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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Somogyi, Árpád Tettamanti, Tamás Varga, Pál Szalay, Zsolt Baranyai, Dániel Lovas, Tamás Budapest University of Technology and Economics Fac. of Civil Engineering Dept. of Photogrammetry and Geoinformatics Budapest Hungary Institute for Computer Science and Control The Eötvös Loránd Research Network Systems and Control Laboratory Budapest Hungary Budapest University of Technology and Economics Fac. of Electrical Engineering and Informatics Dept. of Telecommunications and Media Informatics Budapest Hungary Budapest University of Technology and Economics Fac. of Transportation Eng. and Vehicle Eng. Dept. of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665477161

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Edge-based Architecture for Phasor Measurements in Smart Grids

An Edge-based Architecture for Phasor Measurements in Smart ...

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2022 AEIT International Annual Conference, AEIT 2022

作者： Galantino, Stefano Risso, Fulvio Cazzaniga, Andrea Garrone, Fabrizio Terruggia, Roberta Lazzari, Riccardo Politecnico di Torino Dept. of Computer and Control Engineering Torino Italy Ricerca sul Sistema Energetico Tecnologie di Trasmissione e Distribuzione Milano Italy

ISBN: (数字)9788887237559

ISBN: (纸本)9788887237559

This paper investigates the application of Kubernetes and Edge computing technologies to operate IT services in the context of power systems and smart grids. Traditional services for grid monitoring such as Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs) require a centralized architecture and a rigid networking infrastructure in order to properly function, which today is only achieved at the High Voltage (HV) transmission level. Furthermore, manual intervention is often the only option for PMUs/PDCs maintenance. In this work, the traditional PμPDC services were deployed as docker-containers in a decentralized Kubernetes cluster, which can represent any kind of geographically dispersed TCP/IP network. By leveraging remote orchestration, several key benefits are achieved: (1) no manual reconfiguration of the PMU-PDC communications upon network reconfiguration, (2) automatic PMU traffic redirection in case of PDC service redeployment in a different location, and (3) reduced data-loss upon PDC failure and enhanced overall system resiliency due to minimized ICT services down-time. © 2022 AEIT.

关键词： Phasor measurement units

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Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

Robotic Arm Dataset (RoAD): A Dataset to Support the Design ...

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Annual Conference of Industrial Electronics Society

作者： Alessio Mascolini Sebastiano Gaiardelli Francesco Ponzio Nicola Dall'Ora Enrico Macii Sara Vinco Santa Di Cataldo Franco Fummi Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.

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Action Masked Deep Reinforcement learning for controlling Industrial Assembly Lines

Action Masked Deep Reinforcement learning for Controlling In...

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AI IoT Congress (AIIoT), World

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

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.

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Automated Assessment of Medical Rehabilitation of Gait by Means of Video Processing in Cloud Environment

Automated Assessment of Medical Rehabilitation of Gait by Me...

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E-Health and Bioengineering Conference (EHB)

作者： Elena-Claudia Maftei Otilia Zvorişteanu Hariton-Nicolae Costin Silviu Bejinariu Florin Rotaru Vasile Manta Ramona Luca Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Institute of Computer Science Romanian Academy Iaşi Branch Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Human gait analysis provides qualitative and quantitative information concerning different characteristics of walking of a certain person. Cloud computing proves to be a modern and valuable technology when dealing with data scalability, security and efficiency of processing and storage. Also, this paradigm has some key-benefits like real-time data sharing, fast computation as well as collaborative research. The applications gamut which uses cloud computing is getting bigger and bigger, and healthcare sector is an important domain in this respect. Our approach proposed a cloud-based service designed for the automated assessment of medical rehabilitation of human gait by using video processing. We have chosen this processing technology because it is marker-less, allows fast computation and offers a very good precision of the gait analysis. Cloud computing approach proved to be encouraging and underscores remarkable advantages in comparison with traditional on-site video processing. In this way the patient, after a short training session, can himself/herself make tests and assessments of his/her mobility status, avoiding many consultations of medical or kineto-therapeutic staff.

关键词： Legged locomotion Training Cloud computing Scalability Medical services Gain measurement People with disabilities Real-time systems Security Personnel

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Direct Data-Driven State-Feedback control of General Nonlinear Systems

Direct Data-Driven State-Feedback Control of General Nonline...

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IEEE Conference on Decision and control

作者： Chris Verhoek Patrick J. W. Koelewijn Sofie Haesaert Roland Tóth Dept. of Electrical Engineering Control Systems group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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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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Forum on Specification, Verification and Design Languages, FDL

作者： Luigi Capogrosso Alessio Mascolini Federico Girella Geri Skenderi Sebastiano Gaiardelli Nicola Dall'Ora Francesco Ponzio Enrico Fraccaroli Santa Di Cataldo Sara Vinco Enrico Macii Franco Fummi Marco Cristani 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 USA

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.

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Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

Low power implementation of Geometric High-order Decorrelati...

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IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

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LLCoach: Generating Robot Soccer Plans using Multi-Role Large Language Models

arXiv

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arXiv 2024年

作者： Brienza, Michele Musumeci, Emanuele Suriani, Vincenzo Affinita, Daniele Pennisi, Andrea Nardi, Daniele Bloisi, Domenico Daniele Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Dept. of International Humanities and Social Sciences International University of Rome Rome Italy

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © 2024, CC BY.

关键词： Robot programming

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Direct data-driven state-feedback control of general nonlinear systems

arXiv

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arXiv 2023年

作者： Verhoek, Chris Koelewijn, Patrick J.W. Haesaert, Sofie Tóth, Roland The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

关键词： Linear systems

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