Efficient Analysis Methodology Research Articles

Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

A novel efficient methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity is proposed in this paper. Integrated computational material engineering requires efficient multiscale computational capabilities to enable computational design and validation. Two critical challenges are identified: handling uncertainties from microstructures and material properties; and handling the “curse of dimensionality” for probabilistic solvers. The proposed study addresses these two critical challenges. First, an analytical and hierarchical uncertainty quantification method is proposed for the explicit stochastic microstructure representation at the voxel-level. The hierarchy of uncertainties from both phase maps and uncertainties within each phase is modeled using an explicit Gaussian mixture random field. Analytical approximation for the arbitrary non-Gaussian random field is derived, which can facilitate the computation of gradient information in optimization. Following this, an efficient probabilistic solver using adjoint first-order reliability method combining the importance sampling is derived by formulating the material reliability analysis as a constrained optimization problem. The adjoint method is used to efficiently evaluate the responses and exact gradients with the help of the analytical Gaussian mixture random field. Several numerical examples for material reliability calculation with high-dimensional (voxel-level) random fields are subsequently employed to demonstrate and validate the proposed methodology. The results of the proposed method are quantitatively compared to those obtained via the classical first-order reliability method, direct Monte Carlo simulation, subset simulation, and the sequential importance sampling method. The comparisons indicate that the proposed method possesses high efficiency for high-dimensional material reliability problems.

Hydration methods and physiological potential of sweet corn seeds

The evaluation of the physiological potential of sweet corn seeds is fundamental in the production process, because the germination and vigor reflect the potential for storage, commercialization and use of these seeds. The choice of vigor tests with an adequate and efficient analysis methodology is fundamental to obtaining reliable results. In this sense, the objective of this study was to assess the physiological potential of sweet corn seeds after hydration using different moistening methods to increase water content to 20%. First, the lot of seeds was characterized as to its moisture content and initial physiological potential. Subsequently, two experiments were conducted, both under a completely randomized design. In the first experiment, different volumes of water on the filter paper sheets were assessed for seed hydration a method that uses a wet substrate. In the second, four hydration methods were evaluated: wet substrate (WS), humid atmosphere (HU), immersion in water (IW) and addition of the required amount of water (WR). A constant temperature of 25ºC was used in both experiments and then the moisture content and physiological potential of seeds were determined. The humid atmosphere method reduces the physiological quality of sweet corn seeds because it favors the deterioration and hence is unsuitable for the hydration of seeds of this species. The wet substrate method, in an amount equivalent to 2.5 times the paper mass, increased the water content of sweet corn seeds to 20%, achieving the desired degree of moisture without altering their physiological potential.

Towards the Design of a Multispar Composite Wing

In the pursuit of a lighter composite wing design, fast and effective methodologies for sizing and validating the wing members (e.g., spar, ribs, skins, etc.) are required. In the present paper, the preliminary design methodology of an airliner main composite wing, which has an innovative multispar configuration instead of the conventional two-spar design, is investigated. The investigated aircraft wing is a large-scale composite component, requiring an efficient analysis methodology; for this purpose, the initial wing sizing is mostly based on simplified Finite Element (FE) stress analysis combined to analytically formulated design criteria. The proposed methodology comprises three basic modules, namely, computational stress analysis of the wing structure, comparison of the stress–strain results to specific design allowable and a suitable resizing procedure, until all design requirements are satisfied. The design constraints include strain allowable for the entire wing structure, stability constraints for the upper skin and spar webs, as well as bearing bypass analysis of the riveted/bolted joints of the spar flanges/skins connection. A comparison between a conventional (2-spar) and an innovative 4-spar wing configuration is presented. It arises from the comparison between the conventional and the 4-spar wing arrangement, that under certain conditions the multispar configuration has significant advantages over the conventional design.

