26 research outputs found
Brain Dynamic Information Flow Estimation Based on EEG and Diffusion MRI: A Proof-of-principle Study and Application in Stroke
No full textIn the hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Electroencephalography (EEG), with an excellent temporal resolution, can be used to reveal functional changes in the brain following a stroke. This study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which combines EEG, anatomical MRI and diffusion weighted imaging (DWI), to estimation brain dynamic information flow and its changes following a stroke. EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 88%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals, using matrices lateralization index and activation complexity. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.Mechanical Engineerin
An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process
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A novel approach for modeling neural responses to joint perturbations using the NARMAX method and a hierarchical neural network
No full textThe human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.Biomechatronics & Human-Machine Contro
Improving information sharing in Chinese hospitals with electronic medical record: The resource-based view and social capital theory perspective
No full textThis research implicates that in large Chinese hospitals, in the individual level, EMR is able to make clinicians get access to more sources of information and knowledge to increase their working efficiency and make the right decision; in organisational level, EMR helps Chinese hospitals achieve effective cross-boundary information sharing and integration and promotes organisational learning and organisational memory in these hospitals.Made available in DSpace on 2019-03-22T21:34:44Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-03-1
Torque Vectoring Control Strategies for Distributed Electric Drive Formula SAE Racing Car
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Unveiling Four Key Factors for Tire Force Control Allocation in 4WID-4WIS Electric Vehicles at Handling Limits
No full textThe four-wheel independent drive and four-wheel independent steering
(4WID-4WIS) configurations enhance control flexibility and dynamic performance
potential for more integrated electric vehicles. This paper comprehensively
analyzes the impacts of four key factors on tire force control allocation:
vertical load estimation, actuator dynamic characteristics, tire force
constraints, and wheel steering precision at handling limits. The study
demonstrates that precise vertical load estimation enhances lateral force
allocation accuracy. Additionally, the self-compensating effect of lateral tire
forces minimizes the impact of small deviations in vertical load estimation on
tire force control allocation. A novel control allocation method considering
actuator dynamics is introduced, effectively improving yaw rate response and
reducing tracking errors. Considering tire-road adhesion and actuator rate
constraints, an innovative method to calculate the real-time attainable tire
force volume is proposed based on the tire slip ratio and slip angle.
Feedforward control with bump steer compensation is implemented to improve
wheel steering precision and lateral tire force control accuracy.
Matlab/Simulink and Carsim co-simulation results emphasize the importance of
these key factors' individual impacts and combined effects. This analysis
offers valuable insights for developing advanced tire force control allocation
strategies in 4WID-4WIS electric vehicles
Design and realization of 3D NOC multicast router base on multicast rotational routing arithmetic
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