Performance–energy adaptation of parallel programs in pervasive computing

It is meaningful to use a little energy to obtain more performance improvement compared with the increased energy. It also makes sense to relax a small quantity of performance restriction to save an enormous amount of energy. Trading a small amount of energy for a considerable sum of performance or vice versa is possible if the relativities between performance and energy of parallel programs are exactly known. This work studies the relativities by recording the performance speedup and energy consumption of parallel programs when the number of cores on which programs run are changed. We demonstrate that the performance improvement and the increased energy consumption have a linear negative correlation.In addition, these relativities can guide us to do performance–energy adaptation under two assumptions. Our experiments show that the average correlation coefficients between performance and energy are higher than 97 %. Furthermore, it can be found that exchanging less than 6 % performance loss for more than 37 % energy consumption is feasible and vise versa.     

Assessment of composite beam performance using GWO–ELM metaheuristic algorithm

Composite beams (CBs) include concrete slabs jointed to the steel parts by the shear connectors, which highly popular in modern structures such as high rise buildings and bridges. This study has investigated the structural behavior of simply supported CBs in which a concrete slab is jointed to a steel beam by headed stud shear connector. Determining the behavior of CB through empirical study except its costly process can also lead to inaccurate results. In this case, AI models as metaheuristic algorithms could be effectively used for solving difficult optimization problems, such as Genetic algorithm, Differential evolution, Firefly algorithm, Cuckoo search algorithm, etc. This research has used hybrid Extreme machine learning (ELM)–Grey wolf optimizer (GWO) to determine the general behavior of CB. Two models (ELM and GWO) and a hybrid algorithm (GWO–ELM) were developed and the results were compared through the regression parameters of determination coefficient (R²) and root mean square (RMSE). In testing phase, GWO with the RMSE value of 2.5057 and R² value of 1.2510, ELM with the RMSE value of 4.52 and R² value of 1.927, and GWO–ELM with the RMSE value of 0.9340 and R² value of 0.9504 have demonstrated that the hybrid of GWO–ELM could indicate better performance compared to solo ELM and GWO models. In this case, GWO–ELM could determine the general behavior of CB faster, more accurate and with the least error percentages, so the hybrid of GWO–ELM is more reliable model than ELM and GWO in this study.

     

Interactive scenarios—building ubiquitous computing concepts in the spirit of participatory design

The amount of information technology in our everyday lives is increasing and getting more and more ambient in our daily environments. The environments are supposed to be intelligent, adaptive, intuitive and interactive in the future. User participation for future concept building is essential, but challenging, when designing appliances that might be unfamiliar in their appearance, functionality and impressiveness compared to the users current everyday life. New allocated methods and viewpoints are needed for user experience design and evaluation of intelligent environments to build systems that naturally support the users in their daily life. We present interactive scenario building together with potential users (including role-playing, drama and improvisational aspects) as one promising tool for early concept definition phase.     

Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models.

     

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.     

Methods for computing Nash equilibria of a location–quantity game

A two-stage model is described where firms take decisions on where to locate their facility and on how much to supply to which market. In such models in literature, typically the market price reacts linearly on supply. Often two competing suppliers are assumed or several that are homogeneous, i.e., their cost structure is assumed to be identical. The focus of this paper is on developing methods to compute equilibria of the model where more than two suppliers are competing that each have their own cost structure, i.e., they are heterogeneous. Analytical results are presented with respect to optimality conditions for the Nash equilibria in the two stages. Based on these analytical results, an enumeration algorithm and a local search algorithm are developed to find equilibria. Numerical cases are used to illustrate the results and the viability of the algorithms. The methods find an improvement of a result reported in literature.     

The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli–Turkey

Turkey does not have petrol and natural gas reserves on a large scale. National energy resources are lignite and hydropower. Together with increasing environmental problems and diminishing fossil resources, studies focusing on energy reduction as well as usage of renewable energy resources have accelerated. However, taking the technological and economical impossibilities into account, the most logical solution is energy saving by providing energy efficiency in households. In this study, an artificial neural network (ANN) model is developed in order to predict hourly heating energy consumption of a model house designed in Denizli which is located in Central Aegean Region of Turkey. Hourly heating energy consumption of the model house is calculated by degree-hour method. ANN model is trained with heating energy consumption values of years 2004–2007 and tested with heating energy consumption values of year 2008. The training and test figures were depicted for February month of these years. Best estimate is found with 29 neurons and a good coherence is observed between calculated and predicted values. According to the results obtained, root-mean-squared error (RMSE), absolute fraction (R²) and mean absolute percentage error (MAPE) values are 1.2575, 0.9907, and 0.2091 for training phase and 1.2125, 0.9880, and 0.2081 for testing phase respectively.     

Intelligent computing for numerical treatment of nonlinear prey–predator models

In this study, a new computing paradigm is presented for evaluation of dynamics of nonlinear prey–predator mathematical model by exploiting the strengths of integrated intelligent mechanism through artificial neural networks, genetic algorithms and interior-point algorithm. In the scheme, artificial neural network based differential equation models of the system are constructed and optimization of the networks is performed with effective global search ability of genetic algorithm and its hybridization with interior-point algorithm for rapid local search. The proposed technique is applied to variants of nonlinear prey–predator models by taking different rating factors and comparison with Adams numerical solver certify the correctness for each scenario. The statistical studies have been conducted to authenticate the accuracy and convergence of the design methodology in terms of mean absolute error, root mean squared error and Nash-Sutcliffe efficiency performance indices.     

Optimizations in computing the Duquenne–Guigues basis of implications

In this paper, we consider algorithms involved in the computation of the Duquenne–Guigues basis of implications. The most widely used algorithm for constructing the basis is Ganter’s Next Closure, designed for generating closed sets of an arbitrary closure system. We show that, for the purpose of generating the basis, the algorithm can be optimized. We compare the performance of the original algorithm and its optimized version in a series of experiments using artificially generated and real-life datasets. An important computationally expensive subroutine of the algorithm generates the closure of an attribute set with respect to a set of implications. We compare the performance of three algorithms for this task on their own, as well as in conjunction with each of the two algorithms for generating the basis. We also discuss other approaches to constructing the Duquenne–Guigues basis.     

Effective scheme for generation of N-dimension atomic Greenberger–Horne–Zeilinger states

In this paper, we propose an effective scheme for generation of $N$ -dimension atomic Greenberger–Horne–Zeilinger states with the controlled phase flip gates. The successful probability of our scheme is 100 % in principle. The scheme is implemented with simple linear optical elements, delay lines and polarization-independent circulators. We discuss the feasibility of the setups, concluding that the scheme is feasible with current technology.     

Convergence of an implicit–explicit midpoint scheme for computational micromagnetics

Based on lowest-order finite elements in space, we consider the numerical integration of the Landau–Lifschitz–Gilbert equation (LLG). The dynamics of LLG is driven by the so-called effective field which usually consists of the exchange field, the external field, and lower-order contributions such as the stray field. The latter requires the solution of an additional partial differential equation in full space. Following Bartels and Prohl (2006), we employ the implicit midpoint rule to treat the exchange field. However, in order to treat the lower-order terms effectively, we combine the midpoint rule with an explicit Adams–Bashforth scheme. The resulting integrator is formally of second-order in time, and we prove unconditional convergence towards a weak solution of LLG. Numerical experiments underpin the theoretical findings.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

A novel optimized approach for resource reservation in cloud computing using producer–consumer theory of microeconomics

The Journal of Supercomputing - Designing economic pricing mechanisms have recently attracted a great deal of attention in the context of cloud computing. We believe that microeconomics theory is a...     

Human–computer interaction issues for mobile computing in a variable work context

The current paper takes an introspective look at the human–computer interaction (HCI) issues for mobile computing in a variable work context. We catalogue the current research in four major categories. The major findings of our study are following. (1) A majority of HCI issues, about 58%, fall under the category of computer systems and interface architecture implications. (2) 23% of the articles focus on development and implementation issues. (3) 13% of the articles focus on use and context of computer issues. (4) 6% of the articles focus on human characteristics issues. Further, the literature indicates that the field services is a main application of mobile computing (46%) followed by sales force (21%), health care (17%), fieldwork (8%), insurance claims (4%) and journalism (4%).     

