Quantum Relief algorithm

Liu, Wenjie; Gao, Peipei; Yu, Wai; Qu, Zhiguo; Yang, Ching-Nung

doi:10.1007/s11128-018-2048-x

Cited by 33 publications

(15 citation statements)

References 21 publications

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“…Using domain expertise, the number of parameters of interest was reduced to 12. Next, three algorithms (i.e., the wrapper with genetic search (WGS) (Kohavi and John, 1997), boosting-tree algorithm (BTA) (Sbihi, 2007), and the relief algorithm (RA) (Liu et al, 2018) were applied to select the most relevant parameters for predicting the generator bearing temperature. The wrapper approach uses supervised learning to perform 10-fold cross validation in selecting relevant parameters.…”

Section: Parameter Selectionmentioning

confidence: 99%

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

et al. 2021

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Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

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Section: Parameter Selectionmentioning

confidence: 99%

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

et al. 2021

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“…erefore, the quantum-based dimensionality reduction method still has important research value. In 2018, Liu et al [33] proposed a quantum Relief algorithm (namely, QRelief algorithm) for the two-classification problem, which reduces the complexity of similarity calculation from O(MN) to O(M).…”

Section: Introductionmentioning

confidence: 99%

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

et al. 2020

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The complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multiclassification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and Ry, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover–Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to OM, and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to OM. Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.

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“…The earliest QSS scheme was proposed by Hillery et al in 1999 [1], they used the Greenberger-Horne-Zeilinger (GHZ) entangled state to complete secret sharing. With the development of quantum information, numerous quantum protocols and algorithms have been presented with entanglement and without entanglement [2][3][4][5][6][7][8][9], and the QSS protocols are no exception [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Quantum Secret Sharing Scheme with Credible Authentication based on Quantum Walk

Li¹,

Yan²,

Zhang³

2020

KSII TIIS

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Based on the teleportation by quantum walk, a quantum secret sharing scheme with credible authentication is proposed. Using the Hash function and quantum local operation, combined with the two-step quantum walks circuit on the line, the identity authentication and the teleportation of the secret information in distribution phase are realized. Participants collaborate honestly to recover secret information based on particle measurement results, preventing untrusted agents and external attacks from obtaining useful information. Due to the application of quantum walk, the sender does not need to prepare the necessary entangled state in advance, simply encodes the information to be sent in the coin state, and applies the conditional shift operator between the coin space and the position space to produce the entangled state necessary for quantum teleportation. Security analysis shows that the protocol can effectively resist intercept/resend attacks, entanglement attacks, participant attacks, and impersonation attacks. In addition, the quantum walk circuit used has been implemented in many different physical systems and experiments, so this quantum secret sharing scheme may be achievable in the future.

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Quantum Relief algorithm

Cited by 33 publications

References 21 publications

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

Quantum Secret Sharing Scheme with Credible Authentication based on Quantum Walk

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