Structural Minimax Probability Machine

Gu, Bin; Sun, Xingming; Sheng, Victor S.

doi:10.1109/tnnls.2016.2544779

Cited by 264 publications

(82 citation statements)

References 28 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

“…Conventional pattern recognition methods, like backpropagation (BP) neural network and support vector machine (SVM), are widely applied for fault diagnosis [15,16]. Yang et al distinguished signals at different corrosion stages using BP neural networks in the acoustic emission testing of a tank bottom [17,18]. Nevertheless, BP neural network has disadvantages related to abundant parameter settings and slow convergence and is easily caught in a local minimum.…”

Section: Shock and Vibrationmentioning

confidence: 99%

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Liang

Dong-min

Daidi

et al. 2018

Shock and Vibration

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A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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“…The support vector machine (SVM), introduced by Vapnik, is one of the most popular tools in bioinformatics for a supervised machine learning methods based on structural risk minimization [43][44][45][46][47]. The basic characteristic of SVM is to map the original nonlinear data into a higher dimensional feature space, where a hyperplane is constructed to bisect two classes of data and maximize the margin of separation between itself and those points lying nearest to it (the support vectors).…”

Section: Svmmentioning

confidence: 99%

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Zhao

Sun

Deng

et al. 2016

Entropy

182

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Abstract:Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

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Structural Minimax Probability Machine

Cited by 264 publications

References 28 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 Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

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