A bi-contrast self-supervised learning framework for enhancing multi-label classification in Industrial Internet of Things
Abstract In the Industrial Internet of Things (IIoT), multi-label classification is challenging due
to limited labeled data, class imbalance, and the necessity to consider temporal and spatial …
to limited labeled data, class imbalance, and the necessity to consider temporal and spatial …
A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales
The solution purification process includes multiple continuous reactors. Setting the key
technical indicators of each reactor through global optimization is the prerequisite for …
technical indicators of each reactor through global optimization is the prerequisite for …
A simulation-aided approach to safety analysis of learning-enabled components in automated driving systems
Artificial Intelligence (AI) techniques through Learning-Enabled Components (LEC) are
widely employed in Automated Driving Systems (ADS) to support operation perception and …
widely employed in Automated Driving Systems (ADS) to support operation perception and …
[HTML][HTML] Adopting Graph Neural Networks to Analyze Human–Object Interactions for Inferring Activities of Daily Living
Human Activity Recognition (HAR) refers to a field that aims to identify human activities by
adopting multiple techniques. In this field, different applications, such as smart homes and …
adopting multiple techniques. In this field, different applications, such as smart homes and …
A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses
This paper presents a reconfigurable near-sensor anomaly detection processor to real-time
monitor the potential anomalous behaviors of amputees with limb prostheses. The processor …
monitor the potential anomalous behaviors of amputees with limb prostheses. The processor …
Integrating Self-Organizing Map and Graph Neural Networks to Detect Anomalies in Time-series Data
Anomaly detection is essential in Industrial Cyber-Physical Systems (ICPS) for monitoring
both system and environmental conditions. However, effective anomaly detection remains a …
both system and environmental conditions. However, effective anomaly detection remains a …
Amputee Gait Phase Recognition Using Multiple GMM-HMM
Gait analysis helps clinical assessment and achieves comfortable prosthetic designs for
lower limb amputees, in which accurate gait phase recognition is a key component …
lower limb amputees, in which accurate gait phase recognition is a key component …
Coding-based abnormal behavior differentiation approach for industrial systems
M Ramadan, F Abdollahi - Journal of Industrial Information Integration, 2024 - Elsevier
This paper deals with the problem of sensor faults isolation from overlapping un-stealthy
attacks based on coding sensor outputs for industrial systems represented by Lipschitz …
attacks based on coding sensor outputs for industrial systems represented by Lipschitz …
Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts
P Su, Y Wang, C Xiang, E Wendel… - … and System Health …, 2024 - ieeexplore.ieee.org
Prognostics and health management (PHM) is an engineering discipline that aims to
maintain system behaviour and function and ensure mission success, safety and …
maintain system behaviour and function and ensure mission success, safety and …
Leveraging Anomaly Detection for Affective Computing Applications
S Hamieh - 2024 - theses.hal.science
Recent technological advancements have paved the way for automation in various sectors,
from education to autonomous driving, collaborative robots, and customer service. This has …
from education to autonomous driving, collaborative robots, and customer service. This has …