The Application of Machine Learning to One-Dimensional Problems of Mechanics of a Solid Deformable Body

2020 ◽  
pp. 67-76
Author(s):  
Viacheslav Reshetnikov ◽  
Andrea Tick
Keyword(s):  
Machine Learning ◽  
Deformable Body ◽  
One Dimensional

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Characterization of photoexcited states in the half-filled one-dimensional extended Hubbard model assisted by machine learning

2020 ◽  
Vol 101 (19) ◽  
Author(s):  
Kazuya Shinjo ◽  
Shigetoshi Sota ◽  
Seiji Yunoki ◽  
Takami Tohyama
Keyword(s):  
Machine Learning ◽  
Hubbard Model ◽  
Extended Hubbard Model ◽  
One Dimensional

1D-TICapsNet: An audio signal processing algorithm for bolt early looseness detection

2020 ◽  
pp. 147592172097698
Author(s):  
Furui Wang ◽  
Gangbing Song
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Audio Signal ◽  
Training Procedure ◽  
Audio Signal Processing ◽  
Signal Processing Algorithm ◽  
One Dimensional ◽  
Learning Techniques ◽  
Core Issue ◽  
Contact Sensors

Recently, for bolt looseness detection, percussion-based methods have attracted more attention due to their advantages of eliminating contact sensors. The core issue of percussion-based methods is audio signal processing to characterize different bolt preloads, while current percussion-based methods all depend on machine learning–based techniques that require hand-crafted features and overlook bolt looseness at the incipient stage. Thus, in this article, the main contribution is that we propose a novel one-dimensional training interference capsule neural network (1D-TICapsNet) to process and classify percussion-induced sound signals, thus achieving bolt early looseness detection. First, compared to machine learning–based techniques, 1D-TICapsNet can fuse feature extraction and classification in one frame to achieve better performance. In addition, due to two tricks (i.e. training interference), including wider kernels in the first convolutional layer and the targeted dropout technique, our proposed 1D-TICapsNet outperforms several state-of-the-art deep learning techniques in terms of classification accuracy, computational costs, and the denoising capacity. We call these two tricks as “training interference” since they work during training procedure. Finally, we confirm the effectiveness and superiorities of 1D-TICapsNet via experiments. Considering the efficacy of 1D-TICapsNet, we can expect its real-world applications on bolt early looseness detection and other classification of one-dimensional signals.

scholarly journals Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features

2021 ◽  
Vol 11 (20) ◽  
pp. 9460
Author(s):  
Heechang Lee ◽  
Taeyoung Yoon ◽  
Chaeyun Yeo ◽  
HyeonYoung Oh ◽  
Yebin Ji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmias ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Morphological Features ◽  
Machine Learning Algorithms ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
One Dimensional ◽  
Occurrence Matrix

The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.

scholarly journals Examining Political Trustworthiness through Text-Based Measures of Ideology

2019 ◽  
Vol 33 ◽  
pp. 10029-10030
Author(s):  
Eitan Sapiro-Gheiler
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Statistical Data ◽  
Empirical Support ◽  
Policy Space ◽  
Ideological Position ◽  
One Dimensional ◽  
Word Level ◽  
The Us ◽  
Average Position

This work shows the value of word-level statistical data from the US Congressional Record for studying the ideological positions and dynamic behavior of senators. Using classification techniques from machine learning, we predict senators’ party with near-perfect accuracy. We also develop text-based ideology scores to embed a politician’s ideological position in a one-dimensional policy space. Using these scores, we find that speech that diverges from voting positions may result in higher vote totals. To explain this behavior, we show that politicians use speech to move closer to their party’s average position. These results not only provide empirical support for political economy models of commitment, but also add to the growing literature of machine-learning-based text analysis in social science contexts.

scholarly journals Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model

2019 ◽  
Vol 88 (6) ◽  
pp. 065001 ◽  
Author(s):  
Kazuya Shinjo ◽  
Kakeru Sasaki ◽  
Satoru Hase ◽  
Shigetoshi Sota ◽  
Satoshi Ejima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phase Diagram ◽  
Hubbard Model ◽  
Learning Phase ◽  
Extended Hubbard Model ◽  
One Dimensional
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