Multidimensional ARKN methods for general oscillatory second-order initial value problems

Based on B-series theory, the order conditions of the multidimensional ARKN methods are presented for the general multi-frequency and multidimensional oscillatory second-order initial value problems by Wu et al. (2009). These multidimensional ARKN methods exactly integrate the multi-frequency and multidimensional unperturbed oscillators. In this paper, we pay attention to the analysis of the concrete multidimensional ARKN methods for the general multi-frequency oscillatory second-order initial value problems whose right-hand side functions depend on both  y$y$and  y^′$y^{\prime }$ (the class of physical problems which fall within its scope is broader). Numerical experiments are carried out to show that the new multidimensional ARKN methods are more efficient compared with some well-known methods for dealing with the oscillatory problems in the scientific literature.     

A conservative discontinuous Galerkin scheme for the 2D incompressible Navier–Stokes equations

In this paper we consider a conservative discretization of the two-dimensional incompressible Navier–Stokes equations. We propose an extension of Arakawa’s classical finite difference scheme for fluid flow in the vorticity–stream function formulation to a high order discontinuous Galerkin approximation. In addition, we show numerical simulations that demonstrate the accuracy of the scheme and verify the conservation properties, which are essential for long time integration. Furthermore, we discuss the massively parallel implementation on graphic processing units.     

Individual empowerment of agile and non-agile software developers in small teams

ContextEmpowerment of employees at work has been known to have a positive impact on job motivation and satisfaction. Software development is a field of knowledge work wherein one should also expect to see these effects, and the idea of empowerment has become particularly visible in agile methodologies, in which proponents emphasise team empowerment and individual control of the work activities as a central concern.ObjectiveThis research aims to get a better understanding of how empowerment is enabled in software development teams, both agile and non-agile, to identify differences in empowering practices and levels of individual empowerment.MethodTwenty-five interviews with agile and non-agile developers from Norway and Canada on decision making and empowerment are analysed. The analysis is conducted using a conceptual model with categories for involvement, structural empowerment and psychological empowerment.ResultsBoth kinds of development organisations are highly empowered and they are similar in most aspects relating to empowerment. However, there is a distinction in the sense that agile developers have more possibilities to select work tasks and influence the priorities in a development project due to team empowerment. Agile developers seem to put a higher emphasis on the value of information in decision making, and have more prescribed activities to enable low-cost information flow. More power is obtained through the achievement of managing roles for the non-agile developers who show interest and are rich in initiatives.ConclusionAgile developers have a higher sense of being able to impact the organisation than non-agile developers and have information channels that is significantly differently from non-agile developers. For non-agile teams, higher empowerment can be obtained by systematically applying low-cost participative decision making practices in the manager–developer relation and among peer developers. For agile teams, it is essential to more rigorously follow the empowering practices already established.     

Efficient data selection for speech recognition based on prior confidence estimation using speech and monophone models

This paper proposes an efficient speech data selection technique that can identify those data that will be well recognized. Conventional confidence measure techniques can also identify well-recognized speech data. However, those techniques require a lot of computation time for speech recognition processing to estimate confidence scores. Speech data with low confidence should not go through the time-consuming recognition process since they will yield erroneous spoken documents that will eventually be rejected. The proposed technique can select the speech data that will be acceptable for speech recognition applications. It rapidly selects speech data with high prior confidence based on acoustic likelihood values and using only speech and monophone models. Experiments show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while providing equivalent data selection performance for speech recognition and spoken document retrieval.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

Text classification using genetic algorithm oriented latent semantic features

In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.     

Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote

This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods.