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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Ali, Ali Mohamed Tirel, Luca Carleton University Dept. of Mechanical and Aerospace Engineering Ottawa Canada University of Rome "la Sapienza" Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350337617

The scalability of control algorithms controlling industrial assembly lines either model-based or model-free is still a big barrier from the point of view of implementation. In this paper, various Deep Reinforcement learning (DRL) algorithms will be tested in a virtual environment of a generic industrial assembly line in terms of optimally with respect to optimal exact solutions, and computational time. The agents will take bigger rewards when they minimize the finishing time while maintaining constraints, such as time precedence between tasks. The control actions will be modeled as a task assignment matrix that assigns the tasks to be completed to specific workstations. Another control action is the resource allocation. Due to the big number of constraints, many actions will be unfeasible leading to a longer time of training of the agent to learn not to apply unfeasible actions, therefore action masking will be introduced to the DRL algorithms to reduce the action space only to the feasible actions, thereby significantly reducing training time. Proximal Policy Optimization (PPO) agent shows faster and more stable convergence to the optimal solution over the Deep-Q Network (DQN) family, including the double DQN and Dueling one. © 2023 IEEE.

关键词： Reinforcement learning

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Properties of singular solutions in optimal control problems under input dynamic extension 63

Properties of singular solutions in optimal control problems...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Giamberardino, Paolo Di Iacoviello, Daniela Sapienza University of Rome Dept. of Computer Control and Management Engineering Antonio Ruberti via Ariosto 25 Rome00185 Italy

ISBN: (纸本)9798350316339

The paper addresses the problem of optimal control design in presence of singular solutions for single input dynamics. The dynamical extension for systems obtained adding an integrator on the input is addressed and analyzed. The possibility of computing the optimal control for dynamically extended systems from the solution of the initial ones is investigated, as well as the inverse procedure. These relationships are well evidenced for the singular solutions, showing the possibility of simplifying the optimal control computation. An example is introduced to better highlight the presented results. © 2024 IEEE.

关键词： Optimal control systems

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A hierarchical and modular agent-oriented framework for power systems co-simulations

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Energy Informatics 2022年第4期5卷 48页

作者： De Vizia, Claudia Macii, Alberto Patti, Edoardo Bottaccioli, Lorenzo Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy Interuniversity Dept. of Regional and Urban Studies Politecnico di Torino Turin Italy

During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid "smart". Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture. © 2022, The Author(s).

关键词： Smart power grids

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Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-And-Crazy-Idea Paper

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic M...

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2023 Forum on Specification and Design Languages, FDL 2023

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall'Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina at Chapel Hill United States

ISBN: (纸本)9798350307375

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-Time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before. copy;2023 IEEE. © 2023 IEEE computer Society. All rights reserved.

关键词： Industry 4.0

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MIRROR: MiRNA regulation-level differential network to study sex and ethnic disparities in cancer 46

MIRROR: MiRNA regulation-level differential network to study...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alfano, Caterina Filetti, Marco Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Experimental Medicine Rome Italy Fondazione Policlinico Universitario A. Gemelli Irccs Phase 1 Unit Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350371499

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients' cohorts, to study the role they play at the target genes' level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge. © 2024 IEEE.

关键词： differential network modeling disparities in cancer graph theory miRNA-target association transcriptomics

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Robust control of Right Invertible Nonlinear Systems

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IFAC-PapersOnLine 2024年第5期58卷 144-151页

作者： Alberto Isidori Dept. of Computer Control and Management Engineering “Sapienza” University Rome Italy

In a MIMO a nonlinear system having the same number of inputs and outputs, if certain coefficients that characterize its normal form have a special structure, a number of relevant design problems can been successfully addressed. In this paper we consider the more general case of a system having more inputs than outputs, we show that such special structure implies the regularity of the algorithm for construction of a right-inverse, and we show how a recently developed design method for robust control can be implemented on this class of MIMO systems.

关键词： Nonlinear systems multivariable systems right-invertibility normal forms feedback linearization

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads 31

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Marino, Jacopo Risso, Fulvio Bighi, Mauro Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy PUNCH Torino S.p.A Dept. of Information Technology Torino Italy

ISBN: (纸本)9798350301076

The vast array of cloud providers present in today's market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module's primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase. © 2023 University of Split, FESB.

关键词： Machine learning

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Partial Discharge Classification With 1D Convolutional Neural Network 14

Partial Discharge Classification With 1D Convolutional Neura...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed. © 2024 IEEE.

关键词： Convolutional neural networks

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Partial Discharge Classification with Transformer Neural Networks 14

Partial Discharge Classification with Transformer Neural Net...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

ISBN: (纸本)9798350374605

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed © 2024 IEEE.

关键词： Electric corona

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