In this study, a new normalised adaline-based control algorithm is proposed to control voltage source converter (VSC) used for voltage control and harmonics mitigation of two-winding asymmetric single-phase self excit...
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In this study, a new normalised adaline-based control algorithm is proposed to control voltage source converter (VSC) used for voltage control and harmonics mitigation of two-winding asymmetric single-phase self excited induction generator (SEIG) feeding fluctuating linear and non-linear loads. The proposed control algorithm is implemented on a digital signal processor (DSP). In the proposed SEIG system, a two-winding asymmetric single-phase SEIG is driven by a speed governor controlled diesel/biogas engine. The VSC is used for adjustable fundamental reactive current compensation of a two-winding single-phase SEIG for voltage control, and harmonics elimination of variety of loads. An insulated gate bipolar transistor-based two-leg VSC is connected at the point of common coupling. The proposed new control algorithm has many distinct advantages such as fast response which is highly desirable for fluctuating loads and fast convergence at all frequency components. The mathematical formulation of the proposed control algorithm is given and recorded test results of the SEIG system are presented to demonstrate the voltage control, harmonics mitigation and fast response of the algorithm. The proposed controller of single-phase SEIG system is designed, developed and implemented on a DSP to validate the claims. A MATLAB simulation model of SEIG system with proposed controller is also developed and simulated results are presented along with the test results to validate the model developed.
Motivation: New sequencing technologies generate larger amount of short reads data at decreasing cost. De novo sequence assembly is the problem of combining these reads back to the original genome sequence, without re...
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Motivation: New sequencing technologies generate larger amount of short reads data at decreasing cost. De novo sequence assembly is the problem of combining these reads back to the original genome sequence, without relying on a reference genome. This presents algorithmic and computational challenges, especially for long and repetitive genome sequences. Most existing approaches to the assembly problem operate in the framework of de Bruijn graphs. Yet, a number of recent works use the paradigm of string graph, using a variety of methods for storing and processing suffixes and prefixes, like suffix arrays, the Burrows-Wheeler transform or the FM index. Our work is motivated by a search for new approaches to constructing the string graph, using alternative yet simple data structures and algorithmic concepts. Results: We introduce a novel hash-based method for constructing the string graph. We use incremental hashing, and specifically a modification of the Karp-Rabin fingerprint, and Bloom filters. Using these probabilistic methods might create false-positive and false-negative edges during the algorithm's execution, but these are all detected and corrected. The advantages of the proposed approach over existing methods are its simplicity and the incorporation of established probabilistic techniques in the context of de novo genome sequencing. Our preliminary implementation is favorably comparable with the first string graph construction of Simpson and Durbin (2010) (but not with subsequent improvements). Further research and optimizations will hopefully enable the algorithm to be incorporated, with noticeable performance improvement, in state-of-the-art string graph-based assemblers.
To focus synthetic aperture radar data suffering from phase errors, a novel autofocus algorithm is presented. The algorithm uses the coordinate descent technique to estimate the phase errors for maximum sharpness auto...
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To focus synthetic aperture radar data suffering from phase errors, a novel autofocus algorithm is presented. The algorithm uses the coordinate descent technique to estimate the phase errors for maximum sharpness autofocus, where analytical-form expressions are easy to find for the optimal estimates on a per-parameter basis. Experimental results show the validity of the proposed algorithm.
Wind-blown sand movement often occurs in a very complicated desert environment where sand dunes and ripples are the basic forms. However, most current studies on the theoretic and numerical models of wind-blown sand m...
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Wind-blown sand movement often occurs in a very complicated desert environment where sand dunes and ripples are the basic forms. However, most current studies on the theoretic and numerical models of wind-blown sand movement only consider ideal conditions such as steady wind velocity, flat sand surface, etc. In fact, the windward slope gradient plays a great role in the lift-off and sand particle saltation. In this paper, we propose a numerical model for the coupling effect between wind flow and saltating sand particles to simulate wind-blown sand movement over the slope surface and use the SIMPLE algorithm to calculate wind flow and simulate sands transport by tracking sand particle trajectories. We furthermore compare the result of numerical simulation with wind tunnel experiments. These results prove that sand particles have obvious effect on wind flow, especially that over the leeward slope. This study is a preliminary study on windblown sand movement in a complex terrain, and is of significance in the control of dust storms and land desertification.
