Parallel evolution strategy on grids for the protein threading problem

The protein threading problem is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are unrealistic for long proteins and/or large template data sets. In this paper, we propose an evolution strategy for the solution of the protein threading problem. We also propose three parallel methods for fast threading. Our experiments produced encouraging preliminary results in term of threading energy as well as significant reduction in threading time.     

Learning with Permutably Homogeneous Multiple-Valued Multiple-Threshold Perceptrons

The (n,k,s)-perceptrons partition the input space V R ⁿ into s+1 regions using s parallel hyperplanes. Their learning abilities are examined in this research paper. The previously studied homogeneous (n,k,k–1)-perceptron learning algorithm is generalized to the permutably homogeneous (n,k,s)-perceptron learning algorithm with guaranteed convergence property. We also introduce a high capacity learning method that learns any permutably homogeneously separable k-valued function given as input.     

Dynamic Optimization of Switching States of an Hybrid Power Network

International Journal of Control, Automation and Systems - This paper presents the quality improvement of electric power and the optimization of the switching transient states. We have used a...     

The Computing Capacity of Three-Input Multiple-Valued One-Threshold Perceptrons

In this paper, an exact and general formula is derived for the number of linear partitions of a given point set V in three-dimensional space, depending on the configuration formed by the points of V. The set V can be a multi-set, that is it may contain points that coincide. Based on the formula, we obtain an efficient algorithm for counting the number of k-valued logic functions simulated by a three-input k-valued one-threshold perceptron.     

Open-loop control of a MEMS deformable mirror for large-amplitude wavefront control

A method is introduced for predicting control voltages that will generate a prescribed surface shape on a MEMS deformable mirror. The algorithm is based upon an analytical elastic model of the mirror membrane and an empirical electromechanical model of its actuators. It is computationally simple and inherently fast. Shapes at the limit of achievable mirror spatial frequencies with up to 1.5 microm amplitudes have been achieved with less than 15 nm rms error.