Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-ta...

We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account. In comparison to the weighted euclidean metric used for GRLVQ, this metric is more powerful to...

We demonstrate that the microstructural and mechanical properties of bacterial biofilms can be created through colloidal self-assembly of cells and polymers, and thereby link the complex material properties of biofilms to well understood colloidal and polymeric behaviors. This finding is applied to soften and disassemble staphylococcal biofilms through pH changes. Bacterial biofilms are viscoel...

In this paper, the CMOS gate matrix layout problem is formulated and solved as a n artificial intelligence planning problem in which a “plan” (the solution algorithm) is to be generated to achieve a “goal” (the gate matrix layout). The overall goal consists of many subgoals, each of which corresponds to the placement of a gate to a slot, and to the routing of associated nets connecting to that ...

The objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then,...

We consider the problem of estimating the inverse covariance matrix by maximizing the likelihood function with a penalty added to encourage the sparsity of the resulting matrix. We propose a new approach based on the split Bregman method to solve the regularized maximum likelihood estimation problem. We show that our method is significantly faster than the widely used graphical lasso method, wh...

This work is a continuation of work on algorithms for simultaneous reconstruction and segmentation. In our previous work we developed an algorithm for data with Gaussian noise, and in that algorithm the coefficient matrix for the system is explicitly store. We improve this algorithm in two ways: our new algorithm can handle Poisson noise in the data, and it can solve much larger problems since ...

In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationmaximization (EM) algorithm. In this paper, we extend the FMLLR structure from diagonal to unrestricted full matrix with a sophisticated algorithm for the training of re...

through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (aeas) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...

The effect of the milling time & ageing on the hardness, density, and wear characteristics of Al 7150 alloy specimens made via powder metallurgy has been studied. The different constituents of Al 7150 alloy were processed in a planetary ball milling set up with a BPR of 10:1 for 5 hours, 10 hours, and 20 hours. At 400 °C, the milled powders were subsequently hot compacted in a punch die setup. ...