Performance Evaluation of Bus Rapid Transit System: A Comparative Analysis of Alternative Approaches for Energy Efficient Eco-Friendly Public Transport System

The development of the bus rapid transit system (BRTS) is tremendously growing in developing countries of the world. In large cities, the projection of transportation intends to enhance economic growth and changes the image of the city for both residents and outsiders. The purpose of this research was to study the application of alternative options for energy efficient BRTS in developing countries. The BRTS has some of its accessibility patterns that relate to the socio-economic strata. A decision-making efficiency analysis methodology has been applied to analyze the comparative analysis of both conventional fuel and hybrid bus systems for the Multan city of Pakistan. The section-wise application of a hybrid energy-based bus system has been analyzed in comparison to the conventional bus system. Out of 21 stations, the efficiency-wise hybrid bus system remained superior or equivalent to the standard value of 1 except one midpoint section. The finding of the analysis indicates that the hybrid mechanism of buses can not only replace a conventional fuel-based system, but will also help as an energy-efficient and eco-friendly economical solution. This study will help to revolutionize the bus rapid transit system in developing countries.

Probabilistic fragility and resilience assessment and sensitivity analysis of bridges incorporating aftershock effects

ABSTRACT This paper discusses a computationally efficient methodology for probabilistic fragility and resilience analysis of bridges explicitly incorporating the effects of aftershock seismic events, based on nonlinear time history analysis, stochastic simulation and kriging surrogate modeling. Aftershock fragilities dependent on mainshock-induced damage states are developed and used in conjunction with restoration models for estimating bridge functionality and resilience. The input parameter space for the surrogate models consists of uncertain hazard and structural parameters and deterministic structural/geometrical bridge parameters, leading to probabilistic estimates of bridge performance metrics (i.e., fragility, functionality, resilience). Kriging surrogate modeling is used for estimating these metrics, facilitating efficient adoption of these complex structural and probability models. Relative importance of the predictive parameters is quantified by extending and integrating a probabilistic sensitivity analysis into the proposed framework. An illustrative example of the proposed framework is demonstrated through fragility and resilience assessment of a typical bridge in California subjected to mainshock and aftershock.

A versatile macro-based neurohistological image analysis suite for ImageJ focused on automated and standardized user interaction and reproducible data output

BackgroundThe development and increasing adoption of advanced microscopy imaging technologies, including high resolution, multi-dimensional digital photography and multiple fluorescence channel acquisition, as well as the availability of inexpensive terabyte-capacity storage, have enabled research laboratories to pursue neurohistological imaging experiments involving multiple neurochemical probes and experimental conditions covering a variety of brain regions. Analyzing and processing the resulting datasets, composed of hundreds of micrographs, presents challenges in ensuring accuracy and reproducibility under demanding time and training constraints. New methodThe ‘Custom Macros’ plugin suite for ImageJ automates and systematizes user interaction in neurohistological image analysis tasks, including region selection and thresholding, point/object counts, area measurement, batch filter processing, and data review. Written in the accessible ImageJ macro language, the plugin implements a user login-based data storage framework and facilitates inter-laboratory collaboration over cloud file server clients. ResultsA macro-based interface approach integrates dozens of novel operations, software interactions, algorithm calls, and background tasks into individual shortcut commands. Every completed procedure generates image, region, and calibrated measurement records that are saved in a standardized folder structure. Comparisons with existing methodsPlugin installation adds startup access to a persistent interface layer of extensive and streamlined functionality that is generalizable to a variety of neurohistological contexts, thus providing an efficient and reliable alternative to the use of analysis software in an unstructured, provisional manner that necessitates repeated menu and plugin interaction. ConclusionsOur free/open-source software provides researchers a straightforward solution to addressing daunting usability and data oversight issues, ultimately making efficient, accessible, and reproducible image analysis methodology attainable for many laboratories.

