Accurate Behavioral Model Research Articles

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm.

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas.

On the application of genetic algorithms and gradient methods to the problem of the reconstruction of the initial stress field in a poroelastic inhomogeneous column

Initial stress fields play an important role in deformational processes in porous structures. Accurate modeling of dynamics behavior of the porous medium taking initial stresses into account requires an appropriate mathematical model. One of the common approaches for the construction of a mathematical model of the dynamic behavior of a medium taking the initial state into account is linearization. This paper presents equations of longitudinal vibrations of a poroelastic inhomogeneous body in the presence of the initial stress field. The influence of the initial stress rate and Biot’s modulus on the dynamic behavior of the environment is analyzed. The problem of reconstructing the initial stress field for the poroelastic inhomogeneous column based on the genetic algorithm and nonlinear optimization with the use of modern packages of numerical simulation is studied. A series of numerical experiments on the reconstruction of different initial stress field distribution laws using the described methods is carried out.

Investigation of Thermal Expansion Effects on Si-Based MEMS Structures

This paper presents a study of the effects of stress and thermal expansion of inductively coupled plasma enhanced chemical vapor deposited (ICPCVD) amorphous Si thin films on low-temperature microelectromechanical systems test structures. Experimental data were used in conjunction with finite-element modeling (FEM) to predict deformation in simple microstructures across a wide temperature range from 85 to 300 K. Temperature dependence of residual stress and the coefficient of thermal expansion (CTE) of ICPCVD Si thin films was investigated by characterizing the curvature of bilayer thin-film samples through the use of optical profilometry at low temperature. Extracted parameters were used in an FEM package to confirm the experimental results by correlating with observed deformation of fabricated test structures. It is demonstrated that the experimentally determined CTE enables accurate modeling of the mechanical behavior of thin-film microstructures across a wide range of temperatures. [2015-0175]

Conquering Adversary Behavioral Uncertainty in Security Games: An Efficient Modeling Robust Based Algorithm

Stackelberg Security Games (SSG) have been widely applied for solving real-world security problems—with a significant research emphasis on modeling attackers’ behaviors to handle their bounded rationality. However, access to real-world data (used for learning an accurate behavioral model) is often limited, leading to uncertainty in attacker’s behaviors while modeling. This paper therefore focuses on addressing behavioral uncertainty in SSG with the following main contributions: 1) we present a new uncertainty game model that integrates uncertainty intervals into a behavioral model to capture behavioral uncertainty; 2) based on this game model, we propose a novel robust algorithm that approximately computes the defender’s optimal strategy in the worst-case scenario of uncertainty—with a bound guarantee on its solution quality.

Iron removal and reuse from Fenton-like pretreated olive mill wastewater with novel strong-acid cation exchange resin fixed-bed column

The performance of a fixed-bed cation exchange process was examined for final purification of the iron load derived from catalyst usage in the secondary treatment of olive mill wastewater, which led to unacceptable iron levels. Results showed an increase in the pH value up to 4 enhanced the iron ion exchange (IE) efficiency, which decreased upon higher pH. Furthermore, Thomas model provided utmost accurate dynamic behavior modeling for all inlet concentrations studied (20, 50 and 100mgL−1). Additionally, up to 100% recovery efficiencies were maintained even after 10 complete cycles. Finally, an effluent with quality for irrigation reuse was obtained.

直動転がり案内を用いたリニアモータ駆動による超精密位置決め

High precision positioning can be achieved by incorporating positioning control, if accurate modeling of frictional behavior occurring in such linear motion ball bearings is conducted. Final goal in this research is an achievement of ultra-precision positioning by building the accurate frictional model on displacement range under nano-meter level. For that reason, it is necessary to clarify the frictional behavior in minute displacement range. This paper describes that nonlinear spring behavior is shown in range of amplitude of displacement under 100μm. In addition, it indicates difference characteristics when the amplitude of displacement is continuously changed. Moreover, the frictional behavior in minute displacement is influenced by changing very small velocity compared with experimental results of sinusoidal and triangle feed.

