• Title, Summary, Keyword: support function

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Effects of a Closed Chain Movement of the Support Surface on the Balance of Adults (지지면에 따른 닫힌 사슬운동이 성인의 균형에 미치는 영향)

  • Moon, Sung-Gi;Lee, Sang-Ho
    • The Journal of Korean Society for Neurotherapy
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    • v.22 no.3
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    • pp.19-23
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    • 2018
  • Purpose The purpose of this study was to investigate the change of balance ability by performing closed chain exercise on stable support surface and unstable support surface in twenties. Methods This study randomly selected 15 students in the closed chain exercise group on the stable support side and 15 on the closed chain exercise group on the unstable support side. Balance ability was measured before and after the start of exercise and static balance was measured by OLT(One Leg Standing Test) and FRT (Functional Reach Test). Result The changes of the function reach test of the closed chain movement according to the ground type were significant in the unstable and stable support surfaces and the change of function reach test after the intervention in the two groups was significantly improved compared with the closed chain movement respectively. The one leg standing test changes of the closed chain movement according to the ground type showed significant results on the unstable and stable support surfaces, but there was no significant difference in the one leg standing test changes after intervention between the two groups. Conclusion The effect of closed chain training on ground type is unstable. The change of function reach test and one leg standing test of the closed chain exercise group on the stable support surface resulted in significant changes after exercise, but there was a significant difference in the balance ability of function reach test change after intervention between the groups.

On the Support Vector Machine with the kernel of the q-normal distribution

  • Joguchi, Hirofumi;Tanaka, Masaru
    • Proceedings of the IEEK Conference
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    • pp.983-986
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    • 2002
  • Support Vector Machine (SVM) is one of the methods of pattern recognition that separate input data using hyperplane. This method has high capability of pattern recognition by using the technique, which says kernel trick, and the Radial basis function (RBF) kernel is usually used as a kernel function in kernel trick. In this paper we propose using the q-normal distribution to the kernel function, instead of conventional RBF, and compare two types of the kernel function.

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Mixed-effects LS-SVR for longitudinal dat

  • Cho, Dae-Hyeon
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.2
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    • pp.363-369
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    • 2010
  • In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

e-SVR using IRWLS Procedure

  • Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.1087-1094
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    • 2005
  • e-insensitive support vector regression(e-SVR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the quadratic problem of e-SVR with a modified loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of e-SVR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for e-SVR.

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Support vector quantile regression for autoregressive data

  • Hwang, Hyungtae
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1539-1547
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    • 2014
  • In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

Relationship between the Characteristics of Caregivers and Adults with Intellectual Disability and the Social Support, Family Function, and Rehabilitation Needs in Caregivers (성인기 지적장애인과 주부양자의 특성, 사회적지지, 가족기능, 재활의 필요성 사이에 관련성)

  • Moon, Jonghoon;Kim, Yesoon;Oh, Hyunmin;Hong, Bokyoon;Ho, Seunghee
    • Journal of The Korean Society of Integrative Medicine
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    • v.6 no.4
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    • pp.171-182
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    • 2018
  • Purpose : The purpose of this study was to examine the relationship between the characteristics of caregivers and adults with intellectual disability, and social support, family function, and rehabilitation needs in caregivers. Methods : A total 98 pairs of adults with intellectual disability and their caregivers participated in this study. The researchers examined the general characteristics of the adults with intellectual disability and their caregivers. The evaluation included analysis of the level of activities of daily living, ability to communicate, and health status of the adults with intellectual disability, while the family income, health status, utility and the need for rehabilitation, social support (multidimensional scaled perceived social support, MSPSS) and family function (adaptation, partnership, growth, affection, resolve, and APGAR index) of the caregivers were measured. The data collected were analyzed to determine the relationship of the characteristics of adults with intellectual disability and the social support, family function, and rehabilitation needs of caregivers using regression and correlation analysis. Results : The rehabilitation needs were significantly correlated with the age of the adults with intellectual disability (p<.01), and the subjective health status of the caregivers (p<.05). The education level of the caregivers affected social support significantly ($R^2=.058$, p=.021). The communication ability of the adults with intellectual disability affected family function ($R^2=.071$, p=.01). The social support of caregivers had a significant effect on family function ($R^2=.488$, p<.001). Conclusion : These findings suggest that the barriers to community rehabilitation should be lowered, and the authors discussed the results of the present investigation.

Selection of Optimal Supporting Position to Maximize Natural Frequency of the Structure Using Frequency Response Function (주파수 응답함수를 이용한 구조물 고유진동수 극대화를 위한 최적 지지점 선정)

  • 박용화;정완섭;박윤식
    • Journal of KSNVE
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    • v.10 no.4
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    • pp.648-654
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    • 2000
  • A procedure to determine the realizable optimal positions of rigid supports is suggested to get a maximum fundamental natural frequency. a measured frequency response function based substructure-coupling technique is used to model the supported structure. The optimization procedure carries out the eigenvalue sensitivity analysis with respect to the stiffness of supports. As a result of such stiffness optimization, the optimal rigid-support positions are shown to be determined by choosing the position of the largest stiffness. The optimally determined support conditions are verified to satisfy the eigenvalue limit theorem. To demonstrate the effectiveness of the proposed method, the optimal support positions of a plate model are investigated. Experimental results indicate that the proposed method can effectively find out the optimal support conditions of the structure just based on the measured frequency response functions without any use of numerical model of the structure.

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Support Vector Quantile Regression Using Asymmetric e-Insensitive Loss Function

  • Shim, Joo-Yong;Seok, Kyung-Ha;Hwang, Chang-Ha;Cho, Dae-Hyeon
    • Communications for Statistical Applications and Methods
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    • v.18 no.2
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    • pp.165-170
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    • 2011
  • Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

Sparse Kernel Regression using IRWLS Procedure

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.3
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    • pp.735-744
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    • 2007
  • Support vector machine(SVM) is capable of providing a more complete description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse kernel regression(SKR) to overcome a weak point of SVM, which is, the steep growth of the number of support vectors with increasing the number of training data. The iterative reweighted least squares(IRWLS) procedure is used to solve the optimal problem of SKR with a Laplacian prior. Furthermore, the generalized cross validation(GCV) function is introduced to select the hyper-parameters which affect the performance of SKR. Experimental results are then presented which illustrate the performance of the proposed procedure.

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GACV for partially linear support vector regression

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.391-399
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    • 2013
  • Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.