Design Aspects of Megawatt-Range Direct-Driven Permanent MagnetWind Generators

Direct-driven permanent magnet synchronous generator is one of the most promising topologies for megawatt-range wind power applications. The rotational speed of the direct-driven generator is very low compared with the traditional electrical machines. The low rotational speed requires high torque to produce megawatt-range power. The special features of the direct-driven generators caused by the low speed and high torque are discussed in this doctoral thesis. Low speed and high torque set high demands on the torque quality. The cogging torque and the load torque ripple must be as low as possible to prevent mechanical failures. In this doctoral thesis, various methods to improve the torque quality are compared with each other. The rotor surface shaping, magnet skew, magnet shaping, and the asymmetrical placement of magnets and stator slots are studied not only by means of torque quality, but also the effects on the electromagnetic performance and manufacturability of the machine are discussed. The heat transfer of the direct-driven generator must be designed to handle the copper losses of the stator winding carrying high current density and to keep the temperature of the magnets low enough. The cooling system of the direct-driven generator applying the doubly radial air cooling with numerous radial cooling ducts was modeled with a lumped-parameter-based thermal network. The performance of the cooling system was discussed during the steady and transient states. The effect of the number and width of radial cooling ducts was explored. The large number of radial cooling ducts drastically increases the impact of the stack end area effects, because the stator stack consists of numerous substacks. The effects of the radial cooling ducts on the effective axial length of the machine were studied by analyzing the crosssection of the machine in the axial direction. The method to compensate the magnet end area leakage was considered. The effect of the cooling ducts and the stack end area effects on the no-load voltages and inductances of the machine were explored by using numerical analysis tools based on the three-dimensional finite element method. The electrical efficiency of the permanent magnet machine with different control methods was estimated analytically over the whole speed and torque range. The electrical efficiencies achieved with the most common control methods were compared with each other. The stator voltage increase caused by the armature reaction was analyzed. The effect of inductance saturation as a function of load current was implemented to the analytical efficiency calculation.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

Use of preclinical and early clinical data for dose selection of a selective estrogen receptor modulator toremifene in treatment of breast cancer

In development of human medicines, it is important to predict early and accurately enough the disease and patient population to be treated as well as the effective and safe dose range of the studied medicine. This is pursued by using preclinical research models, clinical pharmacology and early clinical studies with small sample sizes. When successful, this enables effective development of medicines and reduces unnecessary exposure of healthy subjects and patients to ineffectice or harmfull doses of experimental compounds. Toremifene is a selective estrogen receptor modulator (SERM) used for treatment of breast cancer. Its development was initiated in 1980s when selection of treatment indications and doses were based on research in cell and animal models and on noncomparative clinical studies including small number of patients. Since the early development phase, the treatment indication, the patient population and the dose range were confirmed in large comparative clinical studies in patients. Based on the currently available large and long term clinical study data the aim of this study was to investigate how the early phase studies were able to predict the treatment indication, patient population and the dose range of the SERM. As a conclusion and based on the estrogen receptor mediated mechanism of action early studies were able to predict the treatment indication, target patient population and a dose range to be studied in confirmatory clinical studies. However, comparative clinical studies are needed to optimize dose selection of the SERM in treatment of breast cancer.