Probabilistic hurricane risk analysis of coastal bridges incorporating extreme wave statistics

Coastal bridges sustained severe damage during hurricanes Ike, Katrina, and Ivan. Reducing the impact of future hurricane events to coastal bridges requires conducting a comprehensive risk analysis. A comprehensive risk analysis of bridges enables owners to assign limited resources to the most critical bridges in the inventory through a risk-informed decision making process. This study presents a computationally efficient methodology for structural fragility analysis and risk assessment of simply supported coastal bridges vulnerable to hurricane hazard. Various sources of uncertainty associated with hurricane hazard and bridge response are identified, and thereby establishing probability distributions. The novelty of the proposed method includes the consideration of uncertainties in extreme wave heights and wave period by means of a wave spectral density distribution in the calculation of wave forces. The proposed hurricane risk analysis method was successfully applied to coastal bridges located in the state of Georgia (U.S.A). The application presents the derivation of probabilistic models of demand, capacity and hazard; and highlights the potential of the proposed framework for identification and ranking of the most vulnerable bridges.

Mining E-commerce Websites to Provide Efficient Methodology for Sentiment Analysis

Mining E-commerce Websites to Provide Efficient Methodology for Sentiment Analysis

An efficient methodology for robust control of dynamic systems with interval matrix uncertainties

ABSTRACTInterval models are frequently used for dealing with uncertainties of control systems. However, it is well known that direct analysis and synthesis of a controlled dynamic system with interval matrix uncertainties may be a NP-hard problem. In this work, an efficient methodology for robustness analysis and robust control design of dynamic systems with interval matrix uncertainties is presented systematically, in which the uncertainties appearing in the controlled plant and controller realisation are taken into account simultaneously in an integrated framework. The fundamental problems, such as quadratic stability, guaranteed cost control and H∞ control of uncertain systems are taken as examples to show the methodology. Necessary and sufficient conditions for linear dynamic systems with interval matrices are derived by transforming all the interval matrices into some more tractable forms. The whole reasoning process is logical and rigorous, and NP-hard problem is successfully avoided. The presented formulations are within the framework of linear matrix inequality and can be implemented conveniently. In contrast to existing vertex-set methods, in which the vertices of interval matrices need to be constructed and checked, the presented methods are more efficient. Three numerical examples are investigated to demonstrate the effectiveness and feasibility of the presented method.

Research on social data by means of cluster analysis

This paper presents a data mining study and cluster analysis of social data obtained on small producers and family farmers from six country cities in Ceará state, northeast Brazil. The analyzed data involve demographic, economic, agriculture and food insecurity information. The goal of the study is to establish profiles for the small producer families that reside in the region and to identify relevant features which differentiate these profiles. Moreover, we provide an efficient data mining methodology for analysis of social data sets which is capable of handling its natural challenges, such as mixed variables and abundance of null values. We use the Silhouette method for the estimation of the best number of natural groups within the data, along with the Partitioning Around Medoids clustering algorithm in order to compute the profiles. The Correlation-Based Feature Selection method is used to identify which social criteria are the most important to differentiate the families from each profile. Classification models based on support vector machines, multilayer perceptron and decision trees were developed aiming to predict in which of the identified clusters an arbitrary family would be best fit. We obtained a good separation of the families into two clusters, and a multilayer perceptron model with approximately 93.5% prediction accuracy.

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude ∈[0,1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. The approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

Efficient methodology for probabilistic analysis of consolidation considering spatial variability

The inherent spatial variability of natural soil deposits should be considered to obtain realistic probabilistically-based estimates of the time for soil consolidation. Although a variety of commercial software packages for solving consolidation analyses within numerical framework exist, they cannot incorporate spatial variability in a robust manner. Therefore, the consideration of the spatial variability is difficult and time consuming. This study proposes two efficient approaches for probabilistic analysis of consolidation considering the spatial variability of coefficient of consolidation (c), as well as a technique for calculating a representative c for combined vertical and radial consolidation. The proposed approaches, which include a first-order reliability (FORM) and stochastic response surface method (SRSM), were applied to examples of 1-D vertical consolidation and combined vertical and radial consolidation problems, and the applicability of the models and spatial variability are identified. As a result, the random field and numerical consolidation analysis were decoupled, and the proposed approaches were able to perform probabilistic consolidation analysis considering spatial variability using existing deterministic finite element codes without any modification. The probability of under-consolidation can be predicted by only 6 and 11 consolidation analysis using the FORM and SRSM approaches, respectively, and the accuracy of these two approaches was shown to be identical to the results of more computationally-expensive, conventional Monte Carlo simulation (MCS) results. It was found that the probabilistic consolidation analyses are sensitive to the spatial variability of c, and the variability of average degree of consolidation increases as the scale of fluctuation increases, which can be effectively identified and treated using either of the proposed approaches.

