Equivalent dynamic thermoviscoelastic modeling of ionic polymers

Ionic polymers have attracted considerable attention due to their interesting sensing and actuating behavior which make them a proper choice for use in a wide range of applications including biomimetic robots and biomedical devices. The complicated electro-chemo-mechanical dynamics of ionic polymer actuators is a drawback for their applications in functional devices. Therefore, establishing a mathematical model which could effectively predict the actuators' dynamic behavior is of great interest. In this paper, a mathematical model, named equivalent dynamic thermoviscoelastic (EDT) model, based on thermal analogy and beam theory is proposed for dynamic analysis of bending-type ionic polymer actuators. Then, the developed model is extended for analyzing the performance of the actuator in finite element software. The finite element analysis of the actuator enables consideration of material and geometric nonlinearities and facilitates modeling of functional devices based on the ionic polymer actuators. The proposed modeling approach is validated using experimental data.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

Robust video/ultrasonic fusion-based estimation for automotive applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.

Numerical modeling of the dynamic tensile behavior of irregular fibers

Most fibers are irregular, and they are often subjected to rapid straining during mechanical processing and end-use applications. In this paper, the effect of fiber dimensional irregularities on the dynamic tensile behavior of irregular fibers is examined, using the finite element method (FEM). Fiber dimensional irregularities are simulated with sine waves of different magnitude (10%, 30% and 50% level of diameter variation). The tensile behavior of irregular fibers is examined at different strain rates (333%/sec, 3,333%/sec and 30,000%/sec). The breaking load and breaking extension of irregular fibers at different strain rates are then calculated from the finite element model. The results indicate that strain rate has a significant effect on the dynamic tensile behavior of an irregular fiber, and that the position of the thinnest segment along the fiber affects the simulation results markedly. Under dynamic conditions, an irregular fiber does not necessarily break at the thinnest segment, which is different from the quasi-static results.

Kinematic and dynamic modeling of a robotic head with linear motors

Our present research focuses on kinematic and dynamic modeling of a 3-DOF robotic cutting head for the next generation of CNC machines. The robotic cutting head is one kind of parallel manipulator of 3-PUU type, which has a high flexibility of motion in three-dimensional space. The parallel manipulator consists of three linear servomotors, which drive three connecting rods independently according to the cutting strategy. Being a parallel manipulator, the robotic cutting head has higher stiffness and position accuracy; consequently, higher velocities and accelerations can be achieved. A very suitable application of this mechanism is as a cutting head of a precision machine tool for three-dimensional cutting problems.

A physical based modeling approach for the dynamic behavior of ultrafine grained structures

This paper discusses some experimental results on the influence of grain refinement on the final mechanical properties of IF and microalloyed steels designed for auto-body components. It shows also some modeling approaches to understanding the dynamic behavior of fine-rained materials. The Zerilli–Armstrong (Z–A) and Khan–Huang–Liang (KHL) models for studied steels were implemented into FEM code in order to simulate the dynamic compression tests with different strain rates.

Microstructural modeling of dynamic recrystallization using irregular cellular automata

Cellular automaton (CA) was used to simulate dynamic recrystallization (DRX) during thermomechanical deformation. Initial grain size, initial grain orientation and dislocation density were used as input data to the CA model. Flow curve, dislocation density, final grain size and orientation, and DRX volume fraction were the output data which were compared with experimental data to validate the model. The model proposed in this work considered the thermomechanical parameters (e.g., temperature and strain rate) and their role on the nucleation and growth kinetics during DRX. It was shown that the CA model can predict the final microstructure and flow curve to a high degree of accuracy and was able to successfully simulate the volume fraction of DRX as a function of strain for a wide range of deformation conditions.