CO2 solubility in aqueous solutions of N-methyldiethanolamine+piperazine by electrolyte NRTL model

Accurate modeling of the solubility behavior of CO2 in the aqueous alkanolamine solutions is important to design and optimization of equipment and process. In this work, the thermodynamics of CO2 in aqueous solution of N-methyldiethanolamine (MDEA) and piperazine (PZ) is studied by the electrolyte non-random two liquids (NRTL) model. The chemical equilibrium constants are calculated from the free Gibbs energy of formation, and the Henry’s constants of CO2 in MDEA and PZ are regressed to revise the value in the pure water. New experimental data from literatures are added to the regression process. Therefore, this model should provide a comprehensive thermodynamic representation for the quaternary system with broader ranges and more accurate predictions than previous work. Model results are compared to the experimental vapor-liquid equilibrium (VLE), speciation and heat of absorption data, which show that the model can predict the experimental data with reasonable accuracy.

Polymer based aerospace structures under high velocity impact applications; experimental, constitutive and finite element analysis

Polymethyl methacrylate (PMMA) and Polycarbonate (PC) are two famous optically clear thermoplastic polymers widely used in various aeronautical and astronatucial applications. Owing to their exceptional transparency, process shape adaptability, low density and high impact strength they find particular attention in aerospace industry where they are used to manufacture air craft canopies, windshields, impact resistant windows, visors and safety glasses. The design and manufacturing of the satisfied polymeric structures involve accurate modeling of the material behavior and detailed analysis of their service behavior under extreme loading conditions. In order to evaluate the material behavior under these loading conditions, extensive work in terms of experimental characterization and material constitutive modeling is required. A systematic research methodology including the material characterization, material constitutive modeling, constitutive model implementing and finite element prediction of mechanical behaviors is proposed in this work. The material characterization tests were performed by means of universal testing machine and Split Hopkinson pressure bar (SHPB) setup. On the basis of performed tests and using phenomenological approach, a constitutive model was proposed to successfully predict the entire deformation behavior of polymers under various loading conditions with sufficient accuracy. The model was implemented numerically by establishing a User-defined material subroutine (UMAT) in explicit Finite element (FE) solver LS-DYNA. The model successfully ascertained the dynamic behavior of PMMA based aircraft windshield and PC based astronaut helmet visor under application of high velocity projectile impact. A number of FE simulations were carried out to determine the critical impact energy, maximum deformation and stress level in these structures. The simulations results help to evaluate various design approaches to optimize the structural response prior to preliminary experimentations and therefore provide an alternative to costly and time consuming extensive experimental tests.

Application of 2D finite element model for nonlinear dynamic analysis of clamp band joint

Due to frictional slippage between the joint components, clamp band joints may generate nonlinear stiffness and friction damping, which will affect the dynamics of the joint structures. Accurate modeling of the frictional behavior in clamp band joints is crucial for reliable estimation of the joint structure dynamics. While the finite element (FE) method is a powerful tool to analyze structures assembled with joints, it is computationally expensive and inefficient to perform transient analyses with three-dimensional (3D) FE models involving contact nonlinearity. In this paper, a two-dimensional (2D) FE model of much more efficiency is applied to investigate the dynamics of a clamp band jointed structure subjected to longitudinal base excitations. Prior to dynamic analyses, the sources of the model inaccuracy are determined, upon which a two-step model updating technique is proposed to improve the accuracy of the 2D model in accordance with the quasi-static test data. Then, based on the updated 2D model, the nonlinear influence of the clamp band joint on the dynamic response of the joint structure is investigated. Sine-sweep tests are carried out to validate the updated 2D FE model. The FE modeling and updating techniques proposed here can be applied to other types of structures of cyclic symmetry to develop accurate model with high computational efficiency.

IEEE 802.11p VANets: Experimental evaluation of packet inter-reception time

Periodic exchange of situational information (beacons) is at the basis of most active safety applications in vehicular environments, and understanding its performance in real-world situations is very important. However, existing studies are focused on measuring the packet delivery rate (PDR), while disregarding the packet inter-reception (PIR) time which has recently been shown to be more relevant than PDR to characterize active safety application performance. In this paper, we fill this gap by presenting an extensive study of the PIR times observed in real-world highway scenarios.We start by showing that PIR cannot be reliably estimated from PDR, since the two metrics are only weakly correlated. Motivated by this finding, we present a thorough characterization of the PIR time distribution, which is shown to be a power law in a variety of configurations. The shape of the PIR time distribution indicates that potentially dangerous “situational awareness” blackouts are relatively frequent and positively time correlated. We then evaluate the effect of vehicle configuration and line-of-sight conditions on the PIR time, and show that relatively simple multi-hop beaconing techniques can substantially improve PIR statistics and, hence, safety. A final contribution of this paper is promoting the Gilbert–Elliot model, previously proposed to model bit-error bursts in packet switched networks, as a very accurate model of beacon reception behavior observed in real-world scenarios.

