http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
하주형,왕주안,이원재,최영준,Hae-Yong Lee,Jung-Gon Kim,Hiroshi Harima 한국물리학회 2015 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.66 No.6
The hydride vapor-phase epitaxy (HVPE) method was used to deposit high-quality InN layerson GaN inter-layer/sapphire (0001) structures that had been fabricated by using either the HVPEmethod or the metal-organic chemical-phase deposition (MOCVD) method. The effects of the groupV/III ratio and different GaN inter-layers on the crystal quality of the InN layers were systemicallyinvestigated. The InN layer grown at a low Group V/III ratio revealed a high crystal qualitywith a two-dimensional (2D) growth mode. Also, the 110.7-nm-thick InN layer grown by usingHVPE on a GaN inter-layer/sapphire (0001) substrate structure that had been fabricated by usingMOCVD had a high crystal quality, with the full width at half maximum (FWHM) of the InNX-ray diffraction (XRD) peak being about 844 arcsec, and a smooth surface with an atomic forcemicroscopy (AFM) roughness of about 0.07 nm. On the other hand, the 145.7-nm-thick InN layergrown by using HVPE on a GaN inter-layer/sapphire (0001) substrate structure that had beenfabricated by using the HVPE method had a lower crystal quality, a FWHM value for the InN(0002) peak of about 2772 arcsec, and a surface roughness of about 3.73 nm. In addition, the peakof the E2 (high) phonon mode for the 110.7-nm-thick InN layer grown by using HVPE on a GaNinter-layer/sapphire (0001) structure that had been fabricated by using MOCVD was detected at491 cm−1 and had a FWHM of 9.9 cm−1. As a result, InN layers grown by using HVPE on GaNinter-layer/sapphire (0001) substrate structures fabricated by using MOCVD have a high crystalquality and a reduced Raman value, which agrees well with the results of the XRD analysis.
Imanishi, Toshiaki,Hanai, Taizo,Aoyagi, Ichiro,Uemura, Jun,Araki, Katsuhiro,Yoshimoto, Hiroshi,Harima, Takeshi,Honda , Hiroyuki,Kobayashi, Takeshi The Korean Society for Biotechnology and Bioengine 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.
Takeshi Kobayashi,Toshiaki Imanishi,Taizo Hanai,Ichiro Aoyagi,Jun Uemura,Katsuhiro Araki,Hiroshi Yoshimoto,Takeshi Harima,Hiroyuki Honda 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.