A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict

Li, Mi; Zhang, Jinyu; Song, Jie; Zi-jian, LI; Lu, Shengfu

doi:10.1109/tcss.2022.3152091

Cited by 45 publications

(27 citation statements)

References 47 publications

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“…where A and B denote the d-dimensional subspaces generated by principal component analysis [46] for the source and target data, respectively. Tr (•) represents the trace of matrix.…”

Section: Discriminative Subspace Alignmentmentioning

confidence: 99%

Discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction for cross-domain bearing fault diagnosis

Xu,

Liu,

Peng

et al. 2023

Meas. Sci. Technol.

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Many domain adaptation (DA) approaches have been developed to address the challenge of domain divergence in cross-domain fault diagnosis. However, most of them only attempt to align statistical distribution while neglecting geometric alignment between source and target data. Furthermore, the use of some unreliable pseudo-labels may cause geometrical and statistical distributions mismatching and interfere with the DA model generating correct pseudo-labels during the iterative learning. In this paper, we propose a new model called discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction (DSDGSA-PC) for bearing fault diagnosis. Firstly, discriminative subspace alignment is proposed to mitigate feature redundancy and divergence by generating aligned subspaces for two domains, while preserving class discriminative information and global structures of data. Then, DSDGSA-PC leverages the representer theorem and the principle of structural risk minimization (SRM) to learn a domain-invariant classifier in the subspace, while minimizing statistical and geometrical shift by jointly optimizing dynamic graph embedding and dynamic weighted distribution alignment strategies. Finally, a novel pseudo-label correction mechanism is integrated into DSDGSA-PC to evaluate the credibility of pseudo-labels and rectify the unreliable ones during the iterations. The experimental results illustrate that DSDGSA-PC has higher transfer performance compared to several advanced methods on 24 transfer tasks.

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“…where A and B denote the d-dimensional subspaces generated by principal component analysis [46] for the source and target data, respectively. Tr (•) represents the trace of matrix.…”

Section: Discriminative Subspace Alignmentmentioning

confidence: 99%

Discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction for cross-domain bearing fault diagnosis

Xu,

Liu,

Peng

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Therefore, it may lead to some bias in the decision boundaries or classification rules constructed by the classifier [20]. Other methods are also proposed in recent years [7,[21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method based on hybrid sampling algorithm with energy entropy under unbalanced conditions

Zhao

Liu

Chen

et al. 2023

Meas. Sci. Technol.

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For the degraded performance of the fault diagnosis model caused by massive normal samples and scarce fault samples under unbalanced conditions, a new fault diagnosis method based on a hybrid sampling algorithm and energy entropy, namely HSEEFD is proposed in this paper. In the proposed method, Empirical Modal Decomposition (EMD) is employed to decompose the vibration signals into Intrinsic Mode Functions (IMFs), and the energy entropy feature of each IMF component is extracted to construct a feature vector matrix. Then, a new hybrid sampling algorithm using Tomek’s Links algorithm, Euclidean distance, K-means algorithm, and synthetic minority over-sampling technique (SMOTE), namely TSHSA is designed to balance the extracted features. Tomek’s Links algorithm is used to identify and remove the confusable majority class samples at the boundary. Euclidean distance is applied to find the suspected noise points in minority class samples and remove them. The k-means algorithm is employed to cluster the minority class samples and SMOTE is used to deal with each cluster according to the density of the clusters to synthesize new features. Finally, the support vector machine (SVM) is applied to classify faults and realize fault diagnosis. The experiment results on the actual imbalanced data show that the proposed HSEEFD method can effectively improve the accuracy of the fault diagnosis under unbalanced conditions by increasing the accuracy value by more than 2.1%, and the AUC and G-mean by more than 0.7%, 2.1%, respectively.

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“…Spectrum analysis technology is widely used in many fields, including biomedicine [13][14][15][16][17], communication, astronomy, chemical analysis, environmental monitoring and material characterization. In the field of communication, spectrum analysis technology can be used to analyze and monitor the radio spectrum and help detect and solve the problem of wireless signal interference.…”

Section: Introductionmentioning

confidence: 99%

Review of spectrum analysis in fault diagnosis for mechanical equipment

Wang,

Sun

2023

Eng. Res. Express

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Various mechanical equipment play a crucial role, and their health or status may affect efficiency and safety seriously. Spectrum analysis of the corresponding signal has been widely used to diagnose the fault in the past decades. The diagnosis method based on spectrum analysis technology covers almost all aspects of mechanical fault diagnosis. However, there is a lack of review of diagnostic methods of spectrum analysis technologies in the field of mechanical equipment fault diagnosis. In order to fill this gap, this paper reviews the spectrum analysis technology in mechanical equipment diagnosis in detail. First of all, in order to let the researchers who are in contact with spectrum analysis technology for the first time quickly understand this field, the principles of spectrum are systematically sorted out, including spectrum, cepstrum, energy spectrum, power spectrum, higher-order spectrum, Hilbert spectrum, marginal spectrum, envelope spectrum, singular spectrum and so on. Furthermore, the characteristics of corresponding spectrum analysis technologies are summarized, and their advantages and disadvantages are analyzed and compared. High-quality references in recent ten years are cited for illustration to enhance persuasiveness. Finally, the prospect of spectrum analysis technology is summarized, and the future development trend of spectrum analysis technology is pointed out. It is believed that the joint diagnosis of fault severity, variable speed fault diagnosis, combined with deep learning and multiple spectrum analysis technologies should be given more attention in the future. This paper is expected to provide a comprehensive overview of mechanical fault diagnosis based on spectrum analysis theory, and help to develop corresponding spectrum analysis technologies in practical engineering.

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A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict

Cited by 45 publications

References 47 publications

Discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction for cross-domain bearing fault diagnosis

Discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction for cross-domain bearing fault diagnosis

A fault diagnosis method based on hybrid sampling algorithm with energy entropy under unbalanced conditions

Review of spectrum analysis in fault diagnosis for mechanical equipment

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