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Found 23 papers, 2 papers with code
Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples.
Vision Transformers in Domain Adaptation and Generalization: A Study of Robustness
Motivated by the increased interest from the research community, our paper investigates the deployment of vision transformers in domain adaptation and domain generalization scenarios.
Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking
Conventional prediction methods in DBT utilize Kalman Filter(KF) to extrapolate the target location in the upcoming frames by supposing a constant velocity motion model.
Extended Reality for Enhanced Human-Robot Collaboration: a Human-in-the-Loop Approach
The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization.
A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
This paper addresses a production scheduling problem derived from an industrial use case, focusing on unrelated parallel machine scheduling with the personnel availability constraint.
Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization
The advantage of the Safety Optimized RL (SORL) algorithm compared to the traditional Safe RL algorithms is that it omits the need to constrain the policy search space.
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
This paper provides an overview of intelligent condition monitoring and fault detection and diagnosis methods for industrial plants with a focus on the open-source benchmark Tennessee Eastman Process (TEP).
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification
The objective of this paper is to study Conformal Prediction, an emerging distribution-free uncertainty quantification technique, and provide a comprehensive understanding of the advantages and limitations inherent in various methods within the medical imaging field.
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
This paper serves as a valuable resource for researchers to assess the current state of DRL-based machine scheduling and identify research gaps.
Gap and Overlap Detection in Automated Fiber Placement
The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts produced through Automated Fiber Placement (AFP).
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
This work proposed an encoder-transformer architecture-based framework for multivariate time series prediction for a prognostics use case.
Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability.
Model Compression Methods for YOLOv5: A Review
This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.
Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration
Hand gestures recognition which has been a topic of much research and subsequent commercialization in the real world has been possible because of the creation of large, labelled datasets.
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations
The framework employs blob detection on this map to locate manufacturing defects.
Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction
UAV-based intelligent data acquisition for 3D reconstruction and monitoring of infrastructure has experienced an increasing surge of interest due to recent advancements in image processing and deep learning-based techniques.
Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization
Adopted methodologies, findings, and well-defined interpretations of the reviewed studies were summarized in graphical and tabular representations to maximize the utility of the work for both researchers and practitioners.
Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion
Depth completion and object detection are two crucial tasks often used for aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial Vehicles (UAVs).
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation.
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications.
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives
Our contribution is introducing a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the DMP framework.
Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN
The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions.
