Power developed by the wind turbines, at different wind velocities, is a key information required for the successful design and efficient management of wind energy projects. Conventionally, for these applications, manufacturer's power curves are used in estimating the velocity-power characteristics of the turbines. However, performance of the turbines under actual field environments may significantly differ from the manufacturer's power curves, which are derived under 'standard' conditions. In case of existing wind projects with sufficient performance data, the velocity-power variations can better be defined using artificially intelligent models. In this paper, we compare the performance of four such models by applying them to a 2-MW onshore wind turbine. Models based on ANN, KNN, SVM and MARS were developed and tested using the SCADA data collected from the turbine. All the AI models performed significantly better than the manufacturer's power curve. Among the AI methods, SVM-based predictions showed the highest accuracy. A site-specific performance curve for the turbine, based on the SVM model, is presented. Wider adaptability of this approach has been demonstrated by successfully implementing the model for a 3.6-MW wind turbine, working under offshore environment. Being "site-specific data" driven, the proposed models are more accurate and hence better choice for applications like short-term wind power forecasting and pro-diagnostics of wind turbines.
The frontal pole (FP), which largely overlaps with Brodmann's area (BA) 10, is the rostral-most part of the hominid cerebral cortex, and plays a critical role in complex aspects of human cognition. The existing conventions suggested for MRI-based parcellation of this important frontal subdivision have limited cytoarchitectonic meaning with regard to the demarcation of the FP from adjacent prefrontal subdivisions. In this paper, we propose the coronal section containing the anterior termination of the olfactory sulcus (ATOS) as an easy-to-identify landmark for FP parcellation that largely overlaps with the cytoarchitectonic distinction between BA 10 and the more posterior cytoarchitectonic subdivisions of the PFC. Manual segmentation-based parcellation of the FP using the proposed landmark in 20 healthy volunteers yielded highly reliable (standardized item alpha = 0.92) volumetric estimates [right FP volume = 8.421 cm3 (SE = 0.773, range 3.107-15.741); left FP volume = 8.039 cm3 (SE = 0.708, range 2.234-12.956)]. The volumetric measurements of right FP generated in the present study were comparable to those reported in a prior study of BA 10 using histological sections and stereological techniques (Semendeferi et al. In: Am J Phys Anthropol 114:224-241, 2001). Therefore, in the absence of a naturally occurring sulcal boundary, the proposed method for parcellation of the FP can provide unbiased volume estimations for studies of healthy and disordered populations of subjects.
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