Adaptive Analysis Method Research Articles

Adaptive Mission Planning and Analysis for Complex Systems

This paper presents a new method for complex system failure analysis and adaptive mission planning that provides both an overall failure analysis on a system's performance as well as a mission-based failure analysis. The adaptive mission planning and analysis (AMPA) method presented here uses physics-based governing equations to identify the system's overall behavior during both nominal and faulty conditions. The AMPA method is unique, in which it first identifies a specific failure or combination of failures within a system and then determines how each failure scenario will affect the system's overall performance characteristics, i.e., its functionality. Then, AMPA uses this failure information to assess and optimize various missions that the system may be asked to perform. The AMPA method is designed to identify functional failures of a given system and then, depending on the types of failures that have occurred and what tasks the system will be asked to perform, identify the optimal functional approach needed for moving forward to successfully complete its mission. Ultimately, this method could be applied in situ to systems using sensor data rather than simulations to allow autonomous systems to automatically adapt to failures. That is, by using the remaining healthy components in a new or different way to compensate for the faulty components to extend the systems lifespan and optimize the chance of mission completion.

EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

Chatter is a kind of self-excited unstable vibration during machining process, which always leads to multiple negative effects such as poor surface quality, dimension accuracy error, excessive noise, and tool wear. For purposes of monitoring the processing state of milling process and detecting chatter timely, a novel online chatter detection method was proposed. In the proposed method, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals. The two indicators power spectral entropy and fractal dimension which is obtained by morphological covering method are introduced to detect the chatter features. Then, both the frequency characteristic and morphological feature of the extracted signals can be reflected by the two indicators. To verify the approach, milling experiments were performed; the experiment results show that the proposed method can detect chatter timely and effectively, which is important in the aspect of improving the milling quality. And finally, in order to detect milling chatter timely, an online milling chatter monitoring system was developed.

Applicability of nonlinear static procedures for low-rise unreinforced masonry buildings with flexible diaphragms

The applicability of the N2 method, the modal pushover analysis (MPA) and an adaptive pushover analysis method are investigated for estimating the peak seismic responses of unreinforced masonry buildings with flexible diaphragms. The performance of nonlinear static procedures are compared against the nonlinear time-history analyses of three low-rise building models with various levels of stiffness eccentricity, type of failure mechanisms of piers (rocking or shear), and a range of diaphragm stiffness representing timber floor and roof systems. The results indicate that the MPA is unsuitable for unreinforced masonry buildings with flexible diaphragms, if the building response is shear-dominated. The adaptive method provides the most accurate estimates when the diaphragms are relatively stiff. When the diaphragms are relatively flexible, none of these methods can provide accurate predictions of peak seismic demands. However, conservative results may be obtained with the N2 method, by taking the envelope of pushover analyses carried out using the force distributions proportional to the uniform and linear displacement shapes along the height of the building. The present study has also identified the most suitable parameters/methods for the use of various nonlinear static procedures, such as the location of the control node used in the N2 method and the modal combination rule used in the MPA, for unreinforced masonry buildings with flexible diaphragms.

Measuring trade-offs in nephrology: a systematic review of discrete choice experiments and conjoint analysis studies.

Discrete choice experiment (DCE), conjoint analysis or adaptive conjoint analysis methods are increasingly applied to obtain patient, clinician or community preferences in nephrology. This study systematically reviews the above-mentioned published choice studies providing an overview of the issues addressed, methods and findings. Choice studies relating to nephrology were identified using electronic databases, including Medline, Embase, PsychINFO and Econlit from 1990 to 2015. For inclusion in the review, studies had to primarily relate to kidney disease and include results from statistical (econometric) analyses of respondents' choice or preference. Studies meeting the inclusion criteria were assessed against a range of systematic review criteria, and methods and results summarized. We identified 14 eligible studies from Europe, Australasia, North America and Asia, reporting preferences for treatment or screening, patient experiences, quality of life (QOL), health outcomes and priority-setting frameworks. Specific contexts included medical interventions in kidney transplantation and renal cell carcinoma, health policies for organ donation and allocation, dialysis modalities and end-of-life care, using a variety of statistical models. The characteristics of 'time' (i.e. transplant waiting time, dialysis hours, transport time) and QOL (pre- and post-transplant, or pre- and post-dialysis) consistently influenced patient and clinician preferences across the choice studies. DCE are increasingly used to obtain information about key preferences in kidney transplantation and dialysis. These study methods provide quantitative information about respondents' trade-offs between conflicting clinical and policy objectives, and can establish how preferences vary among stakeholder groups.

A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979kgha−1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211kgha−1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.

