Eigensensitivity analysis for symmetric nonviscously damped systems with repeated eigenvalues

An efficient algorithm is derived for computation of eigenvalue and eigenvector derivatives of symmetric nonviscously damped systems with repeated eigenvalues. In the proposed method, the mode shape derivatives of the nonviscously damped systems are divided into a particular solution and a homogeneous solution. A simplified method is given to calculate the particular solution by solving a linear equation with non-singularity coefficients, the method is numerically stable and efficient compared to previous methods since the coefficient matrix is non-singularity and numerically stable. The homogeneous solution are computed by the second order derivative of eigenequation. One numerical example is used to illustrate the validity of the proposed method

An Improved Method for Computing Eigenpair Derivatives of Damped System

The calculation of eigenpair derivatives plays an important role in vibroengineering. This paper presents an improved algorithm for the eigenvector derivative of the damped systems by dividing it into a particular solution and general solution of the corresponding homogeneous equation. Compared with the existing methods, the proposed algorithm can significantly reduce the condition number of the equation for particular solution. Therefore, the relative errors of the calculated solutions are notably cut down. The results on two numerical examples show that such strategy is effective in reducing the condition numbers for both distinct and repeated eigenvalues

Eigensensitivity of damped system with defective multiple eigenvalues

This paper considers the sensitivity of defective multiple eigenvalues of reducible matrix pencil, the average of eigenvalues is proved to be analytic, the derivatives of the average eigenvalues and the corresponding eigenvector matrices are obtained when the generalized eigenvalue is reducible. The sensitivity of defective multiple eigenvalues of a quadratic eigenvalue problem dependent on several parameters are also obtained by the result of generalized eigenvalue problem. The results are useful for investigating structural optimal design, model updating and structural damage detection

Computing eigenpair derivatives of asymmetric damped system by generalized inverse

Many existing approaches for asymmetric damped system are based on the assumption that the eigenvalues are simple or semisimple with separated derivatives. This paper presents a new algorithm for computing the derivatives of the semisimple eigenvalues and corresponding eigenvectors of asymmetric damped system. Compared with the existing methods, the algorithm can be applicable to problems whether the repeated eigenvalues have well separated derivatives. In the proposed method, the derivatives of eigenvectors are divided into a particular solution and a homogeneous solution, where the particular solution is constructed by using generalized inverse matrix. The effectiveness of the proposed algorithm is illustrated by one numerical example

Contourlet textual features: Improving the diagnosis of solitary pulmonary nodules in two dimensional ct images

Materials and Methods: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.Results: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.Objective: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Copyright:Conclusion: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer

A comprehensive and high-resolution genome-wide response of p53 to stress

Tumor suppressor p53 regulates transcription of stress-response genes. Many p53 targets remain undiscovered because of uncertainty as to where p53 binds in the genome and the fact that few genes reside near p53-bound recognition elements (REs). Using chromatin immunoprecipitation followed by exonuclease treatment (ChIP-exo), we associated p53 with 2,183 unsplit REs. REs were positionally constrained with other REs and other regulatory elements, which may reflect structurally organized p53 interactions. Surprisingly, stress resulted in increased occupancy of transcription factor IIB (TFIIB) and RNA polymerase (Pol) II near REs, which was reduced when p53 was present. A subset associated with antisense RNA near stress-response genes. The combination of high-confidence locations for p53/REs, TFIIB/Pol II, and their changes in response to stress allowed us to identify 151 high-confidence p53-regulated genes, substantially increasing the number of p53 targets. These genes composed a large portion of a predefined DNA-damage stress-response network. Thus, p53 plays a comprehensive role in regulating the stress-response network, including regulating noncoding transcription

An Improved Three-Way Clustering Based on Ensemble Strategy

As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data. In order to solve this problem, three-way clustering was presented to show the uncertainty information in the dataset by adding the concept of fringe region. In this paper, we present an improved three-way clustering algorithm based on an ensemble strategy. Different to the existing clustering ensemble methods by using various clustering algorithms to produce the base clustering results, the proposed algorithm randomly extracts a feature subset of samples and uses the traditional clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, labels matching is used to align all clustering results in a given order and voting method is used to obtain the core region and the fringe region of the three way clustering. The proposed algorithm can be applied on the top of any existing hard clustering algorithm to generate the base clustering results. As examples for demonstration, we apply the proposed algorithm on the top of K-means and spectral clustering, respectively. The experimental results show that the proposed algorithm is effective in revealing cluster structures

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures

A Three-Way Clustering Method Based on Ensemble Strategy and Three-Way Decision

Three-way decision is a class of effective ways and heuristics commonly used in human problem solving and information processing. As an application of three-way decision in clustering, three-way clustering uses core region and fringe region to represent a cluster. The identified elements are assigned into the core region and the uncertain elements are assigned into the fringe region in order to reduce decision risk. In this paper, we propose a three-way clustering algorithm based on the ideas of cluster ensemble and three-way decision. In the proposed method, we use hard clustering methods to produce different clustering results and labels matching to align all clustering results to a given order. The intersection of the clusters with the same labels are regarded as the core region. The difference between the union and the intersection of the clusters with the same labels are regarded as the fringe region of the specific cluster. Therefore, a three-way clustering is naturally formed. The results on UCI data sets show that such a strategy is effective in improving the structure of clustering results