In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high...Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high-throughput prediction of material properties,offering a compelling enhancement and alternative to traditional first-principles calculations.While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy,such approaches often lack physical interpretability and insights into materials behavior.Here,we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression(SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression.Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintainingO(n)computational scaling.The integratedSRmodule subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships,achieving 23×acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input.This work suggests a new framework in computational materials science,bridging the gap between predictive accuracy and physical interpretability,offering valuable physical insights into material behavior.展开更多
Digital Twin(DT)is used for lifetime monitoring of the drive train and can be a costly option.This proposal adopts the predictive modelling of wind turbines by digital twins by deep learning strategies.Initially,the d...Digital Twin(DT)is used for lifetime monitoring of the drive train and can be a costly option.This proposal adopts the predictive modelling of wind turbines by digital twins by deep learning strategies.Initially,the data is acquired from publicly available wind turbine datasets.Next,the deep features and statistical features are extracted,and the autoencoder is adapted to get the deep features.Then,the Enhanced Marine Predators Algorithm(EMPA)is to select the optimal weighted fused features,where the EMPA would tune the weights used for fusion and the features selection.Finally,the predictive modelling is done via a newly recommended Adaptive Deep Temporal Convolution Network with an Attention Mechanism(ADTCN-AM).It is tuned for precise outcomes with the help of EMPA for forecasting the wind speed and predicting the generated power.The comparative performance analysis of the recently used wind prediction system model shows better efficient results.展开更多
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
基金supported by National Natural Science Foundation of China (No.12374057)Fundamental Research Funds for the Central Universities. The work (S.T.) at Los Alamos National Laboratory (LANL) was performed at the Center for Integrated Nanotechnologies (CINT), a U.S. Department of Energy, Office of Science user facility at LANL.
文摘Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high-throughput prediction of material properties,offering a compelling enhancement and alternative to traditional first-principles calculations.While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy,such approaches often lack physical interpretability and insights into materials behavior.Here,we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression(SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression.Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintainingO(n)computational scaling.The integratedSRmodule subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships,achieving 23×acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input.This work suggests a new framework in computational materials science,bridging the gap between predictive accuracy and physical interpretability,offering valuable physical insights into material behavior.
文摘Digital Twin(DT)is used for lifetime monitoring of the drive train and can be a costly option.This proposal adopts the predictive modelling of wind turbines by digital twins by deep learning strategies.Initially,the data is acquired from publicly available wind turbine datasets.Next,the deep features and statistical features are extracted,and the autoencoder is adapted to get the deep features.Then,the Enhanced Marine Predators Algorithm(EMPA)is to select the optimal weighted fused features,where the EMPA would tune the weights used for fusion and the features selection.Finally,the predictive modelling is done via a newly recommended Adaptive Deep Temporal Convolution Network with an Attention Mechanism(ADTCN-AM).It is tuned for precise outcomes with the help of EMPA for forecasting the wind speed and predicting the generated power.The comparative performance analysis of the recently used wind prediction system model shows better efficient results.
