using maximum likelihood estimation. However, this post-processing method generally takes a longer time than image acquisition. In this regard, we propose a novel strategy for rapid noise reduction in the MMM system based upon Monte-Carlo (MC) simulation. Ballistic signal, scattering signal, and scattering noise of each channel are quantified in terms of photon distribution launched in tissue model based on MC simulation. From the analysis of photon distribution, we successfully eliminated the ghost images in the MMM sub-images. If the priori MC simulation under a certain optical condition is established at once, our simple, but robust post-processing technique will continuously provide the noise-reduced images, while significantly reducing the computational cost.

• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.419-435
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• 2021

This paper deals with numerical treatment of singularly perturbed differential equations involving small delays. The highest order derivative in the equation is multiplied by a perturbation parameter 𝜀 taking arbitrary values in the interval (0, 1]. For small 𝜀, the problem involves a boundary layer of width O(𝜀), where the solution changes by a finite value, while its derivative grows unboundedly as 𝜀 tends to zero. The considered problem contains delay on the convection and reaction terms. The terms with the delays are approximated using Taylor series approximations resulting to asymptotically equivalent singularly perturbed BVPs. Inducing exponential fitting factor for the term containing the singular perturbation parameter and using central finite difference for the derivative terms, numerical scheme is developed. The stability and uniform convergence of difference schemes are studied. Using a priori estimates we show the convergence of the scheme in maximum norm. The scheme converges with second order of convergence for the case 𝜀 = O(N^-1) and for the case 𝜀 ≪ N^-1, the scheme converge uniformly with first order of convergence, where N is number of mesh intervals in the domain discretization. We compare the accuracy of the developed scheme with the results in the literature. It is found that the proposed scheme gives accurate result than the one in the literatures.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.379-394
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• 2022

Shallow footings usually belonged to the category of thick plate structures. For accurate analysis of thick plates, the contribution of out-of-plane components of the stress tensor should be considered in the formulation. Most of the available shallow footing models are based on the classical plate theories, which usually neglect the effects of the out-of-plane stresses. In this study, a mixed-field plate finite element model (FEM) is developed for the analysis of shallow footings rested on soil foundations. In addition to displacement field variables, the out-of-plane components of the stress tensor are also assumed as a priori unknown variables. For modeling the interaction effect of the soil under and outside of the shallow footings, the modified Vlasov theory is used. The tensionless nature of the supporting soil foundation is taken into account by adopting an incremental, iterative procedure. The equality requirement of displacements at the interface between the shallow footing and soil is fulfilled using the penalty approach. For validation of the present mixed FEM, the obtained results are compared with the results of 3D FEM and previous results published in the literature. The comparisons show the present mixed FEM is an efficient and accurate tool for solving the problems of shallow footings rested on subsoil.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.67-87
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• 2013

La France est un Etat unitaire $r{\acute{e}}agi$ par la Constitution. La $r{\acute{e}}forme$ de la $d{\acute{e}}centralisation$ a $d{\acute{e}}but{\acute{e}}$ par la $d{\acute{e}}claration$ 'des droits et $liber{\acute{e}}s$ des communes, des $d{\acute{e}}partements$ et des $r{\acute{e}}gions'$ qui influence plusieurs domaines de l' administration locale: la suppression du $contr\hat{o}le$ a priori et l' exercice du $contr\hat{o}le$ a posteriori par le commissaire de la Republique et par le Cour des comptes; le transfert des competences de l' Etat vers les communes, les $d{\acute{e}}partements$ et les $r{\acute{e}}gions$ avec la transmission du pouvoir de $d{\acute{e}}cision$; le transfert de moyens financiers pour financer les $d{\acute{e}}penses$ relatives aux $comp{\acute{e}}tences$ acquises; la $r{\acute{e}}forme$ du $syst{\acute{e}}me$ des fonctionnaires territoraux; le paiement des honoraires aux ${\acute{e}}lus$ locaux; la limitation du cumul des mandats.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.188-191
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• 2002

To reduce the bit-rate of video sequences by removing temporal redundancy, motion estimation techniques have been developed. However, the high computational complexity of the problem makes such techniques very difficult to be applied to high-resolution applications in a real time environment. For this reason, low computational complexity motion estimation algorithms are viable solutions. If a priori knowledge about the motion of the current block is available before the motion estimation, a better starting point for the search of n optimal motion vector on be selected and also the computational complexity will be reduced. In this paper, we present an adaptive block matching algorithm based on temporal correlations of consecutive image frames that defines the search pattern and the location of initial starting point adaptively to reduce computational complexity. Experiments show that, comparing with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(㏈) better than DS in terms of PSNR and improves as much as 50% in terms of the average number of search points per motion estimation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1238-1245
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• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.534-540
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• 1993

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.561-575
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• 2014

In this paper, we propose a penalized likelihood method to detect local spatial clusters associated with disease. The key computational algorithm is based on genlasso by Tibshirani and Taylor (2011). The proposed method has two main advantages over Kulldorff's method which is popoular to detect local spatial clusters. First, it is not needed to specify a proper cluster size a priori. Second, any type of covariate can be incorporated and, it is possible to find local spatial clusters adjusted for some demographic variables. We illustrate our proposed method using tuberculosis data from Seoul.