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Abnormal kinase activity is a frequent cause of diseases, which makes kinases a promising pharmacological target. Thus, it is critical to identify the characteristics of protein kinases regulation by studying the activation and inhibition of kinase subunits in response to varied stimuli. Bayesian network (BN) is a formalism for probabilistic reasoning that has been widely used for learning dependency...
Summary form only given. Usually, solutions to sensor validation fall into two major categories: the data-based approaches and the model-based approaches. Model-based methods include nonparametric and parametric approaches. Belonging to the first category are neural-network-bank based approaches. The non-parametric methods are more robust, but a large number of training data are needed nevertheless...
We recently proposed the Edgewise Greedy Algorithm (EGA) for learning a decomposable Markov network of treewidth k approximating a given joint probability distribution of n discrete random variables. The main ingredient of our algorithm is the stepwise forward selection algorithm (FSA) due to Deshpande, Garofalakis, and Jordan. EGA is an efficient alternative to the algorithm (HGA) by Malvestuto,...
In this paper, we address the problem of adaptive sensor fault identification and validation by particle filtering. The model-based approaches are developed, where the sensor system is modeled by a Markov switch dynamic state-space model. To handle the nonlinearity of the problem, two different particle filters: mixture Kalman filter (MKF) and stochastic M-algorithm (SMA) are proposed. Simulation...
On August 30, 2005, we successfully demonstrated a large-scale, real-time, surveillance and control application on a wireless sensor network. The task was to track multiple human targets walking through a 5041 square meter sensor field and dispatch simulated pursuers to capture them. We employed a multi-target tracking algorithm that was a combination of a multi-sensor fusion algorithm for fusing...
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