A DBN Approach to Predict the Link in Opportunistic Networks

Yemeni, Zaid; Shu, Jian; Zhang, Xuepei; Liu, Linlan

doi:10.1007/978-981-10-8944-2_67

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Structure and Pretraining of DBN. DBN is often used for predicting and diagnosis [18][19][20], which is composed of restricted Boltzmann machines (RBM), and the number of layers of DBN will increase with increasing the number of RBMs. Usually, DBN has multiple hidden layer structure with multiple RBMs, and the first RBM is the visible layer of DBN and the output layer of first RBM is the input layer of the second RBM.…”

Section: Fault Diagnosis Model Based On Dbnmentioning

confidence: 99%

A Fault Diagnosis Method for Out‐of‐Round Faults of Metro Vehicle Wheels with Strong Noise

Huang

Zhang²,

2021

Shock and Vibration

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Detection of out-of round (OOR) faults of metro vehicle wheels is very important to improve stationarity and stability in metro vehicles and avoid accidents caused by OOR faults. Diagnosis of OOR faults demands extracting useful information accurately from mass of vibration signals with poor signal-to-noise ratio (SNR) of metro vehicle wheels for complex running condition. In this paper, we proposed a diagnosis method on OOR faults of metro vehicle wheels combined with variational mode decomposition (VMD), kernel principal component analysis (KPCA), and deep belief network (DBN) to diagnose the OOR faults of metro wheels. Vibration signals of China metro vehicle wheels collected while the metro vehicle is running are used to train the diagnosis model and adjust parameters of DBN and KPCA based on testing accuracy. The different dimensions of KPCA, epoch number, and node number of DBN are compared, and the better parameters of diagnosis model based on vibration signals are concluded in this paper. The generalization of the diagnosis model is checked nine times by testing the calculation of each group of parameters and using an error declining process. The mean accuracy of diagnosis model proposed in this paper is 0.9136, and the diagnosis model presented in this paper is very significant to detect OOR faults online.

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Section: Fault Diagnosis Model Based On Dbnmentioning

confidence: 99%

A Fault Diagnosis Method for Out‐of‐Round Faults of Metro Vehicle Wheels with Strong Noise

Huang

Zhang²,

2021

Shock and Vibration

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show abstract

“…These nodes are usually randomly or systematically deployed in targeted areas to collect physical parameters information, such as temperature, pressure, humidity, vibration, noise level, and vital signs. Monitoring applications are essential in environmental, health care, governmental, industrial, and military applications [1][2][3][4]. The data collected by monitoring applications are required to flow continuously over time.…”

Section: Introductionmentioning

confidence: 99%

A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

et al. 2021

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Wireless sensor networks (WSNs) are critically resource-constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non-transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Reliable Single Prediction Data Reduction Approach for WSNs Based on Kalman Filter

Yemeni

Wang

Ismael

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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A DBN Approach to Predict the Link in Opportunistic Networks

Cited by 4 publications

References 13 publications

A Fault Diagnosis Method for Out‐of‐Round Faults of Metro Vehicle Wheels with Strong Noise

A Fault Diagnosis Method for Out‐of‐Round Faults of Metro Vehicle Wheels with Strong Noise

A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

A Reliable Single Prediction Data Reduction Approach for WSNs Based on Kalman Filter

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