Recently, various types of mobile micro-robots have been proposed for medical and industrial applications. Especially in medical applications, a motor system for propulsion cannot easily be used in a micro-robot due to their small size. Therefore, micro-robots are usually actuated by controlling the magnitude and direction of an external magnetic field. However, for micro-robots, these methods in general are only applicable for moving and drilling operations, but not for the undertaking of various missions. In this paper, we propose a new micro-robot concept, which uses wireless power transfer to deliver the propulsion force and electric power simultaneously. The mechanism of Lorentz force generation and the coil design methodologies are explained, and validation of the proposed propulsion system for a micro-robot is discussed thorough a simulation and with actual measurements with up-scaled test vehicles.
This paper presents the development of Short Term Load Forecasting (STLF) model using Artificial Neural Network (ANN). STLF is required for electric power planning and electricity market planning. The proposed model predicts the load demand of Connecticut in the U.S. using hourly historical electric load and weather data. For improving the load prediction accuracy, we consider two main issues that are seasons and weather factors. Each season has different load demand patterns, thus the weather factors are differently applied in each season. The proposed model uses the composited weather factor which consists of temperature and dew point. The temperature and dew point weather factors are selected through the correlation coefficient to obtain the meaningful data among the weather factors. The selected weather factors adjust the level of the pitch which is the predicted load demand of one day ahead. The proposed model improves the forecasting accuracy both in summer and winter.
The present work quantifies numerically the systematic errors present in experimental infrared heat flux studies of boiling surfaces. A transient conduction model for multilayer structures is proposed to describe the periodic heat fluxes encountered on boiling surfaces. The results of the current work show that the systematic error behavior of the infrared method is not uniform but dependent on the frequency of the heat flux signal of the boiling surface; which is a novel finding. As the frequency of the heat flux signal increases, the errors in the measured phase of heat flux signals are expected to increase. The errors in the amplitude of heat flux signals sharply increase at low frequencies (1-10 Hz) and decrease as the frequency increases.The maximum errors in the phase and amplitude of heat flux signals are 9% and 23%, respectively in the frequency range of nucleate boiling (10-80 Hz). Based on the current analysis, it is concluded that the systematic errors found arise from assuming that thermal contact resistances of such systems are negligible. This is an assumption universally adopted
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