Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,te...Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.展开更多
The deformation behaviors of a new quaternary Mg-6Zn-1.5Cu-0.5Zr alloy at temperatures of 523-673 K and strain rates of 0.001-1 s-1 were studied by compressive tests using a Gleeble 3800 thermal-simulator.The results ...The deformation behaviors of a new quaternary Mg-6Zn-1.5Cu-0.5Zr alloy at temperatures of 523-673 K and strain rates of 0.001-1 s-1 were studied by compressive tests using a Gleeble 3800 thermal-simulator.The results show that the flow stress increases as the deformation temperature decreases or as the strain rate increases.A strain-dependent constitutive equation and a feed-forward back-propagation artificial neural network were used to predict flow stress,which showed good agreement with experimental data.The processing map suggests that the domains of 643-673 K and 0.001-0.01 s-1 are corresponded to optimum conditions for hot working of the T4-treated Mg-6Zn-1.5Cu-0.5Zr alloy.展开更多
Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was esta...Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.展开更多
An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com...By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.展开更多
In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. Accor...In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.展开更多
文摘Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.
基金supported by the R&D Program of Korea Institute of Materials Sciencethe World Premier Materials Program funded by The Ministry of Knowledge Economy,Koreasupport from China Scholarship Council(CSC)
文摘The deformation behaviors of a new quaternary Mg-6Zn-1.5Cu-0.5Zr alloy at temperatures of 523-673 K and strain rates of 0.001-1 s-1 were studied by compressive tests using a Gleeble 3800 thermal-simulator.The results show that the flow stress increases as the deformation temperature decreases or as the strain rate increases.A strain-dependent constitutive equation and a feed-forward back-propagation artificial neural network were used to predict flow stress,which showed good agreement with experimental data.The processing map suggests that the domains of 643-673 K and 0.001-0.01 s-1 are corresponded to optimum conditions for hot working of the T4-treated Mg-6Zn-1.5Cu-0.5Zr alloy.
基金Project(2007CB613807)supported by the National Basic Research Program of ChinaProject(NCET-07-0696)supported by the New Century Excellent Talents in University,ChinaProject(35-TP-2009)supported by the Fund of the State Key Laboratory of Solidification Processing in Northwestern Polytechnical University,China
文摘Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
文摘By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.
基金The National Natural Science Foundation of China (59990470).
文摘In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.
