Time domain Boundary Element Method approaches in elastodynamics: A comparative study

Boundary Element Method formulations of elastodynamic problems under plane strain/plane stress conditions are presented. The formulations are performed in the time domain allowing consideration of loads with transient time variation. The advantages, efficiency and accuracy of the methods are depicted through comparative studies of a representative soil-structure interaction problem.     

Analysis of target configurations under dead loads for cable-supported bridges

This paper presents a rigorous approach for analyzing the target configurations of cable-supported structures under dead loads by the Newton–Raphson method. A linearized equilibrium equation of a cable element, which includes the nodal coordinates and the unstrained element length as unknowns, is formulated using the analytical solution of an elastic catenary cable. An incremental equilibrium equation for a single cable is formed with the proposed equilibrium matrices of cable elements. The geometry of the target configuration of a cable-supported structure under dead loads is utilized to solve the incremental equilibrium equation. Detailed procedures to analyze the target configurations of suspension bridges and cable-stayed bridges are presented. The efficiency and the accuracy of the proposed method are demonstrated through numerical examples.     

NASAB: a finite element software for the nonlinear aerostatic stability analysis of cable-supported bridges

This paper presents the development and applications of the finite element software, NASAB, which can be used for linear, geometrically nonlinear, and materially nonlinear analyses of structure and nonlinear aerostatic stability analysis of cable-supported bridges. The software program consists of two main parts: a programming part and a computational part. The windows programming part written in FORTRAN90 was designed mainly to present the NASAB software in a user-friendly environment. The computational part was written in FORTRAN77. The use of FORTRAN77 is to effectively take advantage of existing codes, thus speeding up code design and implementation. The usefulness of FORTRAN programming language to develop a user-friendly interface including pre-processing and post-processing has been demonstrated by the present version of the software.     

A comparative modeling study to estimate wear of concrete

This paper investigates the impacts of fuzzy genetic (FG), a new fuzzy logic model with genetic algorithm, artificial neural networks (ANN) and general linear model (GLM) approaches on abrasive wear of concrete. For this purpose, experimental studies were made to investigate the influence on wear of the following input parameters: hematite, cement, compressive strength and different loads on the experiments. In these models, 60 data sets were used. For training set, 48 data (80 %) were randomly selected and the residual data (12 data, 20 %) were test set. Model results were compared with experimental results. In this paper, main model performance criterion was root mean square errors. Also, sum of squared error and determination coefficient statistics were used as comparing criteria for the evaluation of models’ performances. Comparison results indicate that FG models are superior to ANN and GLM models in modeling of influence hematite, cement, compressive strength and loads on wear of concrete.     

Automatic identification of cardiac health using modeling techniques: A comparative study

Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.     

A comparative study of facial appearance modeling methods for active appearance models

Active appearance models (AAMs) have been widely used in many face modeling and facial feature extraction methods. One of the problems of AAMs is that it is difficult to model a sufficiently wide range of human facial appearances, the pattern of intensities across a face image patch. Previous researches have used principal component analysis (PCA) for facial appearance modeling, but there has been little analysis and comparison between PCA and many other facial appearance modeling methods such as non-negative matrix factorization (NMF), local NMF (LNMF), and non-smooth NMF (ns-NMF). The main contribution of this paper is to find a suitable facial appearance modeling method for AAMs by a comparative study. In the experiments, PCA, NMF, LNMF, and ns-NMF were used to produce the appearance model of the AAMs and the root mean square (RMS) errors of the detected feature points were analyzed using the AR and BERC face databases. Experimental results showed that (1) if the appearance variations of testing face images were relatively non-sparser than those of training face images, the non-sparse methods (PCA, NMF) based AAMs outperformed the sparse methods (nsNMF, LNMF) based AAMs. (2) If the appearance variations of testing face images are relatively sparser than those of training face images, the sparse methods (nsNMF) based AAMs outperformed the non-sparse methods (PCA, NMF) based AAMs.     

A comparative study of machine learning classifiers for modeling travel mode choice

The analysis of travel mode choice is an important task in transportation planning and policy making in order to understand and predict travel demands. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling travel mode choice remains largely unexplored. Using extensive Dutch travel diary data from the years 2010 to 2012, enriched with variables on the built and natural environment as well as on weather conditions, this study compares the predictive performance of seven selected machine learning classifiers for travel mode choice analysis and makes recommendations for model selection. In addition, it addresses the importance of different variables and how they relate to different travel modes. The results show that random forest performs significantly better than any other of the investigated classifiers, including the commonly used multinomial logit model. While trip distance is found to be the most important variable, the importance of the other variables varies with classifiers and travel modes. The importance of the meteorological variables is highest for support vector machine, while temperature is particularly important for predicting bicycle and public transport trips. The results suggest that the analysis of variable importance with respect to the different classifiers and travel modes is essential for a better understanding and effective modeling of people’s travel behavior.     

