Early detection of anomalous events in automated processes within industrial scenarios helps to improve service smoothness,thus becoming critical and urgent.Despite this vision,prior works face challenges in convergen...Early detection of anomalous events in automated processes within industrial scenarios helps to improve service smoothness,thus becoming critical and urgent.Despite this vision,prior works face challenges in convergence on noisy training materials and insufficient construction of spatial-temporal dependencies,leading to performance limitations.In this work,we propose Spectra,a flexible framework for time-series anomaly detection in industrial scenarios.We employ a pair of parallel memory modules in the generative model to store and purify spatial and temporal knowledge in latent embeddings.As such,Spectra offsets the impact of noise and anomalous components in training materials,and signifies the difference between normals and anomalies.To dynamically integrate cross-domain information,we design an embedding fusion mechanism that comprises an agent attention module and a contrastive embedding alignment technique.This mechanism bridges embeddings from instantiated memory modules,aligns dependencies,and improves the organization of the latent space.Extensive experiments on three large-scale industrial datasets demonstrate Spectra’s effectiveness,with an average F1-Score of 0.9083 outperforming the baselines.展开更多
This study aimed to predict the moments at the hip,knee,and ankle joints in multiple planes during various movements.A deep learning model was developed using a bidirectional long short-term memory network(BiLSTM)comb...This study aimed to predict the moments at the hip,knee,and ankle joints in multiple planes during various movements.A deep learning model was developed using a bidirectional long short-term memory network(BiLSTM)combined with an agent attention mechanism(AA).Multimodal data were collected from 20 young subjects,including anthropometric data,joint angles,electromyographic signals,and ground reaction forces.The corresponding joint moments were calculated using Anybody motion simulation software.These data were used as input to the BiLSTM-AA model for joint moment prediction.Different input combinations and dimensionality reduction methods were compared.The best results were obtained by integrating anthropometric data,joint angles,and ground reaction forces.In cross-subject tests,the model showed high accuracy,with a mean absolute error of 0.0395 Nm/kg,root mean square error of 0.0579 Nm/kg,and a coefficient of determination of 0.9117.The model also performed well after reducing input dimensions.In summary,the BiLSTM-AA model predicts lower limb joint moments accurately across activities.This may simplify real-world data collection and help provide solid evidence for rehabilitation planning and assessment.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.2023YJS009the National Natural Science Foundation of China(Nos.62472023,62402027,and 62072029)+2 种基金the Natural Science Foundation of Beijing Municipality(No.L221003)the Beijing Nova Program(Nos.20230484263 and 20240484607)the DiDi Research Collaboration Plan.
文摘Early detection of anomalous events in automated processes within industrial scenarios helps to improve service smoothness,thus becoming critical and urgent.Despite this vision,prior works face challenges in convergence on noisy training materials and insufficient construction of spatial-temporal dependencies,leading to performance limitations.In this work,we propose Spectra,a flexible framework for time-series anomaly detection in industrial scenarios.We employ a pair of parallel memory modules in the generative model to store and purify spatial and temporal knowledge in latent embeddings.As such,Spectra offsets the impact of noise and anomalous components in training materials,and signifies the difference between normals and anomalies.To dynamically integrate cross-domain information,we design an embedding fusion mechanism that comprises an agent attention module and a contrastive embedding alignment technique.This mechanism bridges embeddings from instantiated memory modules,aligns dependencies,and improves the organization of the latent space.Extensive experiments on three large-scale industrial datasets demonstrate Spectra’s effectiveness,with an average F1-Score of 0.9083 outperforming the baselines.
基金supported by National Natural Science Foundation of China(grant numbers 11772214)funding from the Shanxi Huajin Orthopaedic Public Foundation(2024).
文摘This study aimed to predict the moments at the hip,knee,and ankle joints in multiple planes during various movements.A deep learning model was developed using a bidirectional long short-term memory network(BiLSTM)combined with an agent attention mechanism(AA).Multimodal data were collected from 20 young subjects,including anthropometric data,joint angles,electromyographic signals,and ground reaction forces.The corresponding joint moments were calculated using Anybody motion simulation software.These data were used as input to the BiLSTM-AA model for joint moment prediction.Different input combinations and dimensionality reduction methods were compared.The best results were obtained by integrating anthropometric data,joint angles,and ground reaction forces.In cross-subject tests,the model showed high accuracy,with a mean absolute error of 0.0395 Nm/kg,root mean square error of 0.0579 Nm/kg,and a coefficient of determination of 0.9117.The model also performed well after reducing input dimensions.In summary,the BiLSTM-AA model predicts lower limb joint moments accurately across activities.This may simplify real-world data collection and help provide solid evidence for rehabilitation planning and assessment.
