Search Results - bing+zhang

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Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis
Reference #: 01603 The University of South Carolina is offering licensing opportunities for Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis Background: Accurate and efficient modeling of battery degradation is of great challenge and is becoming more and more complex for batteries in modern applications. Traditional degradation...
Published: 5/16/2023   |   Inventor(s): Bin Zhang, Guangxing Niu
Keywords(s): Deep belief network, Diagnosis and prognosis, Fault dynamic model, Lebesgue sampling, Lithium-ion battery, Particle filter, Uncertainty management
Category(s): Engineering and Physical Sciences, Energy
Hybrid Rotating Machinery Fault Diagnosis and Prognosis
Reference #: 01570 The University of South Carolina is offering licensing opportunities for Hybrid Rotating Machinery Fault Diagnosis and Prognosis Background: Bearing faults are the top contributor to the failure of rotating machinery systems. In wind energy systems, about 80% of gearbox failures are caused by bearing faults. According to verified...
Published: 8/22/2024   |   Inventor(s): Guangxing Niu, Bin Zhang
Keywords(s): Continuous wavelet transform, convolutional neural network, Fault model selection, Particle filter, Rotating machinery systems, STP estimation
Category(s): Engineering and Physical Sciences
DRCNN for Multi-task Bearing Fault Diagnosis with Information Fusion
Reference #: 01571 The University of South Carolina is offering licensing opportunities for DRCNN for Multi-task Bearing Fault Diagnosis with Information Fusion Background: First, most industrial systems are working in variable operating conditions and environments. The information of operating conditions, such as load profile, rotating speed, and...
Published: 8/22/2024   |   Inventor(s): Guangxing Niu, Bin Zhang
Keywords(s): Bearing, Deep residual convolutional neural network, Discriminate Feature Learning, Information Fusion, Multi-task Fault Diagnosis
Category(s): Engineering and Physical Sciences
Method for 3D Nonlinear Structured Illumination Super-resolution Imaging
Researchers at the University of Arizona have developed a super-resolution microscopy method that is faster, easier to use, and has less artifacts than current super-resolution methods. The result is a 3D dual-color stimulated emission depletion (STED) nonlinear structured illumination (NL-SIM) microscope. Using a combination of low coherent light and...
Published: 4/3/2023   |   Inventor(s): Leilei Peng, Yu Li, Han Zhang
Keywords(s):  
Category(s): Technology Classifications > Imaging & Optics, Technology Classifications > Imaging & Optics > Medical Imaging, Technology Classifications > Imaging & Optics > Microscopy, Spectroscopy, Polarimetry
Lithium-ion battery health management based on single particle model
Reference #: 01494 The University of South Carolina is offering licensing opportunities for Lithium-ion battery health management based on single particle model Background: A single particle model is used in simulating the behavior of lithium-ion battery. Particle swarm optimization is used to identify the parameters of the single particle model....
Published: 9/3/2022   |   Inventor(s): Guangxing Niu, Bin Zhang
Keywords(s): Bayesian approach, Lebesgue sampling, Particle swarm optimization, Single particle model, State of charge, State of health
Category(s): Energy, Engineering and Physical Sciences
Li-ion-Battery State-of-charge diagnosis-prognosis based on Lebesgue-sampling equivalent-circuit-model
Reference #: 01527 The University of South Carolina is offering licensing opportunities for Li-ion-Battery State-of-charge diagnosis-prognosis based on Lebesgue-sampling equivalent-circuit-model. Background: Traditional SOC estimation and prediction is mainly based on the electrochemical model (EM) or ECM of Li-battery. The EM method has a high computation...
Published: 1/26/2023   |   Inventor(s): Bin Zhang, Enhui Liu
Keywords(s): accuracy, computation, efficiency, Equivalent circuit model, Lebesgue sampling, open circuit voltage, SOC diagnostics and prognostics
Category(s): Energy
Lebesgue-sampling-based battery whole-service-life SOC estimation using simplified first principle model
Reference #: 01493 The University of South Carolina is offering licensing opportunities for Lebesgue-sampling-based battery whole-service-life SOC estimation using simplified first principle model Background: Traditional state of health and state of charge estimation is mainly based on the electrochemical model or equivalent circuit model of Li-battery....
Published: 7/17/2023   |   Inventor(s): Enhui Liu, Bin Zhang
Keywords(s): accuracy, computation, Lebesgue sampling, Simplified first principle model, SOC estimation
Category(s): Engineering and Physical Sciences, Energy
Motor Winding Insulation Diagnosis and Prognosis Using Resistance Simulation Method
Reference #: 01489 The University of South Carolina is offering licensing opportunities for Motor winding insulation diagnosis and prognosis using resistance simulation method Background: The purpose of building Permanent Magnet Synchronous Motor (PMSM) model is to enable the winding insulation fault injection in motor, along with fault (clogging)...
Published: 9/3/2022   |   Inventor(s): Bin Zhang, Enhui Liu
Keywords(s): Diagnosis and prognosis, Equivalent resistance method, Sampling method, Transient dynamic performance
Category(s): Engineering and Physical Sciences, Advanced Materials
Sensor Signal Prediction at Unreported Frequencies
The Internet of Things (IoT) systems are increasingly complex as people continually prefer more information and automation. This trend has been growing in manufacturing, home safety, and building automation. One of the major realities faced by IoT systems is an overwhelming amount of data coming from sensors. Not only that, but the cost of having all...
Published: 8/1/2024   |   Inventor(s): Mohammed Shafae, Bing Zhang, Asthana Shubhi, Aly Megahed, Alaa Elwany
Keywords(s):  
Category(s): Technology Classifications > Engineering & Physical Sciences > Communications & Networking > Networking, Technology Classifications > Engineering & Physical Sciences > Communications & Networking > Wireless
Iterative Feedback Motion Planning
Reference #: 01394 The University of South Carolina is offering licensing opportunities for Iterative Feedback Motion Planning Background: In an autonomous driving system (ADS), the motion planning module is responsible for generating a motion trajectory for the motion controller. The motion controller module attempts to drive the vehicle while following...
Published: 9/4/2022   |   Inventor(s): Bin Zhang, Zhichao Liu, Kai Zhang
Keywords(s): feedback planning, Iterative learning, motion control, motion planning
Category(s): Engineering and Physical Sciences
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