On the small signal modeling of advanced microwave FETs: A comparative study

Although many successful techniques have been proposed in the last decades for extracting the small signal equivalent circuit for microwave transistors from scattering parameter measurements, small signal modeling is still object of intense research. Further improvement and development of the proposed methods are incessantly required to take into account the continuous and rapid evolution of the transistor technology. The purpose of this article is to facilitate the choice of the most appropriate strategy for each particular case. For that, we present a brief but thorough comparative study of analytical techniques developed for modeling different types of advanced microwave transistors: GaAs HEMTs, GaN HEMTs, and FinFETs. It will be shown that a crucial step for a successful modeling is to adapt accurately the small signal equivalent circuit topology under “cold” condition to each investigated technology. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

小麦碰撞声信号时域建模与分类

分析小麦碰撞声信号,可识别受损小麦。提取三类小麦碰撞声信号,分析小麦碰撞声的时域特征,建立合适的拟合模型,并提取残差平方和、判定系数、峰值振幅等六个时域特征;最后利用BP神经网络进行分类,发现小麦完好粒、虫害粒及霉变粒碰撞声信号的时域特征存在差异,并取得了较好的识别率。应用结果表明选用适当的数学模型能够较好地拟合小麦碰撞声信号,实现区分受损小麦颗粒与完好小麦颗粒。     

A study of Nash-EGO algorithm for aerodynamic shape design optimizations

Structural and Multidisciplinary Optimization - In this paper, our newly proposed Nash-EGO algorithm is applied to real large-scale airfoil/wing optimizations. A finite wing is represented by...     

Audio watermarking technique in frequency domain: comparative study MDCT Vs DCT

Digital watermarking is the art of hiding information in a digital document in order to protect it. The inserted mark and the marked document can be an image, an audio or a video. In this article, we will present a comparative study between two variants of a digital audio watermarking technique operating in the frequency domain. In the first variant, the time-frequency mapping is performed by Modified Discrete Cosine Transform (MDCT). For the second variant, the time-frequency mapping is performed by the Discrete Cosine Transform (DCT). We will study the contribution of each transformation, point of view robustness against different types of attacks delivered by Stirmark audio, imperceptibility by using a statistical approach by calculating the SNR and an objective approach by calculating the ODG notes given by PEAQ and capacity of insertion. Finally, to highlight our results, we will compare the two variants of the proposed technique with some other existing techniques.

     

A validation of QDAcity-RE for domain modeling using qualitative data analysis

Requirements Engineering - Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to...     

A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction

This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network architectures evaluated are the multi-layer feed-forward network and the recurrent network. Simulation results on a set of compressor failures showed that in modeling the stochastic nature of reliability data, both the ARIMA and the recurrent neural network (RNN) models outperform the feed-forward model; in terms of lower predictive errors and higher percentage of correct reversal detection. However, both models perform better with short term forecasting. The effect of varying the damped feedback weights in the recurrent net is also investigated and it was found that RNN at the optimal weighting factor gives satisfactory performances compared to the ARIMA model.     

Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks.     

A comparative study between the augmented Lagrangian method and the complementarity approach for modeling the contact problem

The increasing demand for real-time high-fidelity multibody dynamics simulations in several modern fields such as robotics and computer game industries has motivated many researches to propose novel approaches to model multibody systems with several contacts. The possibility of different contact conditions in a system with several contacts yields a combinatorial problem of potentially large size. Rigid contact model which is the most common model used for real-time simulations yields a non-smooth dynamic formulation. The solution of such a system can be governed using different methods. In this paper a comparison between the complementarity approaches and the augmented Lagrangian based formulations to deal with non-smooth contact models is presented via numerical examples, and the advantages and shortcomings of each method are discussed.     

Nonlinear controllers for solar thermal plants: A comparative study

Solar plants have nonlinear dynamics which must be taken into account when a control system is applied to them. The main purpose of the control systems is to maintain the outlet temperature in a desired reference value and, at the same time, attenuate the undesirable transients caused by the disturbances. Linear controllers, like PID ones, are not able to obtain good performance over the whole operation range of these kind of plants. To overcome these limitations two nonlinear controllers, a nonlinear model-based predictive controller and a distributed sliding mode controller, are applied to a solar plant in this work. The performance of these controllers is tested through experimental and simulation results, which show the tracking and disturbance rejection capabilities of the proposed controllers.     

Constraint scores for semi-supervised feature selection: A comparative study

Recent feature selection scores using pairwise constraints (must-link and cannot-link) have shown better performances than the unsupervised methods and comparable to the supervised ones. However, these scores use only the pairwise constraints and ignore the available information brought by the unlabeled data. Moreover, these constraint scores strongly depend on the given must-link and cannot-link subsets built by the user. In this paper, we address these problems and propose a new semi-supervised constraint score that uses both pairwise constraints and local properties of the unlabeled data. Experiments using Kendall’s coefficient and accuracy rates, show that this new score is less sensitive to the given constraints than the previous scores while providing similar performances.     

Practical fusion algorithms for rotation matrices: A comparative study

Many computer vision, sensor fusion, and robotic applications require the estimation of a 3 × 3 rotation matrix from a set of measured or computed 3 × 3 noisy rotation matrices. This article classifies solution methods into three categories: nonlinear least squares, linear optimal, and linear suboptimal algorithms. Their performance is compared through simulation studies. It is shown that the linear suboptimal algorithms proposed in this article have an accuracy comparable to that of the optimal algorithms and are about five times faster. Furthermore, a particular nonlinear optimization algorithm is presented that has computational complexity similar to that of the linear optimal procedures. © 1992 John Wiley & Sons, Inc.     

A time‐domain approach for wideband modeling of electronic packages

This article presents a technique for wideband modeling and characterization of electronic packages. The modeling approach presented is based on time‐domain reflectometry techniques (TDRs). The time‐domain approach is capable of directly identifying the various contributions (reflections) from different discontinuities of the structure with a relatively high spatial resolution. Consequently, this makes it possible to isolate the effect of different discontinuities over the bandwidth of the package. This technique has been successfully applied to several packages, two of which are demonstrated in this article. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.