• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.709-730
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• 2017

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile user. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the effective bandwidth demand of each user actually differs vastly due to their different preferences for mobile applications. In this paper, we formulate a set of non-linear integer programming models for joint user association control and user demand guarantee problem. In this model, we are trying to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control with a software defined network controller. With the fact of NP-hard complexity of non-linear integer programming solver, we propose a Kernighan Lin Algorithm based graph-partitioning method for a large-scale network. Finally, we evaluated the performance of the proposed algorithm for the edge users with heterogeneous bandwidth demands and mobility scenarios. Simulation results show that the proposed adaptive soft-association control can achieve a better performance than the other two and improves the individual quality of user experience with a little price on system throughput.

• 한국환경과학회지
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• 제16권3호
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• pp.287-297
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• 2007

Increasing carbon dioxide emissions from fossil fuel use and land-use change has been perturbing the balanced global carbon cycle and changing the carbon distribution among the atmosphere, the terrestrial biosphere, the soil, and the ocean. SGCM(Simple Global Carbon Model) was used to simulate global carbon cycle for the IPCC emissions scenarios, which was six future carbon dioxide emissions from fossil fuel use and land-use change set by IPCC(Intergovernmental Panel on Climate Change). Atmospheric $CO_2$ concentrations for four scenarios were simulated to continuously increase to $600{\sim}1050ppm$ by the year 2100, while those for the other two scenarios to stabilize at $400{\sim}600ppm$. The characteristics of these two $CO_2$-stabilized scenarios are to suppress emissions below $12{\sim}13$ Gt C/yr by tile year 2050 and then to decrease emissions up to 5 Gt C/yr by the year 2100, which is lower than the current emissions of $6.3{\pm}0.4$ Gt C/yr. The amount of carbon in the atmosphere was simulated to continuously increase for four scenarios, while to increase by the year $2050{\sim}2070$ and then decrease by the year 2100 for the other two scenarios which were $CO_2$-stabilized scenarios. Even though the six emission scenarios showed different simulation results, overall patterns were such similar that the amount of carbon was in the terrestrial biosphere to decrease first several decades and then increase, while in the soil and the ocean to continuously increase. The ratio of carbon partitioning to tile atmosphere for the accumulated total emissions was higher for tile emission scenario having higher atmospheric $CO_2$, however that was decreasing as time elapsed. The terrestrial biosphere and the soil showed reverse pattern to the atmosphere.

• Current Optics and Photonics
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• 제5권4호
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• pp.431-443
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• 2021

Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l_2,0 norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l_2,0 norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l_2,0 norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.