Machine learning study of the relationship between the geometric and entropy discord

Li, Xiaoyü; Zhu, Qihe; Zhu, Mingzheng; Huang, Yufei; Wu, Hao; Wu, Shao-Yi

doi:10.1209/0295-5075/127/20009

Cited by 16 publications

(7 citation statements)

References 49 publications

(53 reference statements)

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“…The fully connected layer is to classify and recognize the signal after feature extraction [18]. This paper is a two-class classification problem of normal sequence and fault sequence, and the Sigmoid activation function can be used.…”

Section: Fully Connected Layermentioning

confidence: 99%

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Zhang¹,

Lin²,

Chen³

et al. 2022

Computer Systems Science and Engineering

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Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.

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Section: Fully Connected Layermentioning

confidence: 99%

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Zhang¹,

Lin²,

Chen³

et al. 2022

Computer Systems Science and Engineering

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“…In this work, we formulate the FWI based on the generalization of Gauss' error law linked to the Rényi entropy (or α-entropy). The Rényi α-entropy [30,31] was proposed in the context of information theory as a generalization of the Boltzmann -Gibbs -Shannon (BGS) entropy [32], which is very useful to modeling and describing several complex systems in ecology [33,34], machine learning [35,36], as well as in quantum entanglement [37,38]. By considering that the errors are independent and identically distributed by a α-generalized Gaussian distribution, which arises from the maximization of the α-entropy, we place a robust misfit function in the broad context of the FWI based on the Gauss' law of error in the Rényi framework.…”

Section: Introductionmentioning

confidence: 99%

Target-oriented full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques

Barbosa¹,

Silva²,

Barra³

et al. 2022

Preprint

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The estimation of physical parameters from data analysis is a crucial point for the description and modeling of many complex systems. Based on Rényi α-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). We show the effectiveness of our proposal by considering two distinct realistic P-wave velocity models, in which the first one is inspired in the Kwanza Basin in Angola and the second in a region of great economic interest in the Brazilian pre-salt field. We call our proposal by the abbreviation α-PGF-FWI. The results reveal that the α-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit α → 2/3, being α the Rényi entropic index.

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“…Valeria Cimini and co-workers proposed an artificial neural network to calculate the negativity of the Wigner function for multi-mode quantum states [22]. The connection between geometric and entropy discord has also been explored using machine learning [23].…”

Section: Introductionmentioning

confidence: 99%

Efficient Characterization of Quantum Evolutions via a Recommender System

Batra

Singh

Mahesh

2021

Quantum

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We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguish a clean database of quantum correlations from a noisy or a fake one. Moreover, we find that the RS brings about a significant computational advantage for building a large database of quantum discord, for which no simple closed-form expression exists. Also, RS can efficiently characterize systems undergoing nonunitary evolutions in terms of quantum discord reduction as well as state-fidelity. Finally, we utilize RS for the construction of discord phase space in a nonlinear quantum system.

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Machine learning study of the relationship between the geometric and entropy discord

Cited by 16 publications

References 49 publications

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Target-oriented full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques

Efficient Characterization of Quantum Evolutions via a Recommender System

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