• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1751-1755
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• 2014

Background: In this study prognostic correlations of histopathologic parameters and the Ki-67 proliferation index and as well as the diagnostic value of immunohistochemical markers in pheochromocytomas were evaluated. Materials and Methods: A total of 22 patients diagnosed with a pheochromocytoma between 2000-2010 in Izmir Katip Celebi University Ataturk Training and Research Hospital were included. Diagnostic value of the PASS scoring system, and prognostic correlations of histopathologic parameters and Ki-67 proliferation index were investigated. SPSS for Windows 17.0 software was used for statistical analysis. Results: There was no statistically significant correlation between recurrence and clinicopathologic parameters or the PASS score (PASS>4). In addition, there were no statistically significant correlations between PASS score and clinicopathologic parameters, such as diameter (5 cm), weight (>100g), gender (female/male ratio) and age (25-45/45-55/>55). Besides, there were no significant correlation between diameter and clinicopathological parameters and also recurrence. However, there was a statistically significant correlation between Ki-67 proliferation index and capsule invasion (p=0.047). Conclusions: Some but not most of the findings in our study were concordant with the literature. To clarify relationships, investigations with standard scoring systems which are not affected by subjective factors and feature appropriate histopathological criteria should be made on larger study groups.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.631-636
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• 2006

In this paper, an algorithm of time series function forecasting using GMDH(group method of data handling) algorithm that gives more weight to the recent data is proposed. Traditional methods of GMDH forecasting gives same weights to the old and recent data, but by the point of view that the recent data is more important than the old data to forecast the future, an algorithm that makes the recent data contribute more to training is proposed for more accurate forecasting. The average error rate of electric power demand forecasting by the traditional GMDH algorithm which does not use data weighting algorithm is 0.9862 %, but as the result of applying the data weighting GMDH algorithm proposed in this paper to electric power forecasting demand the average error rate by the algorithm which uses data weighting algorithm and chooses the best data weighting rate is 0.688 %. Accordingly in forecasting the electric power demand by GMDH the proposed method can acquire the reduced error rate of 30.2 % compared to the traditional method.

• Journal of Power System Engineering
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• v.20 no.6
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• pp.80-86
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• 2016

In this study, The watertight damper was designed to improve conventional DN 350A butterfly valve. The FSI(Fluid-Structure Interaction) analysis has performed to investigate the safety factor for the watertight damper. When watertight damper of disk was closed, the disk of pressure value is constant. However depending on the opening angle of disk, the flow velocity and pressure are changed. The maximum velocity was appeared at the end of disk on the small outlet area of duct. When the opening angel of disk is $90^{\circ}$, the maximum velocity was appeared at the center of ending disk. So we were found the opening angle of disk is bigger, the flow rate is increased and velocity is also increased from the result of FSI analysis. We can find the least deformation and stress when the opening angel of damper is $90^{\circ}$. When the $45^{\circ}$ opening angle of disk, the largest deformation and stress was found and the minimum safety factor 1.3 was calculated. As a result, we found that the structure of watertight damper is safe enough irrespective of opening angel.

• The Journal of Korean Medicine
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• v.22 no.3
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• pp.129-140
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• 2001

Objectives : The aim of this study was to evaluate the effects of Punsimgi-eum(fenxingi-yin) in rat model of depression induced by chronic mild stress (CMS). Methods : The change of the consumption of sucrose solution was measured, and the open field test and passive avoidance conditioning test were performed to investigate the anti-depressive effect of Punsimgi-eum(fenxingi-yin). Results : The results were as follows; I. The consumption of sucrose solution was significantly reversed in the test group at 5, 7 and 8 weeks, but there was no significant change in the control group. 2. There was no significant change of body weight after 8 weeks in each group 3. In the open field test, there was significant increase of exploratory activity in the test group. 4. In the passive avoidance test, the test group showed differences in the maintenance of retention from the control group at the 2-week training-testing interval. Conclusions : The present results suggest that Punsimgi-eum(fenxingi-yin) has an anti-depressive effect.

• Journal of the Korean Applied Science and Technology
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• v.37 no.5
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• pp.1314-1322
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• 2020

Sports drinks help optimize and improve performance by delaying and eliminating the buildup of fatigue-causing substances in the body during exercise. Saengmaeg-san is a nature-friendly traditional beverage that has no side effects on the human body and can quench thirst. However, studies on the relationship between exercise ability and Saengmaeg-san are insufficient. The purpose of this study was to prescribe Saengmaeg-san during the summer training period of 4 weeks and to analyze the effect on body composition and exercise performance. Seventeen male participants were divided into 3 groups (Saengmaeg-san acid intake group [n=9], placebo group [n=8]), and body composition (height, weight, muscle mass, fat mass, BMI) and conducted exercise performance (total exercise time and HRmax). In our study, Saengmaeg-san intake had a positive effect on exercise performance, such as decreased body fat percentage, increased exercise time, and decreased HRmax. Therefore, Saengmaeg-san showed the potential as a sports drink. In the future, additional studies on fatigue-related substances, immune function-markers, and blood lipids are needed in order to clearly explain the change in exercise performance due to consumption of Saengmaeg-san.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.343-348
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• 2005

The goal of this paper is to study the joint effect of factors of neural network teaming procedure. There are many factors, which may affect the generalization ability and teaming speed of neural networks, such as the initial values of weights, the learning rates, and the regularization coefficients. We will apply a constructive training algerian for neural network, then patterns are trained incrementally by considering them one by one. First, we will investigate the effect of these factors on generalization performance and learning speed. Based on these factors' effect, we will propose a joint method that simultaneously considers these three factors, and dynamically hue the learning rate and regularization coefficient. Then we will present the results of some experimental comparison among these kinds of methods in several simulated nonlinear data. Finally, we will draw conclusions and make plan for future work.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.1
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• pp.63-70
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• 2010

Purpose : The purpose of this study was to investigate the effects of virtual reality(VR) program on standing balance in stroke with sensory deficit. Methods : Subjects were assigned randomly to either VR group (n=28) or the control group (n=31) when study began. Both groups were conducted electricity therapy, exercise therapy 5 times per week during six weeks, and a group who have VR group 3 times per week during six weeks. Both groups assessed for standing balance before and after virtual reality program. Results : In standing balance, the virtual reality program which have sensory deficit in stroke group, open eyes from the center area of the body and center line significantly reduced the training period(p<.05). The virtual reality program to instantly move the weight through maximum voluntary safety limits to sensory deficit of virtual reality program(p<.05). Conclusion : Stroke patients with sensory deficit affects the recovery of the standing balance. In addition, virtual reality program was stroke patients with sensory deficit affects the recovery of standing balance.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.391-397
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• 2011

Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered. Based on these clustering centers, least square method is applied for T-S fuzzy linear model parameters. As a result, IPMSM can be modeled as T-S fuzzy model for T-S fuzzy control of them.

• The Journal of Korean Physical Therapy
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• v.18 no.5
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• pp.43-51
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• 2006

Purpose: The purpose of this study was to examine the effects of static stretching and Evjenth-Hamberg stretching on isokinetic muscle strength of knee flexors and extensors. Methods: The subjects were composed of eighty healthy males without weight-training experience. ROM of knee joint measured active maximal extension and isokinetic Peak Torque measured $60^{\circ}/sec,\;120^{\circ}/sec$ using an the En-Knee. Three tests(Baseline, 4 weeks, 8 weeks, respectively) was operated to examine change of each variable. Data were analyzed with a $2{\times}3$ analysis of variance ($group{\times}test$) for repeated measures on last factor by SPSS package 10.0. The data analysis revealed muscle strength were dependent on stretching method. Results: The results were as follows. First, Evjenth-Hamberg stretching(E-HS) was more effective than static stretching(SS) on ROM. Second, Peak Torque of knee flexors and extensors was improved in both methods by each time. but E-HS was more improved than SS. Conclusion: In conclusion, This study indicates that E-HS is more efficient than SS on muscle strength improvement.