Distributed inelasticity planar frame element with localized semi-rigid connections for nonlinear analysis of steel structures

A macro-element model with spread of inelasticity and localized nonlinear semi-rigid hinges is presented in this paper. The element is formulated through force-based approach. Spread of inelasticity along element length and section depth is captured by fiber discretization of monitoring sections, and introduction of localized connections enable representation of any type of linear or nonlinear semi-rigid (partially restrained) connection behavior along element length without any increase in number of degrees of freedom. The element is used in conjunction with corotational formulation for the capture of nonlinear geometric effects. The proposed element is used for the analysis of steel structures considering the presence of beam to column semi-rigid connections in this paper. For a true representation of the hysteretic energy dissipation characteristic of a beam to column connection a quadra-linear moment–rotation based model is implemented incorporating pinching, damage, the possibility of residual moment capacity and degradation of stiffness under various scenarios. The behavior is calibrated via experimental data available in the literature. Robustness and accuracy of the proposed element and hysteretic connection model are assessed through numerical and experimental data, and the importance of accurate modeling of cyclic behavior of semi-rigid connections is demonstrated through analysis of a steel moment resisting frame.

Comparison of Cubic-Plus-Association and Perturbed-Chain Statistical Associating Fluid Theory Methods for Modeling Asphaltene Phase Behavior and Pressure–Volume–Temperature Properties

The tendency of asphaltene, the heaviest and most polarizable component of crude oil, to deposit and block wellbores and pipelines can potentially lead to production loss and significant cost of remediation. Asphaltenes constitute a particular interest in oil production because of the lack of understanding of their molecular structure and the mechanisms by which they precipitate and deposit. Because prevention is far less expensive than removal, a better understanding of asphaltene precipitation and deposition phenomena is of great importance for the oil industry. Precipitation, which is a necessary but not sufficient condition for deposition, requires accurate modeling of asphaltene phase behavior with respect to variations in the temperature, pressure, and composition. Over the past decade, cubic-plus-association (CPA) and perturbed-chain statistical associating fluid theory (PC-SAFT) equations of state (EOSs) have been proposed for modeling complex systems, such as asphaltenic crude oils. In this work, a comparison between CPA and PC-SAFT EOSs is presented to illustrate their potential and limitations on the prediction of asphaltene phase behavior and pressure–volume–temperature (PVT) properties of crude oils over a range of pressures and temperatures. With an optimized characterization, both EOSs are able to give acceptable predictions of the phase behavior and asphaltene precipitation tendency. However, PC-SAFT is superior in the prediction of derivative thermodynamic properties, especially at high pressures.

Accurate Modeling of Quality Factor Behavior of Complex Silicon MEMS Resonators

The quality factor of a resonator represents the decay of vibrational energy over time, and is directly related to the frequency response and other key parameters that determine performance of inertial sensors and oscillators. Accurate prediction of the quality factor is essential for designing high-performance microelectromechanical (MEMS) devices. Several energy dissipation mechanisms contribute to the quality factor. Due to computational complexity, highly simplified models for the dominant dissipation mechanism, such as Zener’s model for thermoelastic dissipation (TED), are often employed. However, the intuition provided by these models is inadequate to predict the quality factor of more complex designs and can be highly misleading. In this paper, we construct complete, quantitative, and predictive models with finite-element methods for the intrinsic energy dissipation mechanisms in MEMS resonators using full anisotropic representation of crystalline silicon and the temperature dependence of all parameters. We find that TED is often a more significant source of damping than has been assumed, because of the previously neglected role of crystalline anisotropy and small geometric features, such as etch release holes—all of which can now be included in practical models. We show that these models, along with simpler scaling models for extrinsic dissipation mechanisms, explain measurements of quality factor in diverse sets of MEMS resonators with unprecedented accuracy. [2014-0106]

Verilog-A behavioral model for resonance-modulated silicon micro-ring modulator.