Sequential selection of discrete features for neural networks – A Bayesian approach to building a cascade

A feature selection procedure is used to successively remove features one-by-one from a statistical classifier by an iterative backward search. Each classifier uses a smaller subset of features than the classifier in the previous iteration. The classifiers are subsequently combined into a cascade. Each classifier in the cascade should classify cases to which a reliable class label can be assigned. Other cases should be propagated to the next classifier which uses also the value of a new feature. Experiments demonstrate the feasibility of building cascades of classifiers (neural networks for prediction of atrial fibrillation (FA)) using a backward search scheme for feature selection.     

Application of hierarchical matrices for computing the Karhunen–Loève expansion

Realistic mathematical models of physical processes contain uncertainties. These models are often described by stochastic differential equations (SDEs) or stochastic partial differential equations (SPDEs) with multiplicative noise. The uncertainties in the right-hand side or the coefficients are represented as random fields. To solve a given SPDE numerically one has to discretise the deterministic operator as well as the stochastic fields. The total dimension of the SPDE is the product of the dimensions of the deterministic part and the stochastic part. To approximate random fields with as few random variables as possible, but still retaining the essential information, the Karhunen–Loève expansion (KLE) becomes important. The KLE of a random field requires the solution of a large eigenvalue problem. Usually it is solved by a Krylov subspace method with a sparse matrix approximation. We demonstrate the use of sparse hierarchical matrix techniques for this. A log-linear computational cost of the matrix-vector product and a log-linear storage requirement yield an efficient and fast discretisation of the random fields presented.     

A nonstandard numerical scheme of predictor–corrector type for epidemic models

In this paper we construct and develop a competitive nonstandard finite difference numerical scheme of predictor–corrector type for the classical SIR epidemic model. This proposed scheme is designed with the aim of obtaining dynamical consistency between the discrete solution and the solution of the continuous model. The nonstandard finite difference scheme with Conservation Law (NSFDCL) developed here satisfies some important properties associated with the considered SIR epidemic model, such as positivity, boundedness, monotonicity, stability and conservation of frequency of the oscillations. Numerical comparisons between the NSFDCL numerical scheme developed here and Runge–Kutta type schemes show its effectiveness.     

An interval extension of SMS method for computing weighted Moore–Penrose inverse

An interval extension of successive matrix squaring (SMS) method for computing the weighted Moore–Penrose inverse \(A^{\dagger }_{MN}\) along with its rigorous error bounds is proposed for given full rank \(m \times n\) complex matrices A, where M and N be two Hermitian positive definite matrices of orders m and n, respectively. Starting with a suitably chosen complex interval matrix containing \(A^{\dagger }_{MN}\), this method generates a sequence of complex interval matrices each enclosing \(A^{\dagger }_{MN}\) and converging to it. A new method is developed for constructing initial complex interval matrix containing \(A^{\dagger }_{MN}\). Convergence theorems are established. The R-order convergence is shown to be equal to at least l, where \(l \ge 2\). A number of numerical examples are worked out to demonstrate its efficiency and effectiveness. Graphs are plotted to show variations of the number of iterations and computational times compared to matrix dimensions. It is observed that ISMS is more stable compared to SMS.     

Parameterized energy–latency trade-offs for data propagation in sensor networks

We study the problem of greedy, single path data propagation in wireless sensor networks, aiming mainly to minimize the energy dissipation. In particular, we first mathematically analyze and experimentally evaluate the energy efficiency and latency of three characteristic protocols, each one selecting the next hop node with respect to a different criterion (minimum projection, minimum angle and minimum distance to the destination). Our analytic and simulation findings suggest that any single criterion does not simultaneously satisfy both energy efficiency and low latency. Towards parameterized energy–latency trade-offs we provide as well hybrid combinations of the two criteria (direction and proximity to the sink). Our hybrid protocols achieve significant performance gains and allow fine-tuning of desired performance. Also, they have nice energy balance properties, and can prolong the network lifetime.