Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

The use of an electrothermal plasma gun to simulate the extremely high heat flux conditions of a tokamak disruption

Disruption damage conditions for future large tokamaks like ITER are nearly impossible to simulate on current tokamaks. The electrothermal plasma source SIRENS has been designed, constructed, and operated to produce high density (> 10²⁵/m³), low temperature (1–3 eV) plasma formed by the ablation of the insulator with currents of up to 100 kA (100 s pulse length) and energies up to 15 kJ. The source heat fluence (variable from 0.2 to 7 MJ/m²) is adequate for simulation of the thermal quench phase of plasma disruption in future fusion tokamaks. Different materials have been exposed to the high heat flux in SIRENS, where comparative erosion behavior was obtained. Vapor shield phenomena has been characterized for different materials, and the energy transmission factor through the shielding layer is obtained. The device is also equipped with a magnet capable of producing a parallel magnetic field (up to 16 T) over a 8 msec pulse length. The magnetic field is produced to decrease the turbulent energy transport through the vapor shield, which provides further reduction of surface erosion (magnetic vapor shield effect).     

Generalized hidden Markov models. II. Application to handwrittenword recognition

For part I see ibid. vol.8, no. 1 (2000). This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the constituent character hidden Markov models. The novel work presented includes the preprocessing, feature extraction, and the application of the generalized hidden Markov models to handwritten word recognition. Methods for training the classical and generalized (fuzzy) models are described. Experiments were performed on a standard data set of handwritten word images obtained from the US Post Office mail stream, which contains real-word samples of different styles and qualities     

Enhanced energetic reasoning-based lower bounds for the resource constrained project scheduling problem

We present new and effective lower bounds for the resource constrained project scheduling problem. This problem is widely known to be notoriously difficult to solve due to the lack of lower bounds that are both tight and fast. In this paper, we propose several new lower bounds that are based on the concept of energetic reasoning. A major contribution of this work is to investigate several enhanced new feasibility tests that prove useful for deriving new lower bounds that consistently outperform the classical energetic reasoning-based lower bound. In particular, we present the results of a comprehensive computational study, carried out on 1560 benchmark instances, that provides strong evidence that a deceptively simple dual feasible function-based lower bound is highly competitive with a state-of-the-art lower bound while being extremely fast. Furthermore, we found that an effective shaving procedure enables to derive an excellent lower bound that often outperforms the best bound from the literature while being significantly simpler.     

W-TSV: Weighted topological signature vector for lexicon reduction in handwritten Arabic documents

This paper proposes a holistic lexicon-reduction method for ancient and modern handwritten Arabic documents. The word shape is represented by the weighted topological signature vector (W-TSV), which encodes graph data into a low-dimensional vector space. Three directed acyclic graph (DAG) representations are proposed for Arabic word shapes, based on topological and geometrical features. Lexicon reduction is achieved by a nearest neighbors search in the W-TSV space. The proposed framework has been tested on the IFN/ENIT and the Ibn Sina databases, achieving respectively a degree of reduction of 83.5% and 92.9% for an accuracy of reduction of 90%.     

Ensemble methods for biclustering tasks

Several biclustering algorithms have been proposed in different fields of microarray data analysis. We present a new approach that improves their performance in using the ensemble methods. An ensemble biclustering is considered and formalized by a problem of binary triclustering. We propose a simple and efficient algorithm to solve it. To illustrate the interest of our ensemble approach, numerical experiments are performed on both artificial and real datasets with two biclustering algorithms commonly used in bioinformatics.     

An efficient stabilized fast Newton adaptive filtering algorithm for stereophonic acoustic echo cancellation SAEC

This paper addresses the field of stereophonic acoustic echo cancellation (SAEC) by adaptive filtering algorithms. Recently, we have proposed a new version of the fast Newton transversal FNTF algorithm for SAEC applications. In this paper, we propose an efficient modification of this algorithm for the same applications. This new algorithm uses a new proposed and simplified numerical stabilization technique and takes into account the cross-correlation between the inputs of the channels. The basic idea is to introduce a small nonlinearity into each channel that has the effect of reducing the inter-channel coherence while not being noticeable for speech due to self masking. The complexity of the proposed algorithm does not alter the complexity of the original version and is kept less than half the complexity of the fastest two-channel FTF filter version. Simulation results and comparisons with the extended two-channel normalized least mean square NLMS and FTF algorithms are presented.     

Constructing high quality use case models: a systematic review of current practices

There is an increasing recognition for the need to develop high quality use case models from the professional and academic communities. Quality in use case models is of particular importance when they are utilized within a use case driven development process, whereby every aspect of development is driven by the models and influenced by their quality. Many practitioners and researchers have provided guidelines, suggestions and techniques to construct high quality use case models. This invaluable body of knowledge is disseminated across numerous literature resources. Without unifying this knowledge into one resource, it cannot be expected that a use case modeler would be fully aware of the entire body of knowledge and benefitting from it. This paper presents a systematic review that was conducted in order to identify and amalgamate this knowledge. The amalgamated knowledge is presented in a unified form, specifically as a set of 26 anti-patterns, which modelers can use to improve the quality of their models.