We present an accurate behavior model for Si micro-ring modulators (MRM) based on Verilog-A, a standard simulation tool for electronic system design. Our model describes the electrical characteristics of the Si MRM using an equivalent circuit and the optical characteristics based on the couple-mode theory. The accuracy of our model is confirmed by comparing simulation results of our behavior model with the measurement results of a fabricated Si MRM. With this behavior model, co-simulation of Si MRM and electronic driving circuits in the standard electronic design environment can be easily performed.

Bias-dependence of luminescent coupling efficiency in multijunction solar cells.

In this work, we demonstrate an improved method to simulate the characteristics of multijunction solar cell by introducing a bias-dependent luminescent coupling efficiency. The standard two-diode equivalent-circuit model with constant luminescent coupling efficiency has limited accuracy because it does not include the recombination current from photogenerated carriers. Therefore, we propose an alternative analytical method with bias-dependent luminescent coupling efficiency to model multijunction cell behavior. We show that there is a noticeable difference in the J-V characteristics and cell performance generated by simulations with a constant vs. bias-dependent coupling efficiency. The results indicate that introducing a bias-dependent coupling efficiency produces more accurate modeling of multijunction cell behavior under real operating conditions.

Memory models for behavioral modeling and digital predistortion of envelope tracking power amplifiers

New advanced Envelope Tracking (ET) techniques can provide RF (Radio Frequency) transmitters with high-efficiency Power Amplifiers (PAs). On the other hand, system complexity substantially increases, requiring more advanced PA models for the representation and compensation of ET PA distortion effects. In this context, this paper proposes some solutions for behavioral modeling and digital predistortion of ET PAs. The adopted modeling strategy consists in including the modulated supply voltage as an additional independent model variable to define more accurate behavioral models capable of an increased accuracy when applied to model and compensate ET PAs. The new model variable is included in a polynomial model with memory whose nonlinear structure is derived from a binomial power series, whence the name of Memory Binomial Model (MBM). Another modeling approach is subsequently proposed, where the Cann model for static PA AM/AM nonlinearities is extended to model both AM/AM and AM/PM dynamic distortion occurring in ET PAs. The Extended Cann model includes an MBM structure for modeling dynamic AM/PM distortion effects. Both modeling approaches are tested on measured data-sets acquired using an ET measurement set-up including a commercial PA from RFMD and an envelope modulator designed using a commercial IC from Texas Instruments. The measured results showed that the proposed models could obtain a better modeling and predistortion performance when applied to ET PAs, with respect to the Memory Polynomial Model, here considered as a reference to represent the state-of-the-art of PA modeling and digital predistortion.

Phenomenological modeling of memristive devices

We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model predicts most accurately I-V relation at small non-disturbing electrical stresses, which is often the most critical range of operation for circuit modeling. While the choice of fitting functions is motivated by the switching and conduction mechanisms of particular titanium dioxide devices, the proposed modeling methodology is general enough to be applied to different types of memory devices which feature smooth non-abrupt resistance switching.

Behavioral Model of Patterned Vertical Alignment Pixel in Active-Matrix Liquid Crystal Displays

This paper proposes a more accurate behavioral circuit model to predict transient responses of an active-matrix liquid crystal display (AMLCD) with a patterned vertical alignment (PVA) mode. In the case of the PVA mode, we observed that simulation errors increase when the conventional circuit model is directly applied to overdriven static and dynamic data transitions. To overcome this issue, we investigate the actual behavior of the liquid crystal (LC) molecules in the PVA AMLCD pixel using a high-speed camera attached to a microscope. We discovered that the behavior of the LC molecules is significantly dependent on their proximity to the edges of transparent electrodes. Based on this investigation, we propose a first-order circuit model that has two LC groups with two different response characteristics. The proposed model is embedded in a circuit simulator using an analog hardware description language. Simulation results show that the transient optical responses are considerably more accurate than the conventional responses for any data transitions, including overdriven transitions.

Incorporation of Cross-Terms in Memory Polynomial Using Evolutionary Algorithm for Accurate Behavioral Modeling of RF Power Amplifiers

Incorporation of Cross-Terms in Memory Polynomial Using Evolutionary Algorithm for Accurate Behavioral Modeling of RF Power Amplifiers

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people’s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton’s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models.