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Azotobacter sp.에 의한 Butyric Acid와 Valeric Acid로부터 Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)의 생산
송희주,이일석,방원기 한국미생물생명공학회 ( 구 한국산업미생물학회 ) 1996 한국미생물·생명공학회지 Vol.24 No.1
Butyric acid 와 valeric acid로부터 poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (P(3HB-co-3HV))를 생산하기 위하여 P(3HB-co-3HV)의 생산능력이 있는 10종의 균주를 토양으로부터 분리하였으며 분리된 균주들중에서 P(3HB-co-3HV)의 생산능력이 가장 우수한 균주 HJ-067을 선별하였고, Azotobacter sp.로 부분 동정하였다. P(3HB-co-3HV)의 생산에 있어서 butyric acid 와 valeric acid의 최적 기질 농도는 각각 3.0g/l이었다. 질소원으로는 (NH_4)_2SO_4가 가장 우수하였으며, 최적 농도는 0.75 g/l(C/N ratio=21.36)이었다. 금속이온(Zn^2+, Co^2+, Mn^2+)의 결핍은 P(3HB-co-3HV)의 생산에 영향을 미치며, 특히 Mn^2+을 첨가하지 않은 경우에 P(3HB-co-3HV)의 생산량이 증가하였다. P(3HB-co-3HV)의 생산을 위한 최적 배양 온도는 27℃였으며, 최적 초기 pH는 7.0이었다. 상기의 최적 조건하에서 36시간 배양하여 얻어진 건조균체량 및 P(3HB-co-3HV)의 생산량은 각각 3.00 g/l, 1.82 g/l이었다. 이때의 P(3HB-co-3HV) yield는 건조 균체량의 60.60%(w/w)이었으며, HV%는 15.92%(w/w)이었다. For the production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (P(3HB-co-3HV)) from butyric acid and valeric acid, 10 strains of bacteria capable of producing P(3HB-co-3HV) wee isolated from soil. Among them, the strain HJ-067 showed the best ability of producing P(3HB-co-3HV), and was identified as a Azotobacter sp. For the production of P(3HB-co-3HV), the optimum concentrations of butyric acid and valeric acid were 3.0 g/l, respectively. The most effective nitrogen source was (NH_4)_2SO_4 at an optimum concentration of 0.75 g/l, which was equivalent to 21.36 in C/N ratio. Deficiency of the cationic metal ions (Zn^2+, Co^2+, Mn^2+) in the production medium had stimulating effect on P(2HB-co-3HV) accumulation, especially in the managanese deficient medium. The optimum temperature for P(3HB-co-3HV) production was 27℃ and the optimum initial pH was 7.0. Under the optimum conditions, 1.82 g/l of P(3HB-co-3HV) and 3.00 g/l of dry biomass were produced after 36 hour cultivation, and the P(3HB-co-3HV) yield and HV% were 60.60% (w/w), 15.92% (w/w), respectively.
골전도 헤드폰 형태로 추출된 골전도 음성 신호의 딥러닝 활용
송희주,유선아,손세강,장웅기,황향희,김현욱,김병희,이형석 한국생산제조학회 2024 한국생산제조학회지 Vol.33 No.1
In this study, we used deep learning to align bone-conducted speech signals with air-conducted speech signals, aiming to replace traditional air conduction microphones in voice-based services capturing surrounding sounds. We fabricated headphones, placing bone conduction microphones on the rami (the branches of a bone in the jaw area), in line with traditional bone conduction headphone configurations. Using LSTM, CNN, and CRNN models, we created databases that aligned bone-conducted speech signals with their air-conducted counterparts and tested them with bone-conducted speech signals captured via our custom-made headphones. The CNN model demonstrated superior performance in accurately distinguishing three English words (“apple,” “hello,” and “pass”), including their voiceless pronunciations. In conclusion, our study shows that deep learning models can effectively use bone-conducted speech signals extracted from the rami for automatic speech recognition (ASR), paving the way for future ASR technology that precisely recognizes only the speaker’s voice.