Professor Jiamei Deng
Professor | Leeds Beckett
Leeds, UNITED KINGDOM
Jiamei Deng's research has been focused on data analytics, artificial intelligence, and control system design.
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 (3)
Education/Learning
Research
Automotive
Areas of Expertise (5)
Big Data
Artificial Intelligence
Control System Design
Energy System Efficiency
Energy System Safety
Affiliations (5)
- EPSRC Strategic Advisory Team in Engineering: Member
- EPSRC Peer Review College : Member
- Smart Energy Research Lab : Member
- IEEE : Senior Member
- Higher Education Academy : Fellow
Links (3)
Languages (1)
- English
Articles (5)
HyperVein: A Hyperspectral Image Dataset for Human Vein Detection
Sensors2024 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.
Descriptor sliding mode observer based fault tolerant control for nuclear power plant with actuator and sensor faults
Progress in Nuclear Energy2023 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
Artificial Intelligence Technique based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review
IEEE Sensors Journal2023 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.
Inferable deep distilled attention network for diagnosing multiple motor bearing faults
IEEE Transactions on Transportation Electrification2022 Bearing, as a vital component in electric powertrains, is increasingly used globally, such as in electric vehicle (EV). Their damages and faults may bring huge cost loss to the industry and even threaten personal safety. This article proposes an inferable deep distilled attention network (IDDAN) method, which is a self-attention (SA) mechanism and a transfer learning-based method to diagnose and classify multiple bearing faults in various motor drive systems efficiently and accurately.
Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review
IEEE Transactions on Transportation Electrification2021 The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis (FDD) of the motor drive system. This article reviews the application of AI techniques in motor FDD in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed...
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