Weighted Direct Position Determination via the Dimension Reduction Method for Noncircular Signals

This work studies the direct position determination (DPD) of noncircular (NC) signals with multiple arrays. Existing DPD algorithms of NC sources ignore the impact of path propagation loss on the performance of the algorithms. In practice, the signal-to-noise ratios (SNRs) of different observation stations are often different and unstable when the NC signal of the same radiation target strikes different observation locations. Besides, NC features of the target signals are applied not only to extend the virtual array manifold but also to bring high-dimensional search. For the sake of addressing the above problems, this study develops a DPD method of NC sources for multiple arrays combing weighted subspace data fusion (SDF) and dimension reduction (RD) search. First, NC features of the target signals are applied to extend the virtual array manifold. Second, we assign a weight to balance the error and obtain higher location accuracy with better robustness. Then, the RD method is used to eliminate the high computational complexity caused by the NC phase search dimension. Finally, the weighted fusion cost function is constructed by using the eigenvalues of the received signal covariance matrixes. It is verified by simulation that the proposed algorithm can effectively improve the location performance, get better robustness, and distinguish more targets compared with two-step location technology and SDF technology. In addition, without losing the estimation performance, the proposed algorithm can significantly reduce the complexity caused by the NC phase search dimension.

Dimension Reduction Analysis of Vowel Signal Data Based on Manifold Learning

Data dimension reduction is an important method to overcome dimension disaster and obtain as much valuable information as possible. Speech signal is a kind of non-stationary random signal with high redundancy, and proper dimension reduction methods are needed to extract and analyze the signal features efficiently in speech signal processing. Studies have shown that manifold structure exists in high-dimensional data. Manifold dimension reduction method aiming at discovering the intrinsic geometric structure of data may be more effective in dealing with practical problems. This paper studies a data dimension reduction method based on manifold learning and applies it to the analysis of vowel signals.

Accurate Prediction and Key Feature Recognition of Immunoglobulin

Immunoglobulin, which is also called an antibody, is a type of serum protein produced by B cells that can specifically bind to the corresponding antigen. Immunoglobulin is closely related to many diseases and plays a key role in medical and biological circles. Therefore, the use of effective methods to improve the accuracy of immunoglobulin classification is of great significance for disease research. In this paper, the CC–PSSM and monoTriKGap methods were selected to extract the immunoglobulin features, MRMD1.0 and MRMD2.0 were used to reduce the feature dimension, and the effect of discriminating the two–dimensional key features identified by the single dimension reduction method from the mixed two–dimensional key features was used to distinguish the immunoglobulins. The data results indicated that monoTrikGap (k = 1) can accurately predict 99.5614% of immunoglobulins under 5-fold cross–validation. In addition, CC–PSSM is the best method for identifying mixed two–dimensional key features and can distinguish 92.1053% of immunoglobulins. The above proves that the method used in this paper is reliable for predicting immunoglobulin and identifying key features.

Reliability Sensitivity Analysis Method for Mechanical Components

Based on the univariate dimension-reduction method (UDRM), Edgeworth series, and sensitivity analysis, a new method for reliability sensitivity analysis of mechanical components is proposed. The univariate dimension-reduction method is applied to calculate the response origin moments and their sensitivity with respect to distribution parameters (e.g., mean and standard deviation) of fundamental input random variables. Edgeworth series is used to estimate failure probability of mechanical components by using first few response central moments. The analytic formula of reliability sensitivity can be derived by calculating partial derivative of the failure probability P f with respect to distribution parameters of basic random variables. The nonnormal random parameters need not to be transformed into equivalent normal ones. Three numerical examples are employed to illustrate the accuracy and efficiency of the proposed method by comparing the failure probability and reliability sensitivity results obtained by the proposed method with those obtained by Monte Carlo simulation (MCS).

Designing a hybrid dimension reduction for improving the performance of Amharic news document classification

The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by combining feature selection and feature extraction. The new dimension reduction method utilizes Information Gain (IG), Chi-square test (CHI), and Document Frequency (DF) to select important features and Principal Component Analysis (PCA) to refine the features that have been selected. We evaluate the proposed dimension reduction method with a dataset containing 9 news categories. Our experimental results verified that the proposed dimension reduction method outperforms other methods. Classification accuracy with the new dimension reduction is 92.60%, which is 13.48%, 16.51% and 10.19% higher than with IG, CHI, and DF respectively. Further work is required since classification accuracy still decreases as we reduce the feature size to save computational time.