An elementary algorithm is used to simulate the industrial production of a fiber of a 3-dimensional nonwoven fabric. The algorithm simulates the fiber as a polyline where the direction of each segment is stochasticall...
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An elementary algorithm is used to simulate the industrial production of a fiber of a 3-dimensional nonwoven fabric. The algorithm simulates the fiber as a polyline where the direction of each segment is stochastically drawn based on a given probability density function (PDF) on the unit sphere. This PDF is obtained from data of directions of fiber fragments which originate from computer tomography scans of a real nonwoven fabric. However, the simulation algorithm requires numerous evaluations of the PDF. Since the established technique of a kernel density estimator leads to very high computational costs, a novel greedy algorithm for estimating a sparse representation of the PDF is introduced. Numerical tests for a synthetic and a real example are presented. In a realistic scenario, the introduced sparsity ansatz leads to a reduction of the computation time for 100 fibers from around 80 days to 2.5 hours. (C) 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Various tensor decompositions use different arrangements of factors to explain multi-way data. Components from different decompositions can vary in the number of parameters. Allowing a model to contain components from...
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ISBN:
(纸本)9781479903573
Various tensor decompositions use different arrangements of factors to explain multi-way data. Components from different decompositions can vary in the number of parameters. Allowing a model to contain components from different decompositions results in a combinatoric number of possible models. Model selection balances approximation error and the number of parameters, but due to the number of possible models, post-hoc model selection is infeasible. Instead, we incrementally build a model. This approach is analogous to sparse coding with a union of dictionaries. The proposed greedy approach can estimate a model consisting of a combination of tensor decompositions.
This paper deals with representing the structural organization of the combinatorial optimization problems in terms of the hypergraphs, whose hyperedges reflect the solutions of the original problem and its nested subp...
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ISBN:
(纸本)9781509050451
This paper deals with representing the structural organization of the combinatorial optimization problems in terms of the hypergraphs, whose hyperedges reflect the solutions of the original problem and its nested subproblems. By using the achievements of the matroid theory, the paper analyzes the parameters of such hypergraphs that determine the suitability of the corresponding problems for being processed by the greedy algorithms. In addition, the study contains the examples of the hypergraph structures constructed for the instance of the minimum spanning tree problem.
In this article we present our contribution to the Rolling Stock Unit Management problem proposed for the ROADEF/EURO Challenge 2014. We propose a greedy algorithm to assign trains to departures. Our approach relies o...
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In this article we present our contribution to the Rolling Stock Unit Management problem proposed for the ROADEF/EURO Challenge 2014. We propose a greedy algorithm to assign trains to departures. Our approach relies on a routing procedure using multi-interval constraint propagation to compute the individual schedules of trains within the railway station. This algorithm allows to build an initial solution, satisfying a significant subset of departures.
Wastewater quality monitoring is receiving growing interest with the necessity of developing new strategies for controlling accidental and intentional illicit intrusions. In designing a monitoring network, a crucial a...
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Wastewater quality monitoring is receiving growing interest with the necessity of developing new strategies for controlling accidental and intentional illicit intrusions. In designing a monitoring network, a crucial aspect is represented by the sensors' location. In this study, a methodology for the optimal placement of wastewater monitoring sensors in sewer systems is presented. The sensor location is formulated as an optimization problem solved using greedy algorithms (GRs). The Storm Water Management Model (SWMM) was used to perform hydraulic and water-quality simulations. Six different procedures characterized by different fitness functions are presented and compared. The performances of the procedures are tested on a real sewer system, demonstrating the suitability of GRs for the sensor-placement problem. The results show a robustness of the methodology with respect to the detection concentration parameter, and they suggest that procedures with multiple objectives into a single fitness function give better results. A further comparison is performed using previously developed multi-objective procedures with multiple fitness functions solved using a genetic algorithm (GA), indicating better performances of the GR. The existing monitoring network, realized without the application of any sensor design, is always suboptimal.
In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger r...
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ISBN:
(数字)9781728138930
ISBN:
(纸本)9781728138947
In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.
Maximum Concurrent Flow techniques try to combine a maximum flow graph resolution and the satisfaction of queries on this graph. They are proven to give better performance over classical heuristic data-source placemen...
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ISBN:
(数字)9781728138930
ISBN:
(纸本)9781728138947
Maximum Concurrent Flow techniques try to combine a maximum flow graph resolution and the satisfaction of queries on this graph. They are proven to give better performance over classical heuristic data-source placement methods combined with greedy query resolution. A new version of the MCF approach is proposed and examined. It is tailored to provide MCF properties for large IoT networks. The results found from evaluation and comparison with other algorithms show that it has high efficiency and outperforms many recent solutions.
Dgreedy(distributed greedy) algorithm evaluates the priority level in view of remaining energy of terminals, and the relationships between neighbor nodes are not considered. At the same time, the adjustable sensing or...
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Dgreedy(distributed greedy) algorithm evaluates the priority level in view of remaining energy of terminals, and the relationships between neighbor nodes are not considered. At the same time, the adjustable sensing orientations of sensors are limited. Therefore, the network coverage ratio of Dgreedy is affected usually by the processing order of sensor nodes. In this paper, an improved greedy algorithm for the coverage in directional sensor network is proposed based on the principle of global greedy. The single coverage area of nodes is considered as priority. The direction of node with maximum single coverage area is deployed firstly. Thereby it reduces the sensing overlapping regions and accomplishes coverage enhancement of the networks. Meanwhile, in order to improve the network coverage ratio, the sensing orientations of sensors are adjustable continuously, so the best sensing orientation of node can be selected by considering the deployment of neighbor nodes. Simulation experiments show that the proposed algorithm can improve the coverage area effectively.
In this study, we present an automatic 3D teeth segmentation algorithm using 3D curvature and a greedy algorithm to localize teeth in 2D level curves. We started by loading the digital study model to generate a 3D gra...
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ISBN:
(纸本)9781450372879
In this study, we present an automatic 3D teeth segmentation algorithm using 3D curvature and a greedy algorithm to localize teeth in 2D level curves. We started by loading the digital study model to generate a 3D graph from the Standard Triangle Language (STL) file. After dividing the 3D model into 30 images corresponding to different level curves, we selected two 2D images and we used a greedy algorithm to locate the centroid of the analyzed teeth. The coordinates of the obtained image are transformed into a 3D model using a bilinear transformation and perspective. Finally, we use Breadth First Search algorithm (BFS) in the 3D graph with a curvature parameter that restricts the progress of the segmentation. The database used was 4 files in the format STL, and the algorithm was implemented in Python. An on-going optimization process will allow us to integrate this method into an operational platform.
The submodular maximization problem is widely applicable in many engineering problems where objectives exhibit diminishing returns. While this problem is known to be NP-hard for certain subclasses of objective functio...
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ISBN:
(数字)9781538682661
ISBN:
(纸本)9781538682678
The submodular maximization problem is widely applicable in many engineering problems where objectives exhibit diminishing returns. While this problem is known to be NP-hard for certain subclasses of objective functions, there is a greedy algorithm which guarantees approximation at least 1/2 of the optimal solution. This greedy algorithm can be implemented with a set of agents, each making a decision sequentially based on the choices of all prior agents. In this paper, we consider a generalization of the greedy algorithm in which agents can make decisions in parallel, rather than strictly in sequence. In particular, we are interested in partitioning the agents, where a set of agents in the partition all make a decision simultaneously based on the choices of prior agents, so that the algorithm terminates in limited iterations. We provide bounds on the performance of this parallelized version of the greedy algorithm and show that dividing the agents evenly among the sets in the partition yields an optimal structure. It is shown that such optimal structures holds even under very relaxed information constraints. We additionally show that this optimal structure is still near-optimal, even when additional information (i.e., total curvature) is known about the objective function.
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