Increasing demand for electric power in more electric aircraft requires a higher operating voltage for the power system that leads to higher electric stress on the insulation system. Operating at high altitudes where the electrical strength of air is weakened exposes the insulation system to even higher levels of electrical stress. As such, the probability of partial discharges (PD), which result in insulation degradation and failure, is higher. The presence of switching circuits in the power distribution system of the more electric aircraft is capable of producing high levels of noise. This noise in combination with the background noise and parasitic impedances will cause high amplitude ringing in the measured partial discharge signal waveform. Here, a method based on the combination of the wavelet and energy techniques is employed to detect PD pulses in a noisy environment under the low air pressure condition. To verify the technique, a laboratory setup consisting of two separate PD sources mounted in low air pressure (33 kPa) chamber is developed where sine and square waveforms are used as the applied voltage. The obtained results demonstrate that the proposed approach offers low computational complexity, high performance in PD phase localization, and robustness in a noisy environment.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The purpose of the present study is to examine the effects of customer relationship management on the marketing performance. The statistical population of this study includes branches of Isfahan Saderat bank. In order to collect the data, a self-administrated questionnaire has been used. Validity of this questionnaire has been examined through Kendal coefficient. This value is 0.75 for this questionnaire that confirms its validity. Also Crocbachs' Alpha Coefficient has been use to examine and confirm reliability of the questionnaire. This value is 0.82 for our questionnaire that confirm its reliability. In order to test the hypotheses and analyze the data, structural equation modeling has been used in Amos20. The results reveal that the customer relationship management influences marketing performance (path coefficient: 0.79, t-value: 3.83). Also the results show that concentration on the key accounts, technology-based customer relationship management, knowledge management, customer relationship management organization have significant positive effects on the marketing performance.
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