• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.738-741
• /
• 2004

The overall performance of AC servo system is greatly affected by the uncertainties of unpredictable mechanical parameter variations and external load disturbances. Therefore, to compensate this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an online identification method of mechanical parameters/load disturbances for AC servo system using Support Vector Regression (SVR). The proposed methodology advocates analytic parameter regression directly from the training data, rather than adaptive controller and observer approaches commonly used in motion control applications. The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with large measurement noise.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.4
• /
• pp.765-775
• /
• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.1875-1881
• /
• 2007

Deformations of bridge deck ends on abutments or on transition between bridge decks can cause extreme deformations on track. Especially, since slab track is fixed onto the bridge deck slab on concrete slab track-installed bridges, deformations of bridge deck ends directly affect the track behavior, and thus these interactions can bring about the premature failure of rail fastenings or other deteriorations to lower the serviceability. In this study, a foreign standard to evaluate forces on track components caused by the track-bridge interactions and the serviceability of bridge deck ends is investigated, and for the real bridges, the serviceability of bridge deck ends according to several parameters of bridge and track is analyzed. It is found that arrangements and spring coefficients of bridge bearings, as well as distance between bridge bearing and last rail support, support spacings, rail support spring coefficient, are very important parameters.

• Structural Monitoring and Maintenance
• /
• v.4 no.4
• /
• pp.365-379
• /
• 2017

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

• Environmental Engineering Research
• /
• v.24 no.4
• /
• pp.559-565
• /
• 2019

In order to alleviate the environmental pressure caused by production process of thermal power plants, the application of cleaner production is imperative. To estimate the implementation effects of cleaner production in thermal plants and optimize the strategy duly, it is of great significance to take a comprehensive evaluation for sustainable development. In this paper, a hybrid model that integrated the analytic hierarchy process (AHP) with least squares support vector machine (LSSVM) algorithm optimized by grid search (GS) algorithm is proposed. Based on the establishment of the evaluation index system, AHP is employed to pre-process the data and GS is introduced to optimize the parameters in LSSVM, which can avoid the randomness and inaccuracy of parameters' setting. The results demonstrate that the combined model is able to be employed in the comprehensive evaluation of the cleaner production in the thermal power plants.

• Journal of the Semiconductor & Display Technology
• /
• v.10 no.3
• /
• pp.75-81
• /
• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• The KIPS Transactions:PartD
• /
• v.16D no.5
• /
• pp.729-736
• /
• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.

• PNF and Movement
• /
• v.16 no.3
• /
• pp.355-363
• /
• 2018

Purpose: The purpose of this study was to obtain detailed and quantified data concerning the effects of plantarflexor muscle fatigue induced in the non-paretic side on the spatial and temporal gait parameters of the bilateral lower extremities during walking in stroke patients. Methods: This study was conducted on 20 patients with chronic stroke. The load contraction fatigue test was applied to induce muscle fatigue in the non-paretic plantarflexor muscle. Step length, stride length, double support, gait velocity and cadence, and functional ambulatory profile (FAP) score in the bilateral lower extremities were measured using a gait analysis system in order to investigate changes in temporal and spatial gait parameters caused by muscle fatigue on the non-paretic side. The statistical significance of the results was evaluated using a paired t-test. Results: A review of the results for gait parameters revealed a significant increase in double support (p<0.05) and a significant decrease in step length, stride length, gait velocity and cadence, and FAP score (p<0.05). Conclusion: These results indicate that the muscle fatigue in the non-paretic side of the stroke patients also affected the paretic side, which led to a decrease in gait functions. This implies a necessity to perform exercise or training programs in a range of clinical aspects not causing muscle fatigue.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.19 no.1
• /
• pp.75-85
• /
• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

• Journal of Nutrition and Health
• /
• v.35 no.5
• /
• pp.512-523
• /
• 2002

The objective of this study was to investigate the nutritional status, biochemical parameters, lipid and electrolytes concentrations of the enteral nutrition patients according to the duration of enteral nutrition. Eighteen neurosurgery patients in the intensive care unit (ICU) at K University Hospital were subjected in this study. The duration of enteral nutrition was classified into under or over six month of period. Anthropometric, biochemical, clinical, and dietary assessments were performed. Patients' intakes of energy and protein were insufficient, from 82% to 95% of their requirements. Mid-arm muscle circumference (MAMC) and mid-am muscle area (MAMA) were significantly lower in patients over six months of enteral nutrition than those in patients under six months. The subjects were malnourished as indicated by nutrition-related parameters such as hemoglobin, albumin, total lymphocyte count (TLC), tricep skinfold thickness (TSF), mid-arm circumference (MAC), MAMC, and MAMA. Serum chloride level of the patients eve, six months of enteral nutrition was lower (94.7 $\pm$ 3.4 mmo1/1) significantly as compared to that of patients (99.3 $\pm$ 3.5 mmol/ 1) under six months. Urinary sodium and chloride levels were lower in the longer time of enteral nutrition patients than those of shorter period of enteral nutrition patients (p < .05). While serum phospholipid level was higher in the patients over six months of enteral nutrition, other blood biochemical parameters and electrolyte concentrations did not show any differences with the duration of enteral nutrition. Neurosurgery patients in the ICU undergoing long-term enteral nutrition tube-feeding were malnourished and had a variety of metabolic complications. The duration of enteral nutrition could affect the patients' nutritional status, biochemical parameters, and electrolytes balance. The patients who require nutritional support over an extended time need the continuous follow-up care and monitoring by the nutrition support team for laboratory, clinical, and nutritional assessments.