Knowledge Of Transition Probabilities Research Articles

H 2 Control of Markovian Jump Systems with Input Saturation and Incomplete Knowledge of Transition Probabilities

This paper proposes an ℋ 2 state-feedback controller for Markovian jump systems with input saturation and incomplete knowledge of transition probabilities. The proposed controller is developed using second-order matrix polynomials of an incomplete transition rate to derive less conservative stabilization conditions. The proposed controller not only guarantees ℋ 2 performance but also rejects matched disturbances. The effectiveness of the proposed method is demonstrated using three numerical examples.

Less conservative stabilization conditions for Markovian jump systems with incomplete knowledge of transition probabilities and input saturation

Summary This paper proposes less conservative stabilization conditions for Markovian jump systems with incomplete knowledge of transition probabilities and input saturation. The transition rates associated with the transition probabilities are expressed in terms of three properties, which do not require the lower and upper bounds of the transition rates, differently from other approaches in the literature. The resulting conditions are converted into the second-order matrix polynomial of the unknown transition rates. The polynomial can be represented as quadratic form of vectorized identity matrices scaled by one and the unknown transition rates. And then, the LMI conditions are obtained from the quadratic form. Also, an optimization problem is formulated to find the largest estimate of the domain of attraction in mean square sense of the closed-loop systems. Finally, two numerical examples are provided to illustrate the effectiveness of the derived stabilization conditions. Copyright © 2016 John Wiley & Sons, Ltd.

DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments

In this paper, we have proposed and designed DPHK (data prediction based on HMM according to activity pattern knowledge mined from trajectories), a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowledge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM (hidden Markov model) algorithm to predict the sensor node locations people will arrive at according to the receivedmessage. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows: (a) the procedure of DPHK is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.

Atomic lifetime measurements employing an electron beam ion trap

Transition probabilities relate to atomic structure and dynamics in ways that are different from straightforward spectra. Besides being a tool for fundamental physics studies, the knowledge of transition probabilities is essential for applications in plasma physics and astrophysics. Techniques and procedures used for measuring the lifetimes of levels in highly charged ions by employing an electron beam ion trap are reviewed to illustrate the state of the art. Examples are drawn from experiments that involve observations in the visible, extreme-ultraviolet, and X-ray ranges, and cover atomic lifetimes in the femtosecond to second range.PACS Nos.: 32.70.Cs, 32.30.Jc, 32.30.Rj

Transition probability parameters of molecular spectra arising from combustion processes

The dependence of intensity distributions in molecular spectra upon ( a) source conditions and (b) molecular constants (particularly transition probabilities) is discussed briefly. Knowledge of transition probabilities is essential to the interpretation of spectral intensities in terms of source conditions. Arrays of Franck-Condon factors q v′v″, which are closely related to relative vibrational transition probabilities. p v′v″, have been computed upon a Morse model and are presented for a comprehensive range of band systems of significance in combustion processes for the molecules C 2, CN, B 2, BO, BH, BN, OH, O 2, NO, PbO. Where available, values of smoothed relative vibrational transition probabilities p v′v″ and band intensities at infinite temperature I ∞r′r″ are also given.