• Journal of International Academy of Physical Therapy Research
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• v.9 no.2
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• pp.1494-1497
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• 2018

This study aimed to determine the effect of McKenzie lumbar support on pulmonary function in Stroke patients. Twenty subjects (n=20) were divided into two groups: a McKenzie lumbar support group (MLS group=10), a control group (n=10). Pulmonary function was performed to assess its effectiveness. A spirometer was used to measure the forced vital capacity (FVC), forced expiratory volume in one second (FEV1), peak expiratory flow (PEF). The intervention was conducted for four weeks. In the MLS group, FEV1, FVC, and PEF were increased after McKenzie lumbar support. (p<0.05), while no significant differences in the variables were found in the control group (p>0.05). There were no significant differences in variables between the MLS group and the control group (p>0.05). Our findings suggest that applying Mckenzie lumbar support may be an alternative maneuver to improve pulmonary function in stroke patients.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.577-594
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• 2020

Service Function Chaining (SFC) supports services through linking an ordered list of functions. There may be multiple instances of the same function, which provides a challenge to select available instances for all the functions in an SFC and generate a specific Service Function Path (SFP). Aiming to solve the problem of SFP selection, we propose an architecture consisting of distributed SFP algorithm and central control mechanism. Nodes generate distributed routings based on the first function and destination node in each service request. Controller supervises all of the distributed routing tables and modifies paths as required. The architecture is scalable, robust and quickly reacts to failures because of distributed routings. Besides, it enables centralized and direct control of the forwarding behavior with the help of central control mechanism. Simulation results show that distributed routing tables can generate efficient SFP and the average cost is acceptable. Compared with other algorithms, our design has a good performance on average cost of paths and load balancing, and the response delay to service requests is much lower.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.107-115
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• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.543-549
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• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• Research in Community and Public Health Nursing
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• v.28 no.3
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• pp.227-239
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• 2017

Purpose: The purpose of this study was to investigate the effects of peer mentoring program on physical activity, knee joint function, self-care agency and social support, which are health conservation elements in elderly women with osteoarthritis. Methods: This study used a quasi-experimental research design. It is a pretest-and-post 1, post 2 test design of a non-equivalent control group. The subjects were elderly women aged over 65 who were diagnosed with osteoarthritis. A total of 60 patients (experimental group 30, control group 30) who registered with the Senior Welfare Center in City G and in Region D participated in this study. The data were collected from June 29th to September 4th, 2015. The collected data were analyzed with $x^2$ test, Fisher's exact test, independent t-test and repeated measurement ANOVA. Results: The experimental group showed a greater increase in physical activity, knee joint function, self-care agency and social support than the control group. Conclusion: The results indicated that the peer mentoring program is effective in increasing physical activity, knee joint function, self-care agency and social support of elderly women with osteoarthritis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.315-315
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• 2000

In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

• Journal of Korean Port Research
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• v.15 no.1
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• pp.37-46
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• 2001

Fuzzy logic control(FLC) is composed of three parts : fuzzy rule-bases, membership functions, and scaling factors. Well-defined fuzzy rule-base should contain proper physical intuition on the plant, so are needed lots of experiences of the skillful expert. When membership functions are considered, some parameters on the memberships function such as function shape, support, allocation density should be selected well. The rule of scaling factors is 'scaling'(amplifying or reducing) for both input and output signals of the FLC to fit in the membership function support and to operate the plant intentionally. To get a better performance of the FLC, it is necessary to adjust the parameters of the FLC. In general, the adaptation of the scaling factors is the most effective adjustment scheme, compared with that of the fuzzy rule-base or membership function parameters. This study proposes the adaptation scheme of the scaling factors. When the adaptation is performed on-line, the stability of the adaptive FLC should be guaranteed. The stable FLC system can be designed with stability analysis in the sense of Lyapunov stability. To adapt the scaling factors for the error signals, the concept of the conventional MRAC would be introduced into slightly modified form. A tracking accuracy of the control system would be enhanced by the modified shape and support of the membership function. The simulation is achieved on the pilot plant with the hydraulic steering control of a UCT(Unmanned Container Transporter) of which modeling dynamics have lots of severe uncertainties and modeling errors.

• Journal of the Korean Society of Physical Medicine
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• v.17 no.2
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• pp.75-82
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• 2022

PURPOSE: This study examined the correlation between the pulmonary function and respiratory muscle strengthening training on an unstable support surface and a stable support surface in stroke patients. METHODS: The study subjects were 22 stroke patients undergoing central nervous system developmental rehabilitation treatment. After excluding six dropouts, eight people in the experimental group and eight people in the control groups were classified by random sampling. Both groups performed central nervous system developmental rehabilitation therapy and were provided a 10-minute break. The experimental group was provided with an unstable support surface using Togu, and the control group was trained to strengthen the respiratory muscle in a stable support surface. Respiratory muscle strengthening training was conducted three times per week for 20 minutes. Before and after each group of experiments, a nonparametric test Wilcoxon signed rank test, and a Mann Whitney U-test analysis were used to analyze the variations between the two groups. All statistical significance levels (α) were set at 0.05. RESULTS: Both groups showed increases in the pulmonary function but showed significant differences only in the experimental group. There was a significant difference in the peak expiratory flow between the two groups. CONCLUSION: Central nervous system development rehabilitation treatment for patients with an impaired nervous system and respiratory muscle strengthening training on unstable support surfaces are effective in improving the pulmonary function of stroke patients. Therefore, they are expected to be applied to physical therapy programs to help various functional activities.