Numerical finite element formulation of the Schapery non‐linear viscoelastic material model

This study presents a numerical integration method for the non‐linear viscoelastic behaviour of isotropic materials and structures. The Schapery's three‐dimensional (3D) non‐linear viscoelastic material model is integrated within a displacement‐based finite element (FE) environment. The deviatoric and volumetric responses are decoupled and the strain vector is decomposed into instantaneous and hereditary parts. The hereditary strains are updated at the end of each time increment using a recursive formulation. The constitutive equations are expressed in an incremental form for each time step, assuming a constant incremental strain rate. A new iterative procedure with predictor–corrector type steps is combined with the recursive integration method. A general polynomial form for the parameters of the non‐linear Schapery model is proposed. The consistent algorithmic tangent stiffness matrix is realized and used to enhance convergence and help achieve a correct convergent state. Verifications of the proposed numerical formulation are performed and compared with a previous work using experimental data for a glassy amorphous polymer PMMA. Copyright © 2003 John Wiley & Sons, Ltd.     

Positivity of discrete singular systems and their stability: An LP-based approach

In this paper we present an efficient approach to the analysis of discrete positive singular systems. One of our main objectives is to investigate the problem of characterizing positivity of such systems. Previously, this issue was not completely addressed. We provide easily checkable necessary and sufficient conditions for such problem to be solved. On the other hand, we study the stability of discrete positive singular systems. Note that this is not a trivial problem since the set of admissible initial conditions is not the whole space but it is represented by a special cone. All the conditions we provide are necessary and sufficient and are based on a reliable computational approach via linear programming.     

Influence of 1,3-Butanediol on the Viscoelasticity of Surfactant Solutions

The rheological behavior of rodlike micelles in a 1,3-butanediol/water mixed solvent was investigated. The rodlike micelles were prepared by mixing sodium salicylate, with cetyltrimethylammonium bromide. When water was replaced by 1,3-butanediol, the viscosity maxima decreased drastically. The viscosity minimum, on the other hand, increased at low 1,3-butanediol contents and decreased again at higher amounts of 1,3-butanediol. As a result of the opposite changes the two maxima were replaced by a single maxima when the content of 1,3-butanediol was 15 %. The change in the viscosity was a consequence of a change in the structural relaxation times while the storage moduli remained constant. The stress relaxation was due to a reptation mechanism at maximum points while at the minimum of the viscosity the mechanism was due to a kinetic controlled mechanism.     

Optimal repair stage for K-out-of-N systems

Optimization studies on reliability systems is currently a fascinating area of research. In recent investigations, optimization techniques have been extended to cover even more complex reliability systems with larger applicational scope. In this paper, we study a complex system in terms of a K-out-of-N system (for example, occuring in mass transmission and computer networks) with provision for a repair facility. We develop an optimization procedure to help identify the Optimal Repair Stage for the system under certain conditions. The applicational use of the theoretical results is illustrated through numerical work, specifically the negative exponential law governing stochastic repair times.     

MULTIVARIABLE PROCESS IDENTIFICATION AND CONTROL OF CONTINUOUS FLUIDIZED BED DRYERS

Dynamic models that rigorously describe fluidized bed dryers based on the fundamental principles of the process are usually so complex to be employed in control system design. To obtain simple reduced-order models for such systems, a sequence of step changes in the manipulated and load variables is introduced into the rigorous model. The obtained input-output dynamic response data are used for off-line model identification. Different types of linear models are generated, which are shown to be adequately representing the fluidized bed drying dynamics. The derived models are useful to develop model-based control algorithms such as Internal Model Control (IMC) and Model Predictive Control (MPC). Performance and robustness properties of these controllers are analyzed. Simulation results demonstrate a good performance in terms of tracking and load rejection capabilities.     

Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences

Choice conjures the idea of a directed selection of a desirable action or object, motivated by internal likes and dislikes, or other such preferences. However, such internal processes are simply the domain of our human physiology. Understanding the physiological processes of decision making across a variety of contexts is a central aim in decision science as it has a great potential to further progress decision research. As a pilot study in this field, this paper explores the nature of decision making by examining the associated brain activity, Electroencephalogram (EEG), of people to understand how the brain responds while undertaking choices designed to elicit the subjects’ preferences. To facilitate such a study, the Tobii-Studio eye tracker system was utilized to capture the participants’ choice based preferences when they were observing seventy-two sets of objects. These choice sets were composed of three images offering potential personal computer backgrounds. Choice based preferences were identified by having the respondent click on their preferred one. In addition, a brain computer interface (BCI) represented by the commercial Emotiv EPOC wireless EEG headset with 14 channels was utilized to capture the associated brain activity during the period of the experiments. Principal Component Analysis (PCA) was utilized to preprocess the EEG data before analyzing it with the Fast Fourier Transform (FFT) to observe the changes in the main principal frequency bands, delta (0.5–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (30–40 Hz). A mutual information (MI) measure was then used to study left-to-right hemisphere differences as well as front-to-back difference. Eighteen participants were recruited to perform the experiments with the average results showing clear and significant change in the spectral activity in the frontal (F3 and F4), parietal (P7 and P8) and occipital (O1 and O2) areas while the participants were indicating their preferences. The results show that, when considering the amount of information exchange between the left and right hemispheres, theta bands exhibited minimal redundancy and maximum relevance to the task at hand when extracted from symmetric frontal, parietal, and occipital regions while alpha dominated in the frontal and parietal regions and beta dominating mainly in the occipital and temporal regions.     

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

A fundamental component of many modern prostheses is the myoelectric control system, which uses the electromyogram (EMG) signals from an individual’s muscles to control the prosthesis movements. Despite the extensive research focus on the myoelectric control of arm and gross hand movements, more dexterous individual and combined fingers control has not received the same attention. The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data from eight participants. Various feature sets are extracted and projected in a manner that ensures maximum separation between the finger movements and then fed to two different classifiers. The second contribution is the use of a Bayesian data fusion postprocessing approach to maximize the probability of correct classification of the EMG data belonging to different movements. Practical results and statistical significance tests prove the feasibility of the proposed approach with an average classification accuracy of ≈90% across different subjects proving the significance of the proposed fusion scheme in finger movement classification.