Browsing by Author "Demirtas M."

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    Treatment of mild-to-moderate hypertension with calcium channel blockers: A multicentre comparison of once-daily nifedipine gits with once-daily amlodipine
    (2003) Kes S.; Caglar N.; Canberk A.; Deger N.; Demirtas M.; Dortlemez H.; Kiliccioglu B.; Kozan O.; Ovunc K.; Turkoglu C.
    Background: Hypertension is one of the most important causes of cardiovascular disease, and treatment of hypertension leads to a significant reduction in cardiovascular mortality and morbidity. Although calcium channel blockers are regarded as an important part of the therapeutic armamentarium against cardiovascular diseases, and are among the most frequently prescribed antihypertensive medications, concern has been aroused about these drugs, particularly the short-acting dihydropyridine derivatives. However, the value of nifedipine GITS (Adalat-Crono*), the long-acting dihydropyridine, is in need of being re-established. Objective: To compare the effectiveness, safety and tolerability of once-daily nifedipine and amlodipine treatment in patients with mild-to-moderate essential hypertension. Design: Randomised multicentre trial with an open comparison of treatments for 12 weeks, with a preceding placebo run-in period of 2 weeks (patients on beta-blockers at the time of enrolment entered a mandatory 2-week wash-out period before being allowed in the placebo run-in period; this wash-out period was one week for patients using any antihypertensive medication other than beta-blockers). Setting: Nine centres (all university hospitals) in Turkey. Patients: 155 patients with essential hypertension (diastolic blood pressure 95-109 mmHg). Interventions: Initial treatment (step 1) consisted of 30 mg nifedipine GITS (n = 76; (Adalat-Crono tablets), or 5 mg amlodipine (n = 79; Norvasc† 5-mg tablets), either administered once daily, as a morning dose, or if the blood pressure was not below 140/90 mmHg, or the reduction in diastolic blood pressure was lower than 10 mmHg after a treatment period of 6 weeks, the dose was increased (Step 2) to 60 mg once daily in the nifedipine group, or 10 mg once daily in the amlodipine group. Main efficacy parameter: Diastolic blood pressure at trough after 12 weeks of active compound therapy adjusted to baseline. Results: After 12 weeks of treatment, the mean diastolic blood pressure was 83.1 and 81.9 mmHg, in the nifedipine and amlodipine groups, respectively (p = 0.436). The mean decrease in systolic blood pressure (28.5 ± 11.9 and 28.2 ± 11.2 mmHg in the nifedipine and amlodipine groups, respectively) and the mean decrease in diastolic blood pressure (16.4 ± 7.0 and 17.5 ± 6.9 mmHg in the nifedipine and amlodipine groups, respectively), as well as the responder rates (88.1 % and 92.1%, in the nifedipine and amlodipine groups, respectively) were comparable at the end of the study. No significant differences between groups were detected in the efficacy parameters assessed in this study. Both drugs were well tolerated. The overall incidence of adverse events was 7.9% in the nifedipine group and 10.1% in the amlodipine group. However, more patients discontinued treatment prematurely in the amlodipine group (13 patients; 19.7%), than in the nifedipine group (four patients; 5.6%). Conclusions: The results of this study demonstrated that once-daily nifedipine in GITS formulation and amlodipine are comparably safe and effective treatment options in patients with mild-to-moderate essential hypertension.
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    Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms
    (2005) Ustun S.V.; Demirtas M.
    This paper presents a method of tuning Proportional Integral (PI) controller coefficients in the off-line control of a nonlinear system. In this method, the first step is the identification of the system via Artificial Neural Networks (ANNs), using maximum overshoot and settling time obtained from the application circuit for different Kp-Ki pairs. With this in mind, multi-layer ANN, which uses back-propagation of the error algorithm, was used as the learning algorithm. In the second step, the purpose is to find the optimum controller coefficients using the ANN model as the objective function via Genetic Algorithms (GAs). A Digital Signal Processor (DSP-TMS320C50) was used to carry out control applications. The C++ language was used for ANN and GA, and and the Assembly language was used for the DSP. It is determined that maximum overshoot and settling time are very small if the system is controlled by control parameters obtained from the optimization process that uses GA.