Professor Jiamei Deng

Professor Leeds Beckett

  • Leeds

Jiamei Deng's research has been focused on data analytics, artificial intelligence, and control system design.

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Biography

Dr. Jiamei Deng joined Leeds Beckett University as a Professor in Artificial Intelligence, Control, and Energy in 2015. Her research has been focused on data analytics, artificial intelligence, and control system design for different applications, such as construction, biomedical engineering, civil engineering, automotive engineering, nuclear power plants, other energy systems. She is holding two EU patents. Jiamei is also the sole author of one monograph, the main author of three book chapters, and has authored /co-authored over one hundred papers including prestigious journals, such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transaction on Industrial Electronics, IEEE Transactions on Transportation Electrification.

Industry Expertise

Education/Learning
Research
Automotive

Areas of Expertise

Big Data
Artificial Intelligence
Control System Design
Energy System Efficiency
Energy System Safety

Affiliations

  • EPSRC Strategic Advisory Team in Engineering: Member
  • EPSRC Peer Review College : Member
  • Smart Energy Research Lab : Member
  • IEEE : Senior Member
  • Higher Education Academy : Fellow

Languages

  • English

Articles

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Sensors

2024

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images.

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Descriptor sliding mode observer based fault tolerant control for nuclear power plant with actuator and sensor faults

Progress in Nuclear Energy

2023

In sophisticated and complex system such as nuclear power plant, fault estimation and fault tolerant control always play an important role in maintaining the system stability and assuring satisfactory and safe operation. Thus, in this work a fault estimation and fault tolerant control scheme based on sliding mode theory is proposed for a pressurized water reactor type nuclear power plant considering simultaneous actuator and sensor faults. First, using descriptor sliding mode observer approach, an accurate estimation of the system states and sensor fault vector have been obtained simultaneously

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Artificial Intelligence Technique based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review

IEEE Sensors Journal

2023

Electric powertrain used in electric vehicles (EVs), which is constituted by motor, transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review contributes to comprehensively summarizing artificial intelligence (AI)-based/AI-supported approaches in EV powertrain condition monitoring and fault diagnosis that can be used in EV applications. The application of AI on PE in EV is a new attempt, which can solve many issues with better performance than traditional methods, and even achieve functions that the conventional methods cannot achieve.

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