Research on PGNAA adaptive analysis method with BP neural network

A new approach method to dealing with the puzzle of spectral analysis in prompt gamma neutron activation analysis (PGNAA) is developed and demonstrated. It consists of utilizing BP neural network to PGNAA energy spectrum analysis which is based on Monte Carlo (MC) simulation, the main tasks which we will accomplish as follows: (1) Completing the MC simulation of PGNAA spectrum library, we respectively set mass fractions of element Si, Ca, Fe from 0.00 to 0.45 with a step of 0.05 and each sample is simulated using MCNP. (2) Establishing the BP model of adaptive quantitative analysis of PGNAA energy spectrum, we calculate peak areas of eight characteristic gamma rays that respectively correspond to eight elements in each individual of 1000 samples and that of the standard sample. (3) Verifying the viability of quantitative analysis of the adaptive algorithm where 68 samples were used successively. Results show that the precision when using neural network to calculate the content of each element is significantly higher than the MCLLS.

Fault detection for turbine engine disk based on an adaptive kernel principal component analysis algorithm

For commercial aircraft, real-time fault detection is essential for condition monitoring of rotating engine components, which can improve aviation safety and reduce maintenance cost for airline companies. In this article, based on the adaptive kernel principal component analysis method, a real-time fault detection algorithm is proposed for turbine engine disk condition monitoring. A sample reduction strategy based on the k-nearest neighbors method is presented to speed up the kernel principal component analysis approach while still guaranteeing correct results. To efficiently detect fault, the fault detection model is updated timely to suit the working process of turbine engine disk. Sample clusters are obtained through the k-mean method, and the parameter of the kernel function is adaptively adjusted by minimizing the within-cluster distance and maximizing the between-cluster distance in the feature space. Experiments have demonstrated the superiority of the proposed approach in fault detection for turbine engine disk.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal–spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.

A fully general and adaptive inverse analysis method for cementitious materials

The paper presents an adaptive method for inverse determination of the tensile relationship, direct tensile strength and Young's modulus of cementitious materials. The method facilitates an inverse analysis with a multi-linear function. Usually, simple bi- or tri-linear functions are applied when modeling the fracture mechanisms in cementitious materials, but the vast development of pseudo-strain hardening, fiber reinforced cementitious materials require inverse methods, capable of treating multi-linear functions. The proposed method is fully general in the sense that it relies on least square fitting between test data obtained from various kinds of test setup, three-point bending or wedge splitting test, and simulated data obtained by either FEA or analytical models. In the current paper adaptive inverse analysis is conducted on test data obtained from three-point bending of notched specimens and simulated data from a nonlinear hinge model. The paper shows that the results obtained by means of the proposed method is independent on the initial shape of the function and the initial guess of the tensile strength. The method provides very accurate fits, and the increased number of variables describing the relationship constitutes the basis for obtaining detailed information of crack propagation in any cementitious material.

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics.

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

BackgroundFunctional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience. New methodTo deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise. ResultsWe revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD. Comparison with existing method(s)Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution. ConclusionsWe found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Adaptive isogeometric finite element analysis of steady‐state groundwater flow

SummaryNumerical challenges occur in the simulation of groundwater flow problems because of complex boundary conditions, varying material properties, presence of sources or sinks in the flow domain, or a combination of these. In this paper, we apply adaptive isogeometric finite element analysis using locally refined (LR) B‐splines to address these types of problems. The fundamentals behind isogeometric analysis and LR B‐splines are briefly presented. Galerkin's method is applied to the standard weak formulation of the governing equation to derive the linear system of equations. A posteriori error estimates are calculated to identify which B‐splines should be locally refined. The error estimates are calculated based on recovery of the L2‐projected solution. The adaptive analysis method is first illustrated by performing simulation of benchmark problems with analytical solutions. Numerical applications to two‐dimensional groundwater flow problems are then presented. The problems studied are flow around an impervious corner, flow around a cutoff wall, and flow in a heterogeneous medium. The convergence rates obtained with adaptive analysis using local refinement were, in general, observed to be of optimal order in contrast to simulations with uniform refinement. Copyright © 2015 John Wiley & Sons, Ltd.

Simulation Model of Surface Heterogeneity, Using Gielis' Superformula for Adaptive Methods of Image Analysis

The article presents a developed orginal model designed to increase the efficiency of the processes of the design of adaptive image analysis methods used for detection of surface defects in materials. The model was created as part of activities related to the Strategic Program – Specialized knowledge bases and expert systems for the simulation of complex processes. The methods presented in this paper make it possible to accelerate design process, including testing of algorithms developed for a wide range of surface defects, such as: cracks, discoloration, loss of materials, geometric distortion, or even the presence of defect agglomerations caused by corrosion, without the need for the acquisition of the physical image of the actual objects. This in many cases can be a significant problem for engineers who design automatic optical inspection systems, because the acquisition of test objects with specified defects which are characterized by a fixed range of values of selected parameters is not always possible. This paper presents a formal model designed to generate material defects on the surface of three-dimensional virtual objects, which is equivalent to the acquisition of actual data from vision systems. The model takes into account various surface characteristics such as their texture or roughness by using mapping by the Blinn method. The results of the use of the system developed for the classification of products represented in digital images for which image analysis algorithms have been based on so-called artificial intelligence in the form of dedicated neural networks are presented. As described in this paper, artificial neural networks are an example of adaptive models, and provide the ability to solve problems for which there are no deterministic models. The models, however, require the use of learning processes (training) with the use of extensive data sets, which in this case were generated with the use of the developed solution model.