Efficiency analysis methodology of FPGAs based on lost frequencies, area and cycles

We propose a methodology to study and to quantify efficiency and the impact of overheads on runtime performance. Most work on High-Performance Computing (HPC) for FPGAs only studies runtime performance or cost, while we are interested in how far we are from peak performance and, more importantly, why. The efficiency of runtime performance is defined with respect to the ideal computational runtime in absence of inefficiencies. The analysis of the difference between actual and ideal runtime reveals the overheads and bottlenecks. A formal approach is proposed to decompose the efficiency into three components: frequency, area and cycles. After quantification of the efficiencies, a detailed analysis has to reveal the reasons for the lost frequencies, lost area and lost cycles. We propose a taxonomy of possible causes and practical methods to identify and quantify the overheads. The proposed methodology is applied on a number of use cases to illustrate the methodology. We show the interaction between the three components of efficiency and show how bottlenecks are revealed.

Reliability-Aware Circuit Design Methodology for Beyond-5G Communication Systems

This paper focuses on efficient reliability analysis methodologies applicable for beyond-5G communication systems demonstrated on prospective terahertz (THz) technologies. Recently, a lot of the research interests have grown on optoelectronic integration which requires simultaneous management of electronic and optical modules. These technologies are evolving very rapidly, providing higher complexity, thereby increasing their susceptibility to stress environments (i.e., mutual self-heating) and finally requiring both robustness assessment and lifetime prediction under different operating conditions. Consequently, long-time reliability is a major issue. Extensive reliability assessment needs novel design methodologies that can provide reliability-aware optimization at the design level. This review discusses an extensive physics-based methodology for reliability prediction and analysis of degradation mechanisms in integrated-circuit technologies. Circuit-level design and optimization methodologies are illustrated by taking into account reliability-aware design techniques. Application examples and results are presented for an InP DHBT process, a viable technological solution for beyond-5G and THz applications.

Efficient and reliable fault analysis methodology for nanomagnetic circuits

SummaryThe increasing issues in scaled Complementary Metal Oxide Semiconductor (CMOS) circuit fabrication favor the flourishing of emerging technologies. Because of their limited sizes, both CMOS and emerging technologies are particularly sensitive to defects that arise during the fabrication process. Their impact is not easy to analyze in order to take the necessary countermeasures, especially in the case of circuits of realistic complexity based on emerging technologies. In this work, we propose a new methodology supported by an efficient and reliable tool for the identification of the impact of faults in complex circuits implemented using the emerging technology we are focusing on in this case: nanomagnetic logic.The methodology is based on three main steps: (i) we performed exhaustive physical‐level simulations of basic blocks based on a detailed finite‐element tool in order to have a full characterization, to know their properties in presence of defects, and to have a solid reference point for the following steps; (ii) we developed a model (fanomag) for the basic block behavior suitable for simulations in presence of defects of complex circuits, that is, lighter than a physical level one, but accurate enough to capture the most important features to be inherited at circuit level; (iii) starting from a physical design of complex circuits that we perform using a specific design tool we developed, that is, ToPoliNano, we simulated using fanomag, now embedded in our ToPoliNano tool, the behavior of circuits in presence of multiple sets of fabrication defects using a Monte Carlo approach now included in ToPoliNano as a new feature. In this paper, a specific type of defect is considered as a case study. The framework and methodology are conceived to be easily extended to handle other types of defects and problems due to working conditions that a designer and/or a technologist might want to focus on.The major outcome is then a powerful methodology and tool capable to analyze with a good accuracy nanomagnetic logic complex circuits and architectures both in ideal conditions and in presence of defects with remarkable performance in terms of simulation times. Copyright © 2016 John Wiley & Sons, Ltd.

