A continuum theory for two phase media

Summary A continuum theory of a two phase solid-fluid media is formulated. The basic balance laws for the solid phase as well as for the fluid phase are presented. Based on thermodynamical consideration a set of constitutive equations are derived and the basic equations of motions of the distributed solid and fluid continua are obtained and discussed. It is shown that the theory contains as its special cases, Mohr-Coulomb criterion of limiting equilibrium of granular materials, Saffman theory of dusty gas, as well as Darcy's law of flow through porous media. It is then concluded that the present theory covers the full spectrum of two phase solid-fluid media from low porosity granular media with Darcy's law of fluid motion to low and high concentration two phase flows such as dusty gas and blood flow.     

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

In this paper a design approach for two-dimensional finite impulse and infinite impulse response digital filters with canonic signed digit (CSD) coefficients based on singular value decomposition and genetic algorithms (GAs) is presented. The proposed technique uses a new chromosome coding scheme to alleviate the problem encountered by many GAs during crossover and mutation which destroys the CSD property. Examples are given to demonstrate the usefulness of the proposed technique. A comparative study carried out with some of the existing techniques indicates the high throughput property of the proposed technique.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

Task-space control of robots using an adaptive Taylor series uncertainty estimator

An uncertainty estimation and compensation can improve the performance of control systems due to structured and unstructured uncertainty. This paper presents a robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators. It is worth noting that not only the lumped uncertainty is estimated and employed in the indirect form of robust controller, but also the upper bound of approximation error is estimated to form a robustifying term and the asymptotic convergence of tracking error and its time derivative are proven based on stability analysis. Finally, the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm (SCARA) robot manipulator.     

Estimation of water-hydrocarbon mutual solubility in gas processing operations using an intelligent model

An accurate prediction of the mutual solubilities of hydrocarbons and water is extremely useful in oil, gas, and chemical industries. Estimating the solubility of hydrocarbons in water is required to describe their phase distribution through the removal process and also in the design of separation equipment. The current study plays emphasis on applying the predictive model based on the least square support vector machine (LSSVM) to estimate mutual water-hydrocarbon solubility at a wide range of conditions. A genetic algorithm (GA) was employed to choose and optimize hyperparameters (γ and σ²), which are embedded in LSSVM model. Utilization of this model showed high competence of the applied model in terms of coefficient of determination (R²) of 0.9998 and 0.9994, Average absolute relative deviation (AARD) of 1.1378 and 1.12459 from experimental values for predicted water solubility in hydrocarbons and hydrocarbon solubility in water, correspondingly. Using this method is quite simple and accurate to determine the mutual water-hydrocarbon solubility with negligible uncertainty.     

Dispersion and Deposition of Spherical Particles from Point Sources in a Turbulent Channel Flow

The dispersion and deposition of particles from a point source in a turbulent channel flow are studied. An empirical mean velocity profile and the experimental data for turbulent intensities are used in the analysis. The instantaneous turbulence fluctuation is simulated as a continuous Gaussian random field, and an ensemble of particle trajectories is generated and statistically analyzed. A series of digital simulations for dispersion and deposition of aerosol particles of various sizes from point sources at different positions from the wall is performed. Effects of Brownian diffusion on particle dispersion are studied. The effects of variation in particle density and particle-surface interaction are also discussed.     

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.     

Mixed integer programming of generalized hydro-thermal self-scheduling of generating units

This paper presents the application of mixed-integer programming (MIP) approach for solving the hydro-thermal self scheduling (HTSS) problem of generating units. In the deregulated environment, the generation companies schedule their generators to maximize their profit while satisfying loads is not an obligation. The HTSS is a high dimensional mixed-integer optimization problem. Therefore, in the large-scale power systems, solving the HTSS is very difficult. In this paper, MIP formulation is adopted for precise modeling of dynamic ramp rate limits, prohibited operating zones, operating services, valve loading effects, variable fuel cost, non-linear start-up cost functions of thermal units, fuel and emission limits of thermal units, multi head power-discharge characteristics of hydro plants and spillage of reservoir. The modified IEEE 118-bus system is used to demonstrate the performance of the proposed method.