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Found 12 papers, 2 papers with code
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments.
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization
The design process of centrifugal compressors requires applying an optimization process which is computationally expensive due to complex analytical equations underlying the compressor's dynamical equations.
Deep Surrogate of Modular Multi Pump using Active Learning
Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field.
Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm
Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements of the target states provided by one or multiple sensors.
Constrained Exploration and Recovery from Experience Shaping
We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties.
Reinforcement Learning Testbed for Power-Consumption Optimization
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management.
Systems and Control
Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection
Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data.
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series.
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World
While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment.
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
In the results section we demonstrate the features of the algorithm using MNIST handwritten digit dataset and latter apply the technique to a real-world obstacle detection problem.
Deep Reinforcement Learning for High Precision Assembly Tasks
High precision assembly of mechanical parts requires accuracy exceeding the robot precision.