Failure path-independent methodology for structural damage evolution and failure mode analysis of framed structures

Traditional methods for structural damage evolution and failure mode analysis usually depend on specific failure path analysis, which is inefficient and time-consuming to implement for complex structures. In this paper, an efficient failure path-independent methodology for structural damage evolution and failure mode analysis of framed structures is proposed based on the elastic modulus reduction method. The element bearing ratio, uniformity of element bearing ratio and reference element bearing ratio are defined in terms of the homogeneous generalized yield function of spatial beam element, while a dynamic criterion is presented to identify the highly stressed elements. Subsequently, a self-adaptive strategy of elastic modulus adjustment is developed to reduce the elastic moduli of the highly stressed elements for simulation of structural damage evolution. Finally, the reduction rate of elastic moduli of the highly stressed elements is presented to determine the failed sections among the failure elements in the last iteration, based on which the potential failure mode is identified. The applicability, computational accuracy and efficiency of the proposed methodology are validated by comparing with the elasto-plastic methods through two numerical examples.

VariantSpark: applying Spark-based machine learning methods to genomic information

Genomic information is increasingly being used for medical research, giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. Catering for this need, we developed VariantSpark, a framework for applying machine learning algorithms in MLlib to genomic variant data using the efficient in-memory Spark compute engine. We demonstrate a speedup compared to our earlier Hadoop-based implementation as well as a published Spark-based approach using the ADAM framework. Adapting emerging methodologies for fast efficient analysis of large diverse data volumes to process genomic information, will be the cornerstone for precision genome medicine, which promises tailored healthcare for diagnosis and treatment.

Incremental Dynamic Analysis: A Nonlinear Stochastic Dynamics Perspective

AbstractAn efficient stochastic incremental dynamic analysis (IDA) methodology for nonlinear/hysteretic oscillators is developed by resorting to nonlinear stochastic dynamics concepts and tools such as stochastic averaging and statistical linearization. Specifically, modeling the excitation as a nonstationary stochastic process possessing an evolutionary power spectrum (EPS), an approximate closed-form expression is derived for the parameterized oscillator response amplitude probability density function (PDF). In this regard, an IDA surface is determined providing the PDF of the engineering demand parameter (EDP) for a given intensity measure (IM) value. In contrast to a computationally expensive Monte Carlo simulation (MCS) based determination of the IDA surface, the methodology developed herein determines the EDP PDF at minimal computational cost. Further, an approximate closed-form expression is derived for the parameterized nonlinear oscillator response EPS as well; thus, a conceptually novel IDA surf...

Autonomously evolving classifier TEDAClass

In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process.

VariantSpark: population scale clustering of genotype information

BackgroundGenomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. The widely used Hadoop MapReduce architecture and associated machine learning library, Mahout, provide the means for tackling computationally challenging tasks. However, many genomic analyses do not fit the Map-Reduce paradigm. We therefore utilise the recently developed Spark engine, along with its associated machine learning library, MLlib, which offers more flexibility in the parallelisation of population-scale bioinformatics tasks. The resulting tool, VariantSpark provides an interface from MLlib to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results.ResultsTo demonstrate the capabilities of VariantSpark, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VariantSpark is 80 % faster than the Spark-based genome clustering approach, adam, the comparable implementation using Hadoop/Mahout, as well as Admixture, a commonly used tool for determining individual ancestries. It is over 90 % faster than traditional implementations using R and Python.ConclusionThe benefits of speed, resource consumption and scalability enables VariantSpark to open up the usage of advanced, efficient machine learning algorithms to genomic data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2269-7) contains supplementary material, which is available to authorized users.