• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• Structural Engineering and Mechanics
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• 제84권1호
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• pp.1-16
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• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• 한국전문물리치료학회지
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• 제19권4호
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• pp.16-23
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• 2012

This study examined the effects of custom-made foot orthotics on the temporal-spatial gait parameters in children with cerebral palsy. Twenty spastic bilateral cerebral palsy (spastic CP) children (11 boys and 9 girls) participated in this study. GAITRite was used to examine the velocity, cadence, step length differential, step length, stride length, stance time, single support time, double support time, base of support, and toe angle while walking with and without foot orthotics. The differences in temporal-spatial parameters were analyzed using paired t-test. The significance level was set at .05. The velocity, cadence, both step lengths, both stride lengths, both bases of support and right toe angle significantly increased when the children with spastic CP with foot orthotics compared to without foot orthotics (p<.05). The step length differential between the two extremities, left stance time and left single support time, significantly decreased with foot orthotics (p<.05). Right stance time, right single support time, both double support times and left toe angle showed little change (p>.05). This study demonstrated that foot orthotics were beneficial for children with spastic CP as a gait assistance tool.

• 중환자간호학회지
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• 제15권3호
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• pp.115-127
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• 2022

Purpose : This study aimed to investigate sleep parameters and to identify differences according to respiratory support therapy, sedation, and pain medication in intensive care unit (ICU) patients. Methods : In this observational study, sleep parameters were measured using actigraphy. We observed 45 sleep events in 30 ICU patients receiving respiratory support therapy. We measured the sleep parameters, time, efficiency, and wakefulness after sleep onset (WASO). The differences in sleep parameters according to the respiratory support therapy were analyzed using the Mann-Whitney U test. Results : The average daily sleep time of the participants was 776.66±276.71 minutes, of which more than 60% accounted for daytime sleep. During night sleep, the duration of WASO was 156.93±107.91 minutes, and the frequency of WASO was 26.02±25.82 times. The high flow nasal cannula (HFNC) group had a significantly shorter night sleep time (𝑥²=7.86, p =.049), a greater number of WASO (𝑥²=5.69, p =.128), and a longer WASO duration (𝑥²=8.75, p =.033) than groups of other respiratory therapies. Conclusion : ICU patients on respiratory support therapy experienced sleep disturbances such as disrupted circadian rhythm and sleep fragmentation. Among respiratory support regimens, HFNC was associated with poor sleep parameters, which appears to be associated with the insufficient use of analgesics. The results of this study warrant the development of interventions that can improve sleep in ICU patients receiving respiratory support, including HFNC.

• 대한기계학회논문집
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• 제12권5호
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• pp.1003-1015
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• 1988

본 연구에서는 상하운동 또는 좌우흔들림운동하에서의 튜브진동에 대한 실험 을 통하여 튜브지지대 인자의 영향을 고찰하고자 하였다.실험은 양단이 고정된 튜 브의 중앙에 지지대가 있는 두마디 튜브의 실험장치에서 수행되었다. 실험시 고찰된 인자들은 튜브편심율, 튜브지지대 두께, 튜브와 튜브지지대간의 간격, 튜브지지대의 위치, 튜브주파수, 선형도, 그리고 튜브거동 형태(nature of the dynamic interaction )등이다.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.679-683
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• 2007

Vapnik의 통계적 학습이론은 분류, 회귀, 그리고 군집화를 위하여 SVM(support vector machine), SVR(support vector regression), 그리고 SVC(support vector clustering)의 3가지 학습 알고리즘을 포함한다. 이들 중에서 SVC는 가우시안 커널함수에 기반한 지지벡터를 이용하여 비교적 우수한 군집화 결과를 제공하고 있다. 하지만 SVM, SVR과 마찬가지로 SVC도 커널모수와 정규화상수에 대한 최적결정이 요구된다 하지만 대부분의 분석작업에서 사용자의 주관적 경험에 의존하거나 격자탐색과 같이 많은 컴퓨팅 시간을 요구하는 전략에 의존하고 있다. 본 논문에서는 SVC에서 사용되는 커널모수와 정규화상수의 효율적인 결정을 위하여 차분진화를 이용한 DESVC(differential evolution based SVC)를 제안한다 UCI Machine Learning repository의 학습데이터와 시뮬레이션 데이터 집합들을 이용한 실험을 통하여 기존의 기계학습 알고리즘과의 성능평가를 수행한다.

• International Journal of Concrete Structures and Materials
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• 제5권1호
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• pp.29-33
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• 2011

This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

• PNF and Movement
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• 제17권1호
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• pp.111-118
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• 2019

Purpose: This study aimed to investigate the correlation between trunk stabilization muscle activation and the parameters of gait analysis in healthy individuals. Methods: Thirty healthy adults (15 male, 15 female) with no history of lower back pain (LBP) or current musculoskeletal and neurological injuries were studied. Trunk stabilization muscle activation (e.g., external oblique, internal oblique, transverse abdominis, erector spinae) were assessed using surface electromyography. To analyze gait, we measured temporal parameters (e.g., gait velocity, single support phase, double support phase, swing phase, and stance phase) and a spatial parameter (e.g., H-H base of support). Results: A statistically significant correlation was found between the internal oblique, transverse abdominis, and erector spinae muscle activity and gait velocity, single support phase, double support phase, swing phase, and stance phase. No statistically significant correlation was found between the external oblique muscle activity and the gait velocity, single support phase, double support phase, swing phase, and stance phase. No statistically significant correlation was found between the external oblique, internal oblique, transverse abdominis, and erector spinae muscle activity and the spatial parameter. Conclusion: This study demonstrated that a relationship exists between trunk stabilization muscle activation and temporal parameter (i.e., gait velocity, single support phase, double support phase, swing phase, and stance phase) during gait analysis. Therefore, the trunk's stabilizer muscles play an important role in the gait of healthy individuals.

• 전력전자학회논문지
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• 제10권5호
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• pp.468-480
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• 2005

서보 시스템의 전체 제어 성능은 기계적 상수의 변화와 부하 토크의 영향을 크게 받는다. 그러므로 서보 시스템의 성능을 향상시키기 위해서는 기계적 상수와 부하 토크를 정확히 알 필요가 있다. 본 논문에서는 Support Vector Regression(SVR)을 이용한 기계적 상수와 부하 토크 추정 알고리즘을 제안한다. 실험 결과는 제안된 SVR 알고리즘이 서보 시스템의 기계적 상수와 부하 토크를 정확하게 추정하고 있음을 보여준다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 춘계학술대회 논문집
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• pp.1065-1070
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• 2004

When the track designer analyze the track structure uses many known & unknown parameters. Unknown parameters, equivalent rail support spring factor, unit rail support spring factor, track damping coefficient, should be assumed. Known parameters are section properties (area, section factor, etc), material properties(modulus of elasticity, mass, etc) and track conditions(wheel load, loading conditions, gauge, etc.). In the assumption of track design parameters, some parameters can be overestimated or under estimated. The purpose of this study is to verify design parameters used in track design, in the way of experimental measurements. Data of displacements, banding stresses, loads, accelerations are measurable at track site. From these data, unknown parameters are derived. Compare these assumed and derived parameters, estimate the entire track stability.