This paper presents an analysis of the tradeoff between repeated communications and computations for a fast distributed computation of global decision variables in a model-predictive-control (MPC)-based coordinated co...
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This paper presents an analysis of the tradeoff between repeated communications and computations for a fast distributed computation of global decision variables in a model-predictive-control (MPC)-based coordinated control scheme. We consider a coordinated predictive control problem involving uncertain and constrained subsystem dynamics and employ a formulation that presents it as a distributed optimization problem with sets of local and global decision variables where the global variables are allowed to be optimized over a longer time interval. Considering a modified form of the dual-averaging -based distributed optimization scheme, we explore convergence bounds under ideal and non-ideal wireless communications and determine the optimal choice of communication cycles between computation steps in order to speed up the convergence per unit time of the algorithm. We apply the algorithm for a class of dynamic-policy based stochastic coordinated control problems and illustrate the results with a simulation example. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
This paper presents a collaborative approach to the assignment and sequencing of batches in pipeline networks. The approach is based on the integration of a heuristic algorithm with a mixed integer linear programming ...
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This paper presents a collaborative approach to the assignment and sequencing of batches in pipeline networks. The approach is based on the integration of a heuristic algorithm with a mixed integer linear programming (MILP) model. The pipeline-scheduling problem is solved using a hierarchical decomposition [Ind. Eng. Chem. Res. 2015, 54, 5077], but a new collaborative approach is proposed for assignment/sequencing tasks. At a first step, the proposed heuristic algorithm (assignment module) determines priorities for sending batches in order to respect deadlines. The algorithm encompasses an analysis of production and demand plans, inventories, and input and output of products in terminals, trying to use resources, namely tanks and pipelines, in an optimized form. This algorithm is used in cooperation with a proposed MILP sequencing model, winch allows overcoming computation difficulties previously indicated by a traditional scheduling approach that tried to aggregate into the same monolithic MILP model assignment and sequencing decisions [Ind. Eng. Chem. Res. 2012, S1, 4591]. The proposed assignment/sequencing collaborative approach can be used to define operational batches with their volumes and routes in pipeline networks. Thus, the lot-sizing problem of batches in pipeline networks is addressed within the proposed paper. Tests were made in pipeline networks of different topologies. First, a small, but representative pipeline network is proposed and a data set for this network is made available for reproducibility purposes. Second, tests are made in a real-world pipeline network and results have been attained in computational times from seconds to few minutes.
The modeling of two-phase flows in computational fluid dynamics is still an area of active research. One popular method is the coupling of level-set and volume-of-fluid (CLSVOF), which benefits from the advantages of ...
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The modeling of two-phase flows in computational fluid dynamics is still an area of active research. One popular method is the coupling of level-set and volume-of-fluid (CLSVOF), which benefits from the advantages of both approaches and results in improved mass conservation while retaining the straightforward computation of the curvature and the surface normal. Despite its popularity, details on the involved complex computational algorithms are hard to find and if found, they are mostly fragmented and inaccurate. In contrast, this article can be used as a comprehensive guide for an implementation of CLSVOF into the existing level-set Navier-Stokes solvers on Cartesian grids in three dimensions.
A new approach to design of a fuzzy-rule-based classifier that is capable of selecting informative features is discussed. Three basic stages of the classifier construction-feature selection, generation of fuzzy rule b...
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A new approach to design of a fuzzy-rule-based classifier that is capable of selecting informative features is discussed. Three basic stages of the classifier construction-feature selection, generation of fuzzy rule base, and optimization of the parameters of rule antecedents-are identified. At the first stage, several feature subsets on the basis of discrete harmonic search are generated by using the wrapper scheme. The classifier structure is formed by the rule base generation algorithm by using extreme feature values. The optimal parameters of the fuzzy classifier are extracted from the training data using continuous harmonic search. Akaike information criterion is deployed to identify the best performing classifiers. The performance of the classifier was tested on real-world KEEL and KDD Cup 1999 datasets. The proposed algorithms were compared with other fuzzy classifiers tested on the same datasets. Experimental results show efficiency of the proposed approach and demonstrate that highly accurate classifiers can be constructed by using relatively few features.
Using the dynamic properties of fractional-order Duffing system, a sequential parameter identification method based on differential evolution optimization algorithm is proposed for the fractional-order Duffing system....
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Using the dynamic properties of fractional-order Duffing system, a sequential parameter identification method based on differential evolution optimization algorithm is proposed for the fractional-order Duffing system. Due to the step by step parameter identification method, the dimension of parameter identification of each step is greatly reduced and the search capability of the differential evolution algorithm has been greatly improved. The simulation results show that the proposed method has higher convergence reliability and accuracy of identification and also has high robustness in the presence of measurement noise.
In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis dete...
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In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis detection be performed efficiently and accurately. In this paper, we put forward a universal correlation analysis detection model and algorithm by introducing state transition diagram. Based on analyzing and comparing the current correlation detection modes, we formalize the correlation patterns and propose a framework according to data packet timing and behavior qualities and then design a new universal algorithm to implement the method. Finally, experiment, which sets up a lightweight intrusion detection system using KDD1999 dataset, shows that the correlation detection model and algorithm can improve the performance and guarantee high detection rates.
Many mathematical physics problems have great computational complexity, especially when they are solved on large-scale three-dimensional grids. The discontinuous Galerkin method is just an example of this kind. Theref...
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Many mathematical physics problems have great computational complexity, especially when they are solved on large-scale three-dimensional grids. The discontinuous Galerkin method is just an example of this kind. Therefore, reduction of the amount of computation is very a topical task. One of the possible ways to reduce the amount of computation is to move some of the computations to the compilation stage. With the appearance of templates, C++ provides such an opportunity. The paper demonstrates the use of template metaprogramming to speed up computations in the discontinuous Galerkin method. In addition, template metaprogramming sometimes simplifies the algorithm at the expense of its generalization.
A topological method for the design and optimization of planar circularly polarized (CP) directional antenna with low profile was presented. By inserting two parasitic layers, generated by particle swarm optimization,...
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A topological method for the design and optimization of planar circularly polarized (CP) directional antenna with low profile was presented. By inserting two parasitic layers, generated by particle swarm optimization, between the equiangular spiral antenna and the ground, a low-profile wideband CP antenna with directional radiation pattern and high gain is achieved. The optimized antenna shows an impedance matching band (|S-11| < 10dB) of 4-12 GHz with a whole-band stable directional pattern in 4-11.5 GHz, and the antenna gain peak is 8 dBi, which work well in the available band. Measured return loss, antenna gain, and far field patterns agree well with simulation results.
The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open acces...
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The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.
Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful to...
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Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.
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