An Adaptive Analysis of the Ship Structures Based on Meshless Local Petrov-Galerkin Method

The paper presents an adaptive analysis method to assess the ship structures based on the meshless local Petrov-Galerkin method. The displacement fleld approximation function is obtained by employing Moving-least Square technique, and the governing formulation and stifiness matrix of the structures are established. Employing Delaunay triangulation reflnement scheme, the adaptive algorithm of the governing equations is achieved. Numerical examples show the solutions are good agreement between FEMs and the proposed method, which verifles the validity of the presented method for stress analysis of the ship structures.

An adaptive KPCA approach for detecting LDoS attack

SummaryLow‐rate denial‐of‐service (LDoS) attack sends out attack packets at low‐average rate of traffic flow in short time. It is stealthier than traditional DoS attack, which makes detection of LDoS extremely difficult. In this paper, an adaptive kernel principal component analysis method is proposed for LDoS attack detection. The network traffic flow is extracted through wavelet multi‐scale analysis. An adaptive kernel principal component analysis method is adopted to detect LDoS attack through the squared prediction error statistics. Key parameters such as the parameter of the radial basis function, the number of principal components, and the squared prediction error confidence limit are adaptively trained with training data and updated with the network environment. Simulation is accomplished in NS‐2 environment, and results prove the favorable LDoS attack detection efficiency by the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.

Quantitative structure–activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods

Bruton’s tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure–activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q2 = 0.884, r2 = 0.929, r2pred = 0.878; 3D-QSAR: q2 = 0.616, r2 = 0.987, r2pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure–activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

Robust adaptive principal component analysis based on intergraph matrix for medical image registration.

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

Agent-Based Supernetworks Model of University Knowledge System

A novel supernetworks framework of knowledge system based on complex networks is proposed for presenting the multidimensional, multilevel, dynamic complexity characteristics and initiative, adaptability, and purposefulness of knowledge mainbody in the university. This model differs from the existing knowledge diffusion method. In order to make good use of abundant information of the mainbody and improve the performance of knowledge diffusion, agent-based model and supernetworks method are adopted to exploit the complex correlation among the knowledge mainbody, knowledge carrier, and knowledge element. Firstly, the agent-based supernetworks model of knowledge diffusion was established by use of complex adaptive system theory and supernetworks analysis method. Secondly, the multilevel structure definition of supernetworks and knowledge networks of mainbody, element, and carrier in the knowledge system were described in detail. Thirdly, for verifying the validity of the model, a local search operator was designed, which can improve the effectiveness and efficiency of knowledge diffusion. Experimental studies based on synthetic datasets show that the proposed method can exhibit good performance, especially providing theoretical and practical guidance for establishing harmonious relationship between students and teachers.

Differential signal-assisted method for adaptive analysis of fringe pattern.

In dynamic 3D measurements, the recovering carrier signal as well as phase retrieval are important issues for single fringe image analysis. Local mean decomposition is a powerful signal demodulation tool, but it usually encounters an obstacle, namely, the mode-mixing problem. Then different components, especially noise and carrier signals, are all probably mixed in one of the decomposition results, thus confusing its physical meaning. Utilizing the characteristics of original noise, we design a pair of differential signals based on the conditions that two mixed components should meet to be separated completely and then add them to the original signal. Re-decomposing the newly formed signal, the differential signal, along with the original noise, will be separated from the carrier signal, leaving very little negative impact due to the characteristics of the same amplitude and opposite polarity of the differential signal. With the mode-mixing problem of high-frequency components being resolved, the decomposition of the following low-frequency components becomes more reasonable, facilitating the fringe pattern analysis and further phase retrieval. The proposed method is suitably used for the signal, even though it is not stable. Experiments illustrate the efficiency of this novel adaptive method.

Fractal behavior in the headway fluctuation simulated by the NaSch model

The fractal behavior of traffic flow is studied by the adaptive fractal analysis method on the basis of the vehicle headway time series, which are obtained from the numerical simulation of the NaSch model. We find that the vehicle headway time series has a fractal behavior that is similar to the standard Brownian motion (BM) over a wide range of scales when the density is low. As the density increases well-defined sharp spectral peaks, corresponding to the stop-and-go waves, appear while the scale range showing BM-like behavior rapidly shrinks. In the high density regime, a new type of fractal behavior with long-range correlations appears, accompanying the worsening of traffic congestions. The underlying dynamics of traffic flow is analyzed, and some meaningful results are obtained.