Modeling a dynamic data replication strategy to increase system availability in cloud computing environments

Failures are normal rather than exceptional in the cloud computing environments. To improve system avai-lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes: 1) analyzing and modeling the relationship between system availability and the number of replicas; 2) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3) calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4) designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Application of extended multivariate modeling for information flow analysis of event related responses

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning

This is an open access article under the CC BY-NC-ND license.Neuro-Fuzzy Systems (NFS) are computational intelligence tools that have recently been employed in hydrological modeling. In many of the common NFS the learning algorithms used are based on batch learning where all the parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, there is a criticism on such learning process as the number of rules are needed to be predefined by the user. This will reduce the flexibility of the NFS architecture while dealing with different data with different level of complexity. On the other hand, online or local learning evolves through local adjustments in the model as new data is introduced in sequence. In this study, dynamic evolving neural fuzzy inference system (DENFIS) is used in which an evolving, online clustering algorithm called the Evolving Clustering Method (ECM) is implemented. ECM is an online, maximum distance-based clustering method which is able to estimate the number of clusters in a data set and find their current centers in the input space through its fast, one-pass algorithm. The 10-minutes rainfall-runoff time series from a small (23.22 km2) tropical catchment named Sungai Kayu Ara in Selangor, Malaysia, was used in this study. Out of the 40 major events, 12 were used for training and 28 for testing. Results obtained by DENFIS were then compared with the ones obtained by physically-based rainfall-runoff model HEC-HMS and a regression model ARX. It was concluded that DENFIS results were comparable to HEC-HMS and superior to ARX model. This indicates a strong potential for DENFIS to be used in rainfall-runoff modeling.

A dynamic ratio-based model for signalling corporate collapse

The recognition of behavioural elements in finance has caused major shifts in the analytic framework pertaining to ratio-based modeling of corporate collapse. The modeling approach so far has been based on the classical rational theory in behavioural economics, which assumes that the financial ratios (i.e., the predictors of collapse) are static over time. The paper argues that, in the absence of rational economic theory, a static model is flawed, and that a suitable model instead is one that reflects the heuristic behavioural framework, which is what characterises behavioural attributes of company directors and in turn influences the accounting numbers used in calculating the financial ratios. This calls for a dynamic model: dynamic in the sense that it does not rely on a coherent assortment of financial ratios for signaling corporate collapse over multiple time periods. This paper provides empirical evidence, using a data set of Australian publicly listed companies, to demonstrate that a dynamic model consistently outperforms its static counterpart in signaling the event of collapse. On average, the overall predictive power of the dynamic model is 86.83% compared to an average overall predictive power of 69.35% for the static model.

Modeling and simulation of collisions of heavy trucks with concrete barriers

On-site collision tests of full-scale concrete barriers are an important method to understand what happens to concrete barriers when vehicles collide with them. However, such tests require both time and money, so modeling and simulation of collisions by computer have been developed as an alternative in this research. First, spring subgrade models were developed to formulate the ground boundary of concrete barriers based on previous experiments. Then, the finite element method models were developed for both heavy trucks and concrete barriers to simulate their dynamic collision performances. Comparison of the results generated from computer simulations and on-site experiments demonstrates that the developed models can be applied to simulate the collision of heavy trucks with concrete barriers, to replicate the movement of the truck at the collision, and to investigate the performance of the concrete barriers. The developed research methodology can be widely used to support the design of new concrete barriers and the safety analysis of existing ones.

Microstructure evolution modeling of square-diamond pass hot bar rolling of AISI 4135 steel

In this study, kinetics of the static (SRX) and metadynamic recrystallization (MDRX) of AISI4135 steel was investigated using hot torsion tests. Continuous torsion tests were carried out to determine the critical strain for dynamic recrystallization (DRX). The times for 50% recrystallization of SRX and MDRX were determined, respectively, by means of interrupted torsion tests. Furthermore, austenite grain size (AGS) evolution due to recrystallization (RX) was measured by optical microscopy. With the help of the evolution model established, the AGS for hot bar rolling of AISI4135 steel was predicted numerically. The predicted AGS values were compared with the results using the other model available in the literature and experimental results to verify its validity. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to investigate the effect of these parameters on AGS distributions for square-diamond pass rolling. Such numerical results were found to be beneficial in understanding the effect of processing conditions on the microstructure evolution better and control the rolling processes more accurately.