進階篩選

Technical category
    • Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • 考慮積灰效應及少故障標籤資料之智慧型高精度太陽光電故障診斷

      FutureTech 考慮積灰效應及少故障標籤資料之智慧型高精度太陽光電故障診斷

      As for the proposed technology by combining the artificial bee colony algorithmthe semi-supervised extreme learning machine (ABC-SSELM), characteristic parameter normalization equations of I-V curves are tuned via low-cost data under normal operation of PV strings. The proposed ABC-SSELM method only needs 1-3 labeled data of the total dataset to save humantime cost. The accuracy of diagnosing various mixed faults can reach more than 99.84,the monitoring of dust accumulation can provide effective cleaning to increase the revenue of the solar PV power generation system.