A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is present...
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A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is presented as a generic method for producing codes for the automatic translation of input models from the format of one code version to another, or even to that of a completely different code. Normally, such translations are done manually. However, input model files, such as for the VSOP codes, often are very large and may consist of many thousands of numeric entries that make no particular sense to the human eye. Therefore the task, of for instance nuclear regulators, to verify the accuracy of such translated files can be very difficult and cumbersome. This may cause translation errors not to be picked up, which may have disastrous consequences later on when a reactor with such a faulty design is built. Therefore a generic algorithm for producing such automatic translation codes may ease the translation and verification process to a great extent. It will also remove human error from the process, which may significantly enhance the accuracy and reliability of the process. The developed algorithm also automatically creates a verification log file which permanently record the names and values of each variable used, as well as the list of meanings of all the possible values. This should greatly facilitate reactor licensing applications. (C) 2013 Elsevier B.V. All rights reserved.
Our objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin. Demographic, clinical, and genetic data were gathered from a previously collected cohort of p...
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Our objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin. Demographic, clinical, and genetic data were gathered from a previously collected cohort of patients with a stable warfarin dosage who were able to achieve an observed international normalized ratio of 2-3. Data from a cohort of 3,415 patients were used to develop an ANN dosing algorithm. Data from another cohort of 856 were used to validate the algorithm. The clinical significance of the ANN dosing algorithm was evaluated by calculating the percentage of patients whose predicted dosage of warfarin was within 20 % of the actual stable therapeutic dose. The clinical significance was also compared with a previously published dosing algorithm. A feed-forward neural network with three layers was able to successfully predict the ideal warfarin dosage in 48 % of the patients. The neural network model explained 48 % and 43 % of the dosage variability observed among patients in the derivation and validation cohorts, respectively. ANN analysis identified several predictors of warfarin dosage including race;age;height;weight;cytochrome P450 (CYP)2C9 genotype;VKORC1 genotype;sulfonamide, azole antifungals, or macrolide administration;carbamazepine, phenytoin, or rifampicin administration;and amiodarone administration. An ANN was applied to develop a warfarin dosing algorithm. The proposed dosing algorithm has the potential to recommend warfarin dosages that are close to the appropriate dosages.
Matrix completion that estimates missing values in visual data is an important topic in computer vision. Most of the recent studies focused on the low rank matrix approximation via the nuclear norm. However, the visua...
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Matrix completion that estimates missing values in visual data is an important topic in computer vision. Most of the recent studies focused on the low rank matrix approximation via the nuclear norm. However, the visual data, such as images, is rich in texture which may not be well approximated by low rank constraint. In this paper, we propose a novel matrix completion method, which combines the nuclear norm with the local geometric regularizer to solve the problem of matrix completion for redundant texture images. And in this paper we mainly consider one of the most commonly graph regularized parameters: the total variation norm which is a widely used measure for enforcing intensity continuity and recovering a piecewise smooth image. The experimental results show that the encouraging results can be obtained by the proposed method on real texture images compared to the stateof-the-art methods.
The paper presents IRSN's results of the OECD/NEA WPEC Subgroup 33 benchmark exercise which is focused upon combined use of differential and integral data using adjustment technique. The results are generated by B...
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The paper presents IRSN's results of the OECD/NEA WPEC Subgroup 33 benchmark exercise which is focused upon combined use of differential and integral data using adjustment technique. The results are generated by BERING code using different sets of input data: integral parameters and sensitivity coefficients for fast benchmark experiments and applications computed by deterministic ERANOS code and Monte Carlo SCALE sequences, COMMARA-2.0 and JENDL-4.0 cross-section-covariance data and integral correlations provided by JAEA. The paper demonstrates results of the adjustment when using different input data and two adjustment algorithms implemented in BERING.
Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importanc...
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Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.
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