Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

Pathological Voice Classification Based on Features Dimension Opti mization

The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.

A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

Time Series Analysis for Vibration-Based Structural Health Monitoring:A Review

Structural health monitoring(SHM)is a vast,interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace,mechanical and civil structures.The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection.Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods.In the literature on the structural damage detection,many time series-based methods have been proposed.When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained,any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure.Depending on the technique employed,various damage sensitive features have been proposed to capture the deviations.This paper reviews the application of time series analysis for SHM.The different types of time series analysis are described,and the basic principles are explained in detail.Then,the literature is reviewed based on how a damage sensitive feature is formed.In addition,some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.

Feature Preserving Mesh Simplification Using Feature Sensitive Metric

We present a new method for feature preserving mesh simplification based on feature sensitive （FS） metric. Previous quadric error based approach is extended to a high-dimensional FS space so as to measure the geometric distance together with normal deviation. As the normal direction of a surface point is uniquely determined by the position in Euclidian space, we employ a two-step linear optimization scheme to efficiently derive the constrained optimal target point. We demonstrate that our algorithm can preserve features more precisely under the global geometric properties, and can naturally retain more triangular patches on the feature regions without special feature detection procedure during the simplification process. Taking the advantage of the blow-up phenomenon in FS space, we design an error weight that can produce more suitable results. We also show that Hausdorff distance is markedly reduced during FS simplification.