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Can Impossible Possibility Save Modalism? (불가능한 가능성은 양상주의를 구원할 수 있는가?)

  • Lee, Jaeho;Kim, Minseok
    • Korean Journal of Logic
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    • v.21 no.2
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    • pp.175-207
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    • 2018
  • Since Kit Fine's influential paper, Essence and Modality (1994), many philosophers have doubted the prospects of modalism, according to which we can analyze the concept of essence with that of de re modality. However, some philosophers have tried to save modalism against Fine's counterexamples seriously. In this paper, we examine two such attempts which appeal to some kind of 'impossible possibilities.' We argue that such attempts have strong tendency to end in either a metaphysical picture which is very similar to Fine's or a concept of essence which is quite different from Fine's. For this reason, we claim that Fine has no reason to worry about such attempts.

A study on Power Quality Recognition System using Wavelet Transformation and Neural Networks (웨이블릿 변환과 신경회로망을 이용한 전력 품질 인식 시스템에 관한 연구)

  • Chong, Won-Yong;Gwon, Jin-Soo
    • Journal of the Institute of Convergence Signal Processing
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    • v.11 no.2
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    • pp.169-176
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    • 2010
  • Nonstationary power quality(PQ) signals which the Sag, Swell, Impulsive Transients, and Harmonics make sometimes the operations of the industrial power electronics equipment, speed and motion controller, plant process control systems in the undesired environments. So, this PQ problem might be critical issues between power suppliers and consumers. Therefore, We have studied the PQ recognition system in order to acquire, analyze, and recognize the PQ signals using the software, i.e, MATLAB, Simulink, and CCS, and the hardware. i.e., TMS320C6713DSK(TI), The algorithms of the PQ recognition system in the Wavelet transforms and Backpropagation algorithms of the neural networks. Also, in order to verify the real-time performances of the PQ recognition system under the environments of software and hardware systems, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances of average 99%.