• Journal of Power Electronics
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• v.12 no.5
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• pp.813-820
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• 2012

This paper presents the development of a software supported acquisition system for metrological verification and testing of the equipment for monitoring and analysis of the basic electrical power quality parameters. The described procedure consists of two functionally connected segments. The first segment involves generation of the reference three-phase voltage signals, including the possibility of simulation of the various power quality disturbances, typical for electrical power distribution networks. The second part of this procedure includes the real-time recording of power quality disturbances in three-phase distribution networks. The procedure is functionally supported by the virtual instrumentation concept, including a software application developed in LabVIEW environment and data acquisition boards NI 6713 and NI 9215A. The software support of this system performs graphical presentation of the previously generated and recorded signal waveforms. A number of the control functions and buttons, implemented on the virtual instrument front panels, are provided to adjust the basic signal acquisition, generation and recording parameters.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.393-402
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• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.