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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads

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

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

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

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.

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Deep Reinforcement Learning based Routing in an Air-to-Air Ad-hoc Network 41

Deep Reinforcement Learning based Routing in an Air-to-Air A...

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41st IEEE/AIAA Digital Avionics systems Conference, DASC 2022

作者： Wang, Zhe Han, Ruixuan Li, Hongxiang Knoblock, Eric J. Apaza, Rafael D. Gasper, Michael R. University of Louisville Dept. of Electrical and Computer Engineering LouisvilleKY United States Nasa Glenn Research Center Communications and Intelligent Systems Division ClevelandOH United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

This paper studies the Multiple Sources and Multiple Destinations (MSMD) routing problem in a dynamic Air-to-Air Ad-hoc Network (AAAN). We consider a spectrum-limited scenario where multiple links have to share the same frequency channel so that co-channel interference becomes inevitable. As a result, routing decisions and spectrum access are coupled and must be jointly considered. This paper proposes a deep Q-learning-based algorithm to find an optimal routing and channel selection strategy that minimizes the end-to-end communication delay. Specifically, under the assumption that only local information is available to every node, the Deep Q-Network (DQN) is trained offline to learn the optimal routing and channel selection strategy. After the trained DQN is implemented in every node, multiple relay nodes can simultaneously determine their next-hop relay and channel selections in real-time. Simulation results demonstrate the efficacy of our proposed algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Digital AirComp-Assisted Federated Edge Learning with Adaptive Quantization

Digital AirComp-Assisted Federated Edge Learning with Adapti...

