Search Results for author: Giovanni De Magistris

Found 12 papers, 2 papers with code

Controllable Image Synthesis of Industrial Data Using Stable Diffusion

no code implementations6 Jan 2024 Gabriele Valvano, Antonino Agostino, Giovanni De Magistris, Antonino Graziano, Giacomo Veneri

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.

Defect Detection Image Generation

Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization

no code implementations6 Sep 2023 Shadi Ghiasi, Guido Pazzi, Concettina Del Grosso, Giovanni De Magistris, Giacomo Veneri

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.

Active Learning

Deep Surrogate of Modular Multi Pump using Active Learning

no code implementations4 Aug 2022 Malathi Murugesan, Kanika Goyal, Laure Barriere, Maura Pasquotti, Giacomo Veneri, Giovanni De Magistris

Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field.

Active Learning

Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

no code implementations4 Aug 2020 Domenico Gaglione, Giovanni Soldi, Paolo Braca, Giovanni De Magistris, Florian Meyer, Franz Hlawatsch

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.

Classification General Classification

Constrained Exploration and Recovery from Experience Shaping

1 code implementation21 Sep 2018 Tu-Hoa Pham, Giovanni De Magistris, Don Joven Agravante, Subhajit Chaudhury, Asim Munawar, Ryuki Tachibana

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 Reinforcement Learning +1

Reinforcement Learning Testbed for Power-Consumption Optimization

1 code implementation21 Aug 2018 Takao Moriyama, Giovanni De Magistris, Michiaki Tatsubori, Tu-Hoa Pham, Asim Munawar, Ryuki Tachibana

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

no code implementations4 Jul 2018 Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris, Sakyasingha Dasgupta

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.

Position Transfer Learning

OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World

no code implementations22 Sep 2017 Tu-Hoa Pham, Giovanni De Magistris, Ryuki Tachibana

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.

Decision Making reinforcement-learning +2

Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning

no code implementations16 Aug 2017 Asim Munawar, Phongtharin Vinayavekhin, Giovanni De Magistris

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.

Anomaly Detection Data Compression

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