Machine Learning: Science and Technology最新文献_第7页

Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion 利用自动微分对基于模型诊断的参数估计进行定性和定量改进，并将其应用于惯性融合

Machine Learning: Science and Technology Pub Date : 2024-02-13 DOI: 10.1088/2632-2153/ad2493

A. Milder, A. S. Joglekar, W. Rozmus, D. H. Froula

{"title":"Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion","authors":"A. Milder, A. S. Joglekar, W. Rozmus, D. H. Froula","doi":"10.1088/2632-2153/ad2493","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2493","url":null,"abstract":"\u0000 Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require \u0000 \u0000 \u0000 \u0000 O\u0000 \u0000 (\u0000 \u0000 10\u0000 3\u0000 \u0000 )\u0000 \u0000 \u0000 parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging the Gap Between High-Level Quantum Chemical Methods and Deep Learning Models 缩小高层量子化学方法与深度学习模型之间的差距

Machine Learning: Science and Technology Pub Date : 2024-02-09 DOI: 10.1088/2632-2153/ad27e1

Viki Kumar Prasad, Alberto Otero-de-la-Roza, G. Dilabio

{"title":"Bridging the Gap Between High-Level Quantum Chemical Methods and Deep Learning Models","authors":"Viki Kumar Prasad, Alberto Otero-de-la-Roza, G. Dilabio","doi":"10.1088/2632-2153/ad27e1","DOIUrl":"https://doi.org/10.1088/2632-2153/ad27e1","url":null,"abstract":"\u0000 Supervised deep learning (DL) models are becoming ubiquitous in computational chemistry because they can efficiently learn complex input-output relationships and predict chemical properties at a cost significantly lower than methods based on quantum mechanics. The central challenge in many deep learning applications is the need to invest considerable computational resources in generating large (N > 1e5) training sets such that the resulting DL model can be generalized reliably to unseen systems. The lack of better alternatives has encouraged the use of low-cost and relatively inaccurate density-functional theory (DFT) methods to generate training data, leading to DL models that lack accuracy and reliability. In this article, we describe a robust and easily implemented approach based on property-specific atom-centered potentials (ACPs) that resolves this central challenge in DL model development. ACPs are one-electron potentials that are applied in combination with a cheap but inaccurate quantum mechanical method (e.g. double-$zeta$ DFT) and fitted against relatively few high-level data ($N approx num{1e3}$--$num{1e4}$), possibly obtained from the literature. The resulting ACP-corrected methods retain the low cost of the double-$zeta$ DFT approach, while generating high-level-quality data in unseen systems for the specific property for which they were designed. With this approach, we demonstrate that ACPs can be used as an intermediate method between high-level approaches and DL model development, enabling the calculation of large and accurate DL training sets for the chemical property of interest. We demonstrate the effectiveness of the proposed approach by predicting bond dissociation enthalpies, reaction barrier heights, and reaction energies with chemical accuracy at a computational cost lower than the DFT methods routinely used for DL training data set generation.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"246 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging the Gap Between High-Level Quantum Chemical Methods and Deep Learning Models 缩小高层量子化学方法与深度学习模型之间的差距

Machine Learning: Science and Technology Pub Date : 2024-02-09 DOI: 10.1088/2632-2153/ad27e1

Viki Kumar Prasad, Alberto Otero-de-la-Roza, G. Dilabio

{"title":"Bridging the Gap Between High-Level Quantum Chemical Methods and Deep Learning Models","authors":"Viki Kumar Prasad, Alberto Otero-de-la-Roza, G. Dilabio","doi":"10.1088/2632-2153/ad27e1","DOIUrl":"https://doi.org/10.1088/2632-2153/ad27e1","url":null,"abstract":"\u0000 Supervised deep learning (DL) models are becoming ubiquitous in computational chemistry because they can efficiently learn complex input-output relationships and predict chemical properties at a cost significantly lower than methods based on quantum mechanics. The central challenge in many deep learning applications is the need to invest considerable computational resources in generating large (N > 1e5) training sets such that the resulting DL model can be generalized reliably to unseen systems. The lack of better alternatives has encouraged the use of low-cost and relatively inaccurate density-functional theory (DFT) methods to generate training data, leading to DL models that lack accuracy and reliability. In this article, we describe a robust and easily implemented approach based on property-specific atom-centered potentials (ACPs) that resolves this central challenge in DL model development. ACPs are one-electron potentials that are applied in combination with a cheap but inaccurate quantum mechanical method (e.g. double-$zeta$ DFT) and fitted against relatively few high-level data ($N approx num{1e3}$--$num{1e4}$), possibly obtained from the literature. The resulting ACP-corrected methods retain the low cost of the double-$zeta$ DFT approach, while generating high-level-quality data in unseen systems for the specific property for which they were designed. With this approach, we demonstrate that ACPs can be used as an intermediate method between high-level approaches and DL model development, enabling the calculation of large and accurate DL training sets for the chemical property of interest. We demonstrate the effectiveness of the proposed approach by predicting bond dissociation enthalpies, reaction barrier heights, and reaction energies with chemical accuracy at a computational cost lower than the DFT methods routinely used for DL training data set generation.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems 连续动态系统的数据驱动谎言点对称性检测

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad2629

Alex Gabel, Rick Quax, E. Gavves

{"title":"Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems","authors":"Alex Gabel, Rick Quax, E. Gavves","doi":"10.1088/2632-2153/ad2629","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2629","url":null,"abstract":"\u0000 Symmetry detection, the task of discovering the underlying symmetries of a given dataset, has been gaining popularity in the machine learning community, particularly in science and engineering applications. Most previous works focus on detecting \"canonical\" symmetries such as translation, scaling, and rotation, and cast the task as a modeling problem involving complex inductive biases and architecture design of neural networks. We challenge these assumptions and propose that instead of constructing biases, we can learn to detect symmetries from raw data without prior knowledge. The approach presented in this paper provides a flexible way to scale up the detection procedure to non-canonical symmetries, and has the potential to detect both known and unknown symmetries alike. Concretely, we focus on predicting the generators of Lie point symmetries of PDEs, more specifically, evolutionary equations for ease of data generation. Our results demonstrate that well-established neural network architectures are capable of recognizing symmetry generators, even in unseen dynamical systems. These findings have the potential to make non-canonical symmetries more accessible to applications, including model selection, sparse identification, and data interpretability.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"40 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas 从模拟湍流气体密度图深度学习宇宙射线传输

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad262a

Chad Bustard, John Wu

{"title":"Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas","authors":"Chad Bustard, John Wu","doi":"10.1088/2632-2153/ad262a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad262a","url":null,"abstract":"\u0000 The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines -- for instance, diffusive vs streaming transport models -- is an unsolved challenge. Leveraging a recent training set of magnetohydrodynamic turbulent box simulations, with CRs spanning a range of transport parameters, we use convolutional neural networks (CNNs) trained solely on gas density maps to classify CR transport regimes. We find that even relatively simple CNNs can quite effectively classify density slices to corresponding CR transport parameters, distinguishing between streaming and diffusive transport, as well as magnitude of diffusivity, with class accuracies between 92% and 99%. As we show, the transport-dependent imprints that CRs leave on the gas are not all tied to the resulting density power spectra: classification accuracies are still high even when image spectra are flattened (85% to 98% accuracy), highlighting CR transport-dependent changes to turbulent phase information. We interpret our results with saliency maps and image modifications, and we discuss physical insights and future applications.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"18 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas 从模拟湍流气体密度图深度学习宇宙射线传输

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad262a

Chad Bustard, John Wu

{"title":"Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas","authors":"Chad Bustard, John Wu","doi":"10.1088/2632-2153/ad262a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad262a","url":null,"abstract":"\u0000 The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines -- for instance, diffusive vs streaming transport models -- is an unsolved challenge. Leveraging a recent training set of magnetohydrodynamic turbulent box simulations, with CRs spanning a range of transport parameters, we use convolutional neural networks (CNNs) trained solely on gas density maps to classify CR transport regimes. We find that even relatively simple CNNs can quite effectively classify density slices to corresponding CR transport parameters, distinguishing between streaming and diffusive transport, as well as magnitude of diffusivity, with class accuracies between 92% and 99%. As we show, the transport-dependent imprints that CRs leave on the gas are not all tied to the resulting density power spectra: classification accuracies are still high even when image spectra are flattened (85% to 98% accuracy), highlighting CR transport-dependent changes to turbulent phase information. We interpret our results with saliency maps and image modifications, and we discuss physical insights and future applications.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"25 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems 连续动态系统的数据驱动谎言点对称性检测

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad2629

Alex Gabel, Rick Quax, E. Gavves

{"title":"Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems","authors":"Alex Gabel, Rick Quax, E. Gavves","doi":"10.1088/2632-2153/ad2629","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2629","url":null,"abstract":"\u0000 Symmetry detection, the task of discovering the underlying symmetries of a given dataset, has been gaining popularity in the machine learning community, particularly in science and engineering applications. Most previous works focus on detecting \"canonical\" symmetries such as translation, scaling, and rotation, and cast the task as a modeling problem involving complex inductive biases and architecture design of neural networks. We challenge these assumptions and propose that instead of constructing biases, we can learn to detect symmetries from raw data without prior knowledge. The approach presented in this paper provides a flexible way to scale up the detection procedure to non-canonical symmetries, and has the potential to detect both known and unknown symmetries alike. Concretely, we focus on predicting the generators of Lie point symmetries of PDEs, more specifically, evolutionary equations for ease of data generation. Our results demonstrate that well-established neural network architectures are capable of recognizing symmetry generators, even in unseen dynamical systems. These findings have the potential to make non-canonical symmetries more accessible to applications, including model selection, sparse identification, and data interpretability.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"1 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Energy-Pressure Regression for a Thermodynamically Consistent EOS Model 热力学一致的 EOS 模型的深度能量-压力回归

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad2626

Dayou Yu, Deep Shankar Pandey, J. Hinz, D. Mihaylov, Valentin V. Karasiev, Suxing Hu, Qi Yu

{"title":"Deep Energy-Pressure Regression for a Thermodynamically Consistent EOS Model","authors":"Dayou Yu, Deep Shankar Pandey, J. Hinz, D. Mihaylov, Valentin V. Karasiev, Suxing Hu, Qi Yu","doi":"10.1088/2632-2153/ad2626","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2626","url":null,"abstract":"\u0000 In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal Equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"24 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Energy-Pressure Regression for a Thermodynamically Consistent EOS Model 热力学一致的 EOS 模型的深度能量-压力回归

Machine Learning: Science and Technology Pub Date : 2024-02-05 DOI: 10.1088/2632-2153/ad2626

Dayou Yu, Deep Shankar Pandey, J. Hinz, D. Mihaylov, Valentin V. Karasiev, Suxing Hu, Qi Yu

{"title":"Deep Energy-Pressure Regression for a Thermodynamically Consistent EOS Model","authors":"Dayou Yu, Deep Shankar Pandey, J. Hinz, D. Mihaylov, Valentin V. Karasiev, Suxing Hu, Qi Yu","doi":"10.1088/2632-2153/ad2626","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2626","url":null,"abstract":"\u0000 In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal Equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"85 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Cross-Subject Classification Performance of Motor Imagery Signals: A Data Augmentation-focused Deep Learning Framework 提高运动图像信号的跨主体分类性能：以数据增强为重点的深度学习框架

Machine Learning: Science and Technology Pub Date : 2024-01-18 DOI: 10.1088/2632-2153/ad200c

Muhammed Enes Ozelbas, E. Tülay, Serhat Ozekes

{"title":"Improving Cross-Subject Classification Performance of Motor Imagery Signals: A Data Augmentation-focused Deep Learning Framework","authors":"Muhammed Enes Ozelbas, E. Tülay, Serhat Ozekes","doi":"10.1088/2632-2153/ad200c","DOIUrl":"https://doi.org/10.1088/2632-2153/ad200c","url":null,"abstract":"\u0000 Motor Imagery Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data. In the context of MI-BCIs, with limited data availability, the acquisition of EEG data can be difficult. In this study, several data augmentation techniques have been compared with the proposed data augmentation technique Adaptive Cross-Subject Segment Replacement (ACSSR). This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on Common Spatial Patterns (CSP) with a sliding window and a parallel two-branch Convolutional Neural Network (CNN). The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10- fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.46%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"114 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0