Data-driven Prognostic Model of Li-ion Battery with Deep Learning Algorithm

2019 ◽  
Author(s):  
Phattara Khumprom ◽  
Nita Yodo
Keyword(s):  
Deep Learning ◽  
Prognostic Model ◽  
Learning Algorithm ◽  
Data Driven ◽  
Li Ion Battery ◽  
Deep Learning Algorithm ◽  
Li Ion

scholarly journals Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line

2021 ◽  
Vol 11 (19) ◽  
pp. 8967
Author(s):  
Lin Song ◽  
Liping Wang ◽  
Jun Wu ◽  
Jianhong Liang ◽  
Zhigui Liu
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Condition Monitoring ◽  
Physical System ◽  
Production Line ◽  
Learning Algorithm ◽  
Data Driven ◽  
Equation Modeling ◽  
Cyber Physical System ◽  
Deep Learning Algorithm

In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model.

scholarly journals Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology images

2021 ◽  
Author(s):  
Mohsin Bilal ◽  
Shan E Ahmed Raza ◽  
Ayesha Azam ◽  
Simon Graham ◽  
Muhammad Ilyas ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Data Driven ◽  
Molecular Pathways ◽  
Cellular Composition ◽  
Genetic Pathways ◽  
Histological Features ◽  
Deep Learning Algorithm ◽  
The Status

SummaryBackgroundDetermining molecular pathways involved in the development of colorectal cancer (CRC) and knowing the status of key mutations are crucial for deciding optimal target therapy. The goal of this study is to explore machine learning to predict the status of the three main CRC molecular pathways – microsatellite instability (MSI), chromosomal instability (CIN), CpG island methylator phenotype (CIMP) – and to detect BRAF and TP53 mutations as well as to predict hypermutated (HM) CRC tumors from whole-slide images (WSIs) of colorectal cancer (CRC) slides stained with Hematoxylin and Eosin (H&E).MethodsWe propose a novel iterative draw-and-rank sampling (IDaRS) algorithm to select representative sub-images or tiles from a WSI given a single WSI-level label, without needing any detailed annotations at the cell or region levels. IDaRS is used to train a deep convolutional network for predicting key molecular parameters in CRC (in particular, prediction of HM tumors and the status of three main CRC molecular pathways – MSI, CIN, CIMP – as well as the detection of two key mutations, BRAF and TP53) from digitized images of routine H&E stained tissue slides of CRC patients (n=497 for TCGA cohort and n=47 cases for the Pathology AI Platform or PAIP cohort). Visual fields most predictive of each pathway and HM tumors identified by IDaRS are analyzed for verification of known histological features for the first time to reveal novel histological features. This is achieved by systematic, data-driven analysis of the cellular composition of strongly predictive tiles.FindingsIDaRS yields high prediction accuracy for prediction of the three main CRC genetic pathways and key mutations by deep learning based analysis of the WSIs of H&E stained slides. It achieves the state-of-the-art AUROC values of 0.90, 0.83, and 0.81 for prediction of the status of MSI, CIN, and HM tumors for the TCGA cohort, which is significantly higher than any other currently published methods on that cohort. We also report prediction of status of CIMP pathway (CIMP-High and CIMP-Low) from H&E slides, with an AUROC of 0.79. We analyzed key discriminative histological features associated with HM tumors and each molecular pathway in a data-driven manner, via an automated quantitative analysis of the cellular composition of tiles strongly predictive of the corresponding molecular status. A key feature of the proposed method is that it enables a systematic and data-driven analysis of the cellular composition of image tiles strongly predictive of the various molecular parameters. We found that relatively high proportion of tumor infiltrating lymphocytes and necrosis are found to be strongly associated with HM and MSI, and moderately associated with CIMP-H and genome-stable (GS) cases, whereas relatively high proportions of neoplastic epithelial type 2 (NEP2), mesenchymal and neoplastic epithelial type 1 (NEP1) cells are found to be associated with CIN cases.InterpretationAutomated prediction of genetic pathways and key mutations from image analysis of simple H&E stained sections with a high accuracy can provide time and cost-effective decision support. This work shows that a deep learning algorithm can mine both visually recognizable as well as sub-visual histological patterns associated with molecular pathways and key mutations in CRC in a data-driven manner.FundingThis study was funded by the UK Medical Research Council (award MR/P015476/1).

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