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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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Deep Image Inpainting to Support Endoscopic Procedures

Deep Image Inpainting to Support Endoscopic Procedures

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Filippo Betello Francesco Delli Priscoli Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome “La Sapienza” Rome Italy

Deep image inpainting is a computer vision task that uses Deep Neural Networks to generate plausible content to complete an image, for example for the restoration of a damaged image or the removal of unwanted elements captured in the picture. This paper uses deep image inpainting to restore endoscopic images that are affected by various types of artifacts. To this end, we developed a transfer learning-based procedure that uses the CSA inpainting model, which was originally proposed for unrelated tasks including the restoration of images from the Paris StreetView Dataset. The proposed system is trained and validated on the EndoCV2020 dataset, consisting of images from real endoscopies, highlighting how deep image inpainting may be a promising technology for frame restoration during medical procedures.

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Vertically-Advised Federated Learning for Multi-Strategic Stock Predictions through Stochastic Attention-based LSTM

Vertically-Advised Federated Learning for Multi-Strategic St...

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Emanuele Ciccarelli Michele Viscione Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome ”La Sapienza” Rome Italy

In recent years, stock price forecasting has become a challenging task commonly used to evaluate the performance of various machine learning solutions. This work explores a Federated Learning (FL) framework within a competitive collaboration scenario with the aim of training a centralised model advised by non-recoverable decentralised strategies so that no exchange of private data is required. The proposed Vertically-Advised Federated Learning (VAFL) framework combines elements from both horizontal and vertical FL, as each client trains two independent models. Furthermore, a novel forecasting architecture, based on a stochastic variant of an Attention-based Long Short Term Memory (LSTM) network, is proposed and validated on a simulated scenario based on real data from the stock market.

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A Feature Fusion based Custom Deep Learning Model for Vehicle Make and Model Recognition

A Feature Fusion based Custom Deep Learning Model for Vehicl...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Triyas Ghosh Soumyajit Gayen Sourajit Maity Daria Valenkova Ram Sarkar Dept. of Computer Sc. and Engineering Jadavpur University Kolkata India Dept. of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

Vehicle Make and Model Recognition (VMMR) is a pivotal task in various domains including surveillance, traffic management, and the automotive industry. Despite significant progress in deep learning approaches, existing VMMR systems still struggle to attain robust accuracy, particularly across diverse environments and viewing angles. In this paper, we propose a novel approach based on the fusion of two feature maps extracted from DenseNet201 and ResNet50V2 baseline models, respectively. Additionally, we employ a modified Convolutional Block Attention Module (CBAM) to enhance the resilience and precision of VMMR systems. By incorporating attention mechanisms in the feature extraction process, our modified CBAM model effectively captures both spatial and channel-wise dependencies, facilitating more potent discriminative feature representations. We assess our proposed approach through extensive experiments on two benchmark datasets namely Stanford Cars and CompCarsSV. Achieved accuracies are 93.51% and 99.03%, on Stanford Cars and CompCarsSV, respectively that are better than past methods. The code of our proposed model can be found at: https://***/JUVCSE/featurefusion.

关键词： Deep learning Industries Accuracy Surveillance Traffic control Feature extraction Robustness

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements 36

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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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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