Transfer learning focuses on the learning scenarios when the test data from target domains and the training data from source domains are drawn from similar but different data distribution with respect to the raw featu...
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Membrane algorithms are a new class of heuristic algorithms, which attempt to incorporate some components of membrane computing models (also called P systems) for designing efficient optimization algorithms, such as t...
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Membrane algorithms are a new class of heuristic algorithms, which attempt to incorporate some components of membrane computing models (also called P systems) for designing efficient optimization algorithms, such as the structure of P systems, the way of communication between cells, etc. Membrane algorithms are a kind of parallel methods, where many operations can be performed in parallel. Although the importance of the parallelism of such algorithms is recognized, membrane algorithms were often implemented on the serial computing device Central processing Unit (CPU), which makes the algorithms cannot work in a more efficient way. In this work, we consider the implementation of membrane algorithms on the parallel computing device Graphics processing Unit (GPU). Under such implementation, all cells of membrane algorithms can work simultaneously. Experiment results on two classical intractable problems, point set matching problem and TSP, show that GPU implementation of membrane algorithms is much more efficient than CPU implementation in terms of runtime, especially for solving the problems with a high complexity.
In this paper, we propose a bilingual lexical cohesion trigger model to capture lexical cohesion for document-level machine translation. We integrate the model into hierarchical phrase-based machine translation and ac...
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This letter presents the graphic processor unit (GPU)implementation of the finite-difference time-domain (FDTD)method for the solution of the two-dimensional electromagnetic fields inside dispersive *** improved Z-tra...
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This letter presents the graphic processor unit (GPU)implementation of the finite-difference time-domain (FDTD)method for the solution of the two-dimensional electromagnetic fields inside dispersive *** improved Z-transform-based finite-difference time-domain (ZTFDTD) method was presented for simulating the interaction of electromagnetic wave with unmagnetized *** using the newly introduced Compute Unified Device Architecture (CUDA) technology, we illustrate the efficacy of GPU in accelerating the FDTD computations by achieving significant speedups with great ease and at no extra hardware *** effect of the GPU-CPU memory transfers on the speedup will be also studied.
In this paper, we propose a novel model of three points named TP for location estimation in wireless sensor networks(WSNs) with random deployment of anchor nodes. In this model, we select three anchor nodes which have...
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In this paper, we propose a novel model of three points named TP for location estimation in wireless sensor networks(WSNs) with random deployment of anchor nodes. In this model, we select three anchor nodes which have the strongest received signal strength(RSS) for location estimation, the centroid algorithm and the method of intersection of judgment are used to estimate the location of unknown nodes. To further exploit three nearest intersection points in TP, the enhanced TP(ETP) is proposed. The simulation results show that the proposed models outperform MMSE and BML in terms of the localization accuracy for WSNs. Moreover, the localization accuracy of the proposed models in scenario 2 with random deployment of anchor nodes are better than in scenario 1 with planned deployment of anchor nodes. Additionally, compared with MMSE and BML, ETP and TP can reduce the environmental impact on location estimation.
Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration. Under the condition of nitrogen deficiency, the crop shows the nitrogen deficiency symptoms in the bottom leaves, while e...
Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration. Under the condition of nitrogen deficiency, the crop shows the nitrogen deficiency symptoms in the bottom leaves, while excessive nitrogen will affect the upper layer leaves first. Thus, timely measurement of vertical distribution of foliage nitrogen content is critical for growth diagnosis, crop management and reducing environmental impact. This study presents a method using bi-directional reflectance difference function (BRDF) data to invert foliage nitrogen vertical distribution. We developed upper-layer nitrogen inversion index (ULNI), middle-layer nitrogen inversion index (MLNI) and bottom-layer nitrogen inversion index (BLNI) to reflect foliage nitrogen inversion at upper layer, middle layer and bottom layer, respectively. Both ULNI and MLNI were made by the value of the ratio of Modified Chlorophyll Absorption Ration Index to the second Modified Triangular Vegetation Index (MCARI/MTVI2) referred to as canopy nitrogen inversion index (CNII) in this study at ±40° and ±50°, and at ±30° and ±40° view angles, respectively. The BLNI was composed by the value of nitrogen reflectance index (NRI) at ±20° and ±30° view angles. These results suggest that it is feasible to measure foliage nitrogen vertical-layer distribution in a large scale by remote sensing.
In Beijing, most taxi drivers intentionally avoid working during peak hours despite of the huge customer demand within these peak periods. This dilemma is mainly due to the fact that taxi drivers' congestion costs...
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ISBN:
(纸本)9781577356332
In Beijing, most taxi drivers intentionally avoid working during peak hours despite of the huge customer demand within these peak periods. This dilemma is mainly due to the fact that taxi drivers' congestion costs are not reflected in the current taxi fare structure. To resolve this problem, we propose a new pricing scheme to provide taxi drivers with extra incentives to work during peak hours. This differs from previous studies of taxi market by considering market variance over multiple periods, taxi drivers' profit-driven decisions, and their scheduling constraints regarding the interdependence among different periods. The major challenge of this research is the computational intensiveness to identify optimal strategy due to the exponentially large size of a taxi driver's strategy space and the scheduling constraints. We develop an atom schedule method to overcome these issues. It reduces the magnitude of the problem while satisfying the constraints to filter out infeasible pure strategies. Simulation results based on real data show the effectiveness of the proposed methods, which opens up a new door to improving the efficiency of taxi market in megacities (e.g., Beijing).
Community Detection in social networks is usually considered as an objective optimization *** to the objective function,the global optimum cannot describe the real partition well,and it is time *** this paper,a layere...
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Community Detection in social networks is usually considered as an objective optimization *** to the objective function,the global optimum cannot describe the real partition well,and it is time *** this paper,a layered optimization framework is designed to improve the optimization process,reduce the scale of network and increase the quality of *** framework consists of three parts: finding cores in networks,repairing isolated nodes and optimization in a new constructed weighted network which is a compressed network of the origin ***,the equivalency of modularity optimization in the new compressed weighted network and the original one is ***,a combined algorithm of community Detection named DBPSO including similarity-based clustering,isolated nodes repairing strategies and a modified particle swarm optimization is proposed according to the layered optimization *** addition,a suitable mutation strategy for particle swarm optimization (PSO) is introduced to guarantee the convergence and global search ***,the experiments are conducted to evaluate the proposed algorithm by using the synthetic and real-world network *** results show that the proposed algorithm can effectively extract the intrinsic community structure of social networks.
2D-to-3D video conversion is a feasible way to generate 3D programs for the current 3DTV industry. However, for large-scale 3D video production, current systems are no longer adequate in terms of the time and labor re...
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2D-to-3D video conversion is a feasible way to generate 3D programs for the current 3DTV industry. However, for large-scale 3D video production, current systems are no longer adequate in terms of the time and labor required for conversion. In this paper, we introduce a distributed 2D-to-3D video conversion system that includes a 2D-to-3D video conversion module, architecture of the parallel computation on the cloud, and 3D video coding in the system. The system enables cooperation among multiple users in the simultaneous completion of their conversion tasks so that the conversion efficiency is greatly promoted. In the experiments, we evaluate the system based on criteria related to both time consumption and video coding performance.
This paper describes a novel strategy for automatic induction of a monolingual dependency grammar under the guidance of bilingually-projected dependency. By moderately leveraging the dependency information projected f...
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