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항균제 및 항진균제 첨가를 통한 효모 및 유산균의 선택배지 제조
박호민(Homin Park),차정민(Jeongmin Cha),박성은(Seong-Eun Park),손홍석(Hong-Seok Son) 한국식품과학회 2022 한국식품과학회지 Vol.54 No.5
본 연구에서는 항균제인 chloramphenicol과 항진균제인 cycloheximide, amphotericin B, methyl paraben을 각각 YM 배지와 MRS 배지에 첨가하였을 때 배지의 선택성을 확인하였다. Chloramphenicol은 0.01 g/L 이상의 농도에서 유산균의 생육을 억제하였고, cycloheximide는 0.5 g/L 이상의 농도에서 효모의 생육을 억제하는 효과를 나타내었다. 반면 amphotericin B와 methyl paraben은 각각 0.125-0.5, 0.28 g/L의 농도에서는 효모의 생육을 거의 억제하지 못했다. MRS 배지에 지시약인 bromocresol green을 0.05 g/L 첨가했을 때 L. fermentum과 S. cerevisiae 콜로니를 육안상으로 구별하는데 도움이 되었다. 결론적으로 MRS 배지에서 유산균의 생균수 측정을 위해서는 항진균제로 0.5 g/L 이상의 농도의 cycloheximide를, YM 배지에서 효모의 생균수 측정을 위해서는 항균제로 0.01 g/L 이상의 농도의 chloramphenicol을 첨가하는 것이 바람직하다. 실제 막걸리 시료에 chloramphenicol과 cycloheximide를 적용한 결과 효과적으로 배지의 선택성을 증가시킬 수 있음을 확인하였다. 본 연구는 김치와 막걸리와 같이 유산균과 효모가 같이 존재하는 식품을 MRS, YM 배지를 이용해 유산균이나 효모의 생균수만을 확인하고자 할 때 선택배지 제조에 있어 활용이 가능할 것으로 판단된다. 하지만 다양한 미생물이 상이하게 존재하는 여러 발효식품에서도 유효한지에 관해서는 추가적이 연구가 필요하다. In this study, we examined whether the of yeast mold agar and de Man, Rogosa and Sharpe (MRS) agar was improved when antibacterial and antifungal agents were added. The addition of 0.01 g/L of chloramphenicol, a widely used antibacterial agent, was sufficient to inhibit the growth of lactic acid bacteria (LAB), and the addition 0.5 g/L of the antifungal agent, cycloheximide, inhibited yeast growth. The antifungal agents, amphotericin B and methyl paraben, poorly inhibited yeast growth at concentrations of 0.5 and 0.28 g/L, respectively. Moreover, it was shown that the addition of chloramphenicol or cycloheximide selectively inhibited the growth of LAB or yeast in the makgeolli sample, whereby both yeast and LAB were present. These results suggest that these selective media can be used to count in various types of samples in which LAB and yeast coexist.
Sung Ik Park,Homin Eum,So Ra Park,Geon Kim,Yong-Tae Lee,Heung Mook Kim,오왕록 한국전자통신연구원 2009 ETRI Journal Vol.31 No.4
In this paper, we propose a novel equalization onchannel repeater (OCR) with a feedback interference canceller (FIC) to relay terrestrial digital multimedia broadcasting signals in single frequency networks. The proposed OCR not only has high output power by cancelling the feedback signals caused by insufficient antenna isolation through the FIC, but also shows better output signal quality than the conventional OCR by removing multipath signals existing between the main transmitter and the OCR through an equalizer. In addition, computer simulations and laboratory test results demonstrate that the proposed OCR successfully cancels feedback signals and compensates channel distortions and provides a higher quality transmitting signal with higher output power than conventional OCRs.
T-DMB 시스템에서 궤환간섭 제거기를 가지는 등화형 OCR
박성익(Sung Ik Park),음호민(Homin Eum),박소라(So Ra Park),김건(Geon Kim),이용태(Yong-Tae Lee),김흥묵(Heung Mook Kim) 한국방송·미디어공학회 2008 한국방송공학회 학술발표대회 논문집 Vol.2008 No.-
본 논문에서는 T-DMB 시스템의 단일 주파수 망 구성을 위해 궤환신호 제거기를 가지는 등화형 OCR (Equalization On-Channel Repeater, E-OCR)을 제안한다. 제안된 OCR은 송/수신 안테나의 충분치 못한 분리도로 인해 야기된 궤환신호를 궤환간섭 제거기를 통해 제거하여 송신출력을 높일 뿐만 아니라. 송신기와 중계기 사이의 다중경로 신호를 등화기를 통해 제거하여 우수한 출력신호 품질을 보장한다. 또한, 본 논문에서는 전산실험을 통해 제안된 OCR의 성능을 살펴보고 실험실 테스트를 통해 실제 구현된 OCR의 성능을 검증한다.
Traffic Information Service Model Considering Personal Driving Trajectories
( Homin Han ),( Soyoung Park ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.4
In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road nodebased indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver`s driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.
Traffic Information Service Model Considering Personal Driving Trajectories
Han, Homin,Park, Soyoung Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4
In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.