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1. Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes NSTL国家科技图书文献中心

Yuting Lyu |  Le Zhou... -  《IEEE transactions on automation science and engineering: a publication of the IEEE Robotics and Automation Society》 - 2024,21(2) - 2027～2038 - 共12页

摘要： mixture probability principle component analysis model | , a multirate mixture probability principal |  component analysis model is proposed for process modeling |  multirate models are built first for each mode and all of |  them are subsequently fused with the mixture modeling
关键词： Process monitoring |  Analytical models |  Data models |  Bayes methods |  Principal component analysis |  Fault diagnosis |  Fault detection
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2. Texture Image Classification Using LLGMN and HLAC Features NSTL国家科技图书文献中心

Taiga Eguchi |  Osamu Fukuda... -  《2024 IEEE International Conference on Industrial Technology: 25th IEEE International Conference on Industrial Technology (ICIT), 25-27 March 2024, Bristol, United Kingdom》 -  IEEE International Conference on Industrial Technology - 2024, - 1～6 - 共6页

摘要： obtained from the principal component analysis of Higher |  production of diverse products from the perspective of |  diversifying customer needs. The classification of products |  using Log-Linearized Gaussian Mixture Networks (LLGMN | ), which are neural networks based on a probabilistic
关键词： Training |  Neural networks |  Production |  Feature extraction |  Probabilistic logic |  Manufacturing |  Data mining
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3. An MPPCA-based approach for anomaly detection of structures under multiple operational conditions and missing data EI 工程索引 NSTL国家科技图书文献中心

Zhi Ma |  Yaozhi Luo... -  《Structural health monitoring》 - 2023,22(2) - 1069～1089 - 共21页

摘要： a mixture of probabilistic principal component | . Principal component analysis (PCA) and probabilistic PCA |  analysis (MPPCA) for the anomaly detection of structures |  early warning of structural damage to existing civil |  physical models of structures, have been widely studied
关键词： Structural health monitoring |  anomaly detection |  multiple operational conditions |  mixture of probabilistic principal component analysis |  missing data
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4. HEMPPCAT: MIXTURES OF PROBABILISTIC PRINCIPAL COMPONENT ANALYSERS FOR DATA WITH HETEROSCEDASTIC NOISE NSTL国家科技图书文献中心

Alec S. Xu |  Laura Balzano... -  《2023 IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP 2023, Rhodes Island, Greece, 4-10 June 2023, [v.12]》 -  IEEE International Conference on Acoustics, Speech and Signal Processing - 2023, - 9591～9595 - 共5页

摘要：Mixtures of probabilistic principal component |  of principal component analysis (PCA). Similar to |  analysis (MPPCA) is a well-known mixture model extension |  proposes a heteroscedastic mixtures of probabilistic PCA |  PCA, MPPCA assumes the data samples in each mixture
关键词： Heterogeneous data |  latent factors |  expectation maximization
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5. A Novel Probabilistic Network Model for Estimating Cognitive-Gait Connection Using Multimodal Interface NSTL国家科技图书文献中心

Sumit Hazra |  Acharya Aditya Prata...... -  《IEEE transactions on cognitive and developmental systems》 - 2023,15(3) - 1430～1448 - 共19页

摘要： obtained. Also, principal component analysis (PCA) and |  analysis. The advantage of the multimodal system is to | Research in human gait analysis has captivated |  provide adequate motion signatures with the ensemble of |  nontemporal probabilistic models. We estimate prior
关键词： Brain modeling |  Computational modeling |  Principal component analysis |  Bayes methods |  Electroencephalography |  Biological system modeling |  Analytical models
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6. A novel stochastic generation method of shale blocks in S?RM considering the particle size effect of morphological features EI 工程索引 NSTL国家科技图书文献中心

Chang Liu |  Han Zhang... -  《Bulletin of engineering geology and the environment》 - 2023,82(1) - 30.1～30.14 - 共14页

摘要： principal component analysis (PCA) and probability density | The increasing interest in the effect of block |  morphological features on shear behaviors of the soil–rock |  mixture (S-RM) presents a challenge to robust numerical |  paper proposes an improved probabilistic method to
关键词： Block morphology |  Spherical harmonics (SH) |  Principal component analysis (PCA) |  Particle size effect |  Discrete element method (DEM) model
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7. A probabilistic view on modelling weather regimes EI 工程索引 NSTL国家科技图书文献中心

Alessandro Baldo |  Robin Locatelli -  《International Journal of Climatology: A Journal of the Royal Meteorological Society》 - 2023,43(4) - 1710～1730 - 共21页

摘要：: Empirical Orthogonal Functions or Principal Component | Abstract The statistical modelling of weather |  regimes encompasses the definition of a framework |  highly sparse feature space of weather anomaly maps |  Analysis are used to assess the former; a standard K
关键词： Bayesian statistics |  climate variability |  machine learning |  probabilistic modelling |  variational inference |  weather regimes
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8. Hidden Markov Model Based Status Monitoring for Gas-liquid Two-phase Flow in Horizontal Pipe NSTL国家科技图书文献中心

Liyuan Zhang |  Wentao Wu... -  《2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2023), Vol.1: Kuala Lumpur, Malaysia.22-25 May 2023》 -  IEEE International Instrumentation and Measurement Technology Conferences - 2023, - 237～242 - 共6页

摘要：-liquid two-phase flow. The principal component analysis |  characteristics of complex processes such as complex structure |  achieve accurate monitoring of the gas-liquid two-phase |  dynamic characteristics of the flow with good |  Gaussian mixture model (GMM) is used to fit the data
关键词： gas-water two-phase flow |  status monitoring |  hidden Markov model |  genetic algorithm
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9. No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models NSTL国家科技图书文献中心

Yue Cao |  Nabil Magbool Jan... -  《IEEE transactions on automation science and engineering: a publication of the IEEE Robotics and Automation Society》 - 2023,20(1) - 167～178 - 共12页

摘要： principal component analysis (GMM-VBPCA) is proposed. GMM |  information where each Gaussian component of GMM represents | The primary goal of multimodal process |  monitoring is to detect abnormalities or occurrence of |  Gaussian mixture model based variational Bayesian
关键词： Process monitoring |  Principal component analysis |  Bayes methods |  Probabilistic logic |  Delay effects |  Sensitivity |  Gaussian distribution
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10. An MPPCA approach for anomaly detection of a retractable roof structure NSTL国家科技图书文献中心

Zhi MA |  Yaozhi LUO... -  《The 2022 Annual Symposium of the International Association for Shell and Spatial Structures & The 13th Asian-Pacific Conference on Shell and Spatial Structures》 -  Annual Symposium of the International Association for Shell and Spatial Structures |  Asian-Pacific Conference on Shell and Spatial Structures - 2022, - 840～851 - 共12页

摘要： a mixture of probabilistic principal component |  early warning of structural damage to existing civil |  analysis(MPPCA)for a retractable roof structure with |  healthy conditions,where the estimation of the MPPCA |  structural anomalies.The probability distributions of the
关键词： structural health monitoring |  retractable roof structure |  anomaly detection |  mixture of probabilistic principal component analysis |  missing data
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