A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification

Gu, Bin; Sheng, Victor S.

doi:10.1109/tnnls.2016.2527796

Cited by 347 publications

(121 citation statements)

References 11 publications

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“…Second, the baselines of the IoT traffic demands of LEO satellite networks may need to be revised using realistic traffic traces and appropriate prediction algorithms. Big data, [61][62][63] wavelet analysis, 64 and support vector regression [65][66][67][68][69] technologies would be helpful for analyzing traffic characteristic and designing better traffic model. Therefore, in future work, we hope to address more real-world projects, collect realistic traces, and find appropriate traffic prediction algorithms suitable for IoT and satellite environments.…”

Section: Resultsmentioning

confidence: 99%

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

Liu

Wang

et al. 2017

International Journal of Distributed Sensor Networks

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Satellite networks provide complete connectivity and worldwide data transmission capability for constructing the Internet of Things. However, because of the varying Internet of Things traffic density, satellite networks may endure imbalanced traffic requirements and frequent link congestion. To effectively resolve these problems and optimally transmit Internet of Things data, a novel hybrid global-local load balancing routing scheme for Low Earth Orbit satellite networks is proposed in this article. Hybrid global-local load balancing routing scheme enables satellites to route Internet of Things traffic through global planning and local real-time adjustments in two steps. In hybrid global-local load balancing routing scheme, given the predictive nature of Internet of Things traffic distribution and Low Earth Orbit satellite networks, the inter-satellite traffic demand is decomposed into a predictable long-range baseline and unpredictable shortrange fluctuations. A global strategy is employed first for preliminary global traffic allocation based on long-range baselines, and a local strategy is then employed for route adjustments based on short-range fluctuations. With the combination of global planning and local real-time adjustments, network traffic can eventually obtain a near-optimal allocation. Numerical simulations indicate that in contrast to single-strategy schemes, hybrid global-local load balancing routing scheme can more thoroughly eliminate congestion, and it performs better in measures such as packet loss rate, average queuing delay, traffic distribution, route oscillation, and communication overhead.

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Section: Resultsmentioning

confidence: 99%

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

Liu

Wang

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Thirdly, our proposed method will surely work well in combination with other computational intelligence algorithms, such as minimax probability machine 79 , support vector classification 81 , fuzzy C-means 82 , and least significant bit (LSB) matching [83][84] . …”

Section: Discussionmentioning

confidence: 96%

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

Feng¹,

Wang²,

Gao³

2016

IJCIS

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Cuckoo search (CS) is a novel biologically inspired algorithm and has been widely applied to many fields. Although some binary-coded CS variants are developed to solve 0-1 knapsack problems, the search accuracy and the convergence speed are still needed to further improve. According to the analysis of the shortcomings of the standard CS and the advantage of the global harmony search (GHS), a novel hybrid meta-heuristic optimization approach, called cuckoo search Algorithm with global harmony search (CSGHS), is proposed in this paper to solve 0-1 knapsack problems (KP) more effectively. In CSGHS, it is the combination of the exploration of GHS and the exploitation of CS that makes the CSGHS efficient and effective. The experiments conducted demonstrate that the CSGHS generally outperformed the binary cuckoo search, the binary shuffled frog-leaping algorithm and the binary differential evolution in accordance with the search accuracy and convergence speed. Therefore, the proposed hybrid algorithm is effective to solve 0-1 knapsack problems.

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“…Linear methods have been shown to outperform most persistence methods in short-term forecasting as they can capture the time relevance and probability distribution of wind speed data [19,20]. Nonlinear methods such as artificial neural networks (ANNs) [21], support vector machines (SVM) [22,23], etc., are demonstrated to outperform linear methods in nonlinear models. ANN, which is a simplified model of human brain neural processing, has the advantage of fast self-learning capability, easy implementation, and high prediction accuracy [24].…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

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Energy internet provides an open framework for integrating every piece of equipment involved in energy generation, transmission, transformation, distribution, and consumption with novel information and communication technologies. In this chapter, the authors adopt a combination of game theory and big data to address the coordinated management of renewable and traditional energy, which is a typical issue on energy interconnections. The authors formulate the energy management problem as a three-stage Stackelberg game and employ the backward induction method to derive the closed-form expressions of the optimal strategies. Next, we study the big data-based power generation forecasting techniques and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Simulation results show that more accurate prediction results of wind power are conducive to better energy management.

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A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification

Cited by 347 publications

References 11 publications

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

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