scholarly journals Determination of Single Molecule erbB1 Homodimer Lifetimes Using Single Quantum Dot Tracking and a Diffusive Hidden Markov Model

2010 ◽  
Vol 98 (3) ◽  
pp. 498a
Author(s):  
Shalini T. Low-Nam ◽  
Keith A. Lidke ◽  
Patrick J. Cutler ◽  
Rob C. Roovers ◽  
Paul M.P. van Bergen en Henegouwen ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Single Molecule ◽  
Hidden Markov ◽  
Single Quantum ◽  
Single Quantum Dot

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

Photon-by-Photon Hidden Markov Model Analysis for Microsecond Single-Molecule FRET Kinetics

2016 ◽  
Vol 120 (51) ◽  
pp. 13065-13075 ◽  
Author(s):  
Menahem Pirchi ◽  
Roman Tsukanov ◽  
Rashid Khamis ◽  
Toma E. Tomov ◽  
Yaron Berger ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Single Molecule ◽  
Hidden Markov ◽  
Model Analysis ◽  
Single Molecule Fret

Analysis of the Steps in Single-Molecule Photobleaching Traces by Using the Hidden Markov Model and Maximum-Likelihood Clustering

2014 ◽  
Vol 9 (8) ◽  
pp. 2303-2308 ◽  
Author(s):  
Jinghe Yuan ◽  
Kangmin He ◽  
Ming Cheng ◽  
Jianqiang Yu ◽  
Xiaohong Fang
Keyword(s):  
Maximum Likelihood ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Single Molecule ◽  
Hidden Markov

scholarly journals Determination of the complex microwave photoconductance of a single quantum dot

2001 ◽  
Vol 63 (3) ◽  
Author(s):  
H. Qin ◽  
F. Simmel ◽  
R. H. Blick ◽  
J. P. Kotthaus ◽  
W. Wegscheider ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Single Quantum ◽  
Single Quantum Dot

Single-molecule measurements with a single quantum dot

2007 ◽  
Vol 7 (5) ◽  
pp. 295-304 ◽  
Author(s):  
Noritada Kaji ◽  
Manabu Tokeshi ◽  
Yoshinobu Baba
Keyword(s):  
Quantum Dot ◽  
Single Molecule ◽  
Single Quantum ◽  
Single Quantum Dot

Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines

2011 ◽  
Author(s):  
Andrea Giantomassi ◽  
Francesco Ferracuti ◽  
Alessandro Benini ◽  
Gianluca Ippoliti ◽  
Sauro Longhi ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Models ◽  
Residual Life ◽  
Hidden Markov ◽  
Life Time ◽  
Turbofan Engines ◽  
Useful Life ◽  
Determinant Factor

Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.

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