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Byeongchan KANG,Soyoung OH,Ji-Yeon KIM,Hyunsoo KANG,In Seop CHANG 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.4
Transcription in prokaryotes proceeds as the related gene units consist of a single operon. These operons induce bacteria to form favorable conditions for a given environment by regulating the level of transcription according to the substrate or specific environmental conditions. Eubacterium limosum KIST612 is a Gram-positive, obligately anaerobic, and acetogenic bacteria. This strain can use carbon monoxide as the sole energy source and carbon source. In E. limosum KIST612, it was confirmed that the transcription level of genes is regulated depending on the substrate or environmental conditions. This mechanism is called transcriptional regulation. This pattern of transcriptional regulation has also been confirmed in genes for consuming carbon monoxide, a representative substrate that E. limosum KIST612 can use. To investigate the regulation of the level of transcription of a gene related to carbon monoxide consumption, we analyze the operon unit of the related gene by co-transcription analysis and identify the transcription regulator.
On-Line Prediction of Nonstationary Variable-Bit-Rate Video Traffic
Sungjoo Kang,Seongjin Lee,Youjip Won,Byeongchan Seong IEEE 2010 IEEE transactions on signal processing Vol.58 No.3
<P>In this paper, we propose a model-based bandwidth prediction scheme for variable-bit-rate (VBR) video traffic with regular group of pictures (GOP) pattern. Multiplicative ARIMA (autoregressive integrated moving-average) process called GOP ARIMA (ARIMA for GOP) is used as a base stochastic model, which consists of two key ingredients: prediction and model validity check. For traffic prediction, we deploy a Kalman filter over GOP ARIMA model, and confidence interval analysis for validity determination. The GOP ARIMA mPodel explicitly models inter and intra-GOP frame size correlations and the Kalman filter-based prediction maintains ?state? across the prediction rounds. Synergy of the two successfully addresses a number of challenging issues, such as a unified framework for frame type dependent prediction, accurate prediction, and robustness against noise. With few exceptions, a single video session consists of several scenes whose bandwidth process may exhibit different stochastic nature, which hinders recursive adjustment of parameters in Kalman filter, because its stochastic model structure is fixed at its deployment. To effectively address this issue, the proposed prediction scheme harbors a statistical hypothesis test in the prediction framework. By formulating the confidence interval of a prediction in terms of Kalman filter components, it not only <I>predicts</I> the frame size but also <I>determines</I> validity of the stochastic model. Based upon the results of the model validity check, the proposed prediction scheme updates the structures of the underlying GOP ARIMA model. We perform a comprehensive performance study using publicly available MPEG-2 and MPEG-4 traces. We compare the prediction accuracy of four different prediction schemes. In all traces, the proposed model yields superior prediction accuracy than the other prediction schemes. We show that confidence interval analysis effectively detects the structural changes in the sample sequence and that properly updating the model results in more accurate prediction. However, model update requires a certain length of observation period, e.g., 60 frames (2 s). Due to this learning overhead, the advantage of model update becomes less significant when scene length is short. Through queueing simulation, we examine the effect of prediction accuracy over user perceivable QoS. The proposed bandwidth prediction scheme allocates less 50% of the queue(buffer) compared to the other bandwidth prediction schemes, but still yields better packet loss behavior.</P>
Non-stationary VBR 트래픽을 위한 동적 데이타 크기 예측 알고리즘
강성주(Sungjoo Kang),원유집(Youjip Won),성병찬(Byeongchan Seong) 한국정보과학회 2007 정보과학회논문지 : 정보통신 Vol.34 No.3
본 논문에서는 VBR(Variable-Bit-Rate) 트래픽의 비선형적이고 버스티한 특성을 모델화 한 GOP ARIMA(ARIMA for Group Of Pictures) 모델을 칼만 필터 알고리즘을 이용하여 실시간으로 예측하는 기법을 제안한다. 칼만 필터를 이용한 예측 기법은 GOP ARIMA의 상태공간 모델링 과정과 향후 N초 간의 트래픽을 예측하는 과정으로 구성된다. 실험을 위해 GOP의 크기가 각각 15인 세 가지 종류의 MPEG VBR 트래픽(뉴스, 드라마, 스포츠)을 제작하였고, 칼만 필터를 이용한 세 가지 종류의 트래픽의 예측 결과를 선형 예측법과 이중 지수 평활법을 이용해 예측한 결과와 비교해 예측 성능이 상대적으로 우수함을 확인할 수 있었다. 또한 예측값에 신뢰 구간을 설정하는 신뢰 구간 분석법을 통해 트래픽 관점에서 장면 변화를 예측하는 방법을 제시하였다. 본 논문의 칼만 필터 기반의 예측 알고리즘은 MPEG 기반 VBR 트래픽을 비롯한 기타 인터넷 트래픽을 실시간으로 예측하는 방법과 이를 이용해 인터넷 서버의 설계 및 자원 할당 정책 등을 위한 트래픽 엔지니어링 연구에 기여할 수 있을 것이다. In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.