Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor

Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the issue of inconsistency between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies at current time step. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor sensor data forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie

Transcriptome-wide association study reveals novel susceptibility genes for coronary atherosclerosis

BackgroundGenetic risk factors substantially contributed to the development of coronary atherosclerosis. Genome-wide association study (GWAS) has identified many risk loci for coronary atherosclerosis, but the translation of these loci into therapeutic targets is limited for their location in non-coding regions. Here, we aimed to screen the potential coronary atherosclerosis pathogenic genes expressed though TWAS (transcriptome wide association study) and explore the underlying mechanism association.MethodsFour TWAS approaches (PrediXcan, JTI, UTMOST, and FUSION) were used to screen genes associated with coronary atherosclerosis. Enrichment analysis of TWAS-identified genes was applied through the Metascape website. The summary-data-based Mendelian randomization (SMR) analysis was conducted to provide the evidence of causal relationship between the candidate genes and coronary atherosclerosis. At last, the cell type-specific expression of the intersection genes was examined by using human coronary artery single-cell RNA-seq, interrogating the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity.ResultsWe identified 19 genes by at least three approaches and 1 gene (NBEAL1) by four approaches. Enrichment analysis enriching the genes identified at least by two TWAS approaches, suggesting that these genes were markedly enriched in asthma and leukocyte mediated immunity reaction. Further, the summary-data-based Mendelian randomization (SMR) analysis provided the evidence of causal relationship between NBEAL1 gene and coronary atherosclerosis, confirming the protecting effects of NBEAL1 gene and coronary atherosclerosis. At last, the single cell cluster analysis demonstrated that NBEAL1 gene has differential expressions in macrophages, plasma cells and endothelial cells.ConclusionOur study identified the novel genes associated with coronary atherosclerosis and suggested the potential biological function for these genes, providing insightful guidance for further biological investigation and therapeutic approaches development in atherosclerosis-related diseases

Effect of temperature on texture and sensory properties of butter : A Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Food Innovation at Lincoln University

Abstract withheld for embargo reaso

Cross-Perspective Human Behavior Recognition Based on a Joint Sparse Representation and Distributed Adaptation Algorithm Combined with Wireless Optical Transmission

Traditional human behavior recognition needs many training samples. Signal transmission of images and videos via visible light in the body is crucial for detecting specific actions to accelerate behavioral recognition. Joint sparse representation techniques improve identification accuracy by utilizing multi-perspective information, while distributional adaptive techniques enhance robustness by adjusting feature distributions between different perspectives. Combining both techniques enhances recognition accuracy and robustness, enabling efficient behavior recognition in complex environments with multiple perspectives. In this paper, joint sparse representation has been combined with distributed adaptation algorithm to recognize human behavior under the fusion algorithm, and verify the feasibility of the fusion algorithm through experimental analysis. The research objective of this article is to explore the use of the combination of joint sparse representation technology and distributed adaptive technology in the recall and accuracy of human detection, combined with the cross perspective human behavior recognition of wireless optical transmission. The experimental results showed that in the process of human detection, the recall and precision of the fusion algorithm in this paper reached 92% and 90% respectively, which are slightly higher than the comparison algorithm. In the experiment of recognition accuracy of different actions, the recognition accuracy of the fusion algorithm in this paper was also higher than that of the control algorithm. It can be seen that the fusion of joint sparse representation and distributed adaptation algorithms, as well as wireless communication light technology, are of great significance for human behavior recognition

Dishevelled promotes wnt receptor degradation through recruitment of znrf3/rnf43 e3ubiquitin ligases

Tumor suppressors ZNRF3 and RNF43 inhibit Wnt signaling through promoting degradation of Wnt coreceptors Frizzled (FZD) and LRP6, and this activity is counteracted by stem cell growth factor R-spondin. The mechanism by which ZNRF3 and RNF43 recognize Wnt receptors remains unclear. Here we uncover an unexpected role of Dishevelled (DVL), a positive Wnt regulator, in promoting Wnt receptor degradation. DVL knockout cells have significantly increased cell surface levels of FZD and LRP6. DVL is required for ZNRF3/RNF43-mediated ubiquitination and degradation of FZD. Physical interaction with DVL is essential for the Wnt inhibitory activity of ZNRF3/RNF43. Binding of FZD through the DEP domain of DVL is required for DVL-mediated downregulation of FZD. Fusion of the DEP domain to ZNRF3/RNF43 overcomes their DVL dependency to downregulate FZD. Our study reveals DVL as a dual function adaptor to recruit negative regulators ZNRF3/RNF43 to Wnt receptors to ensure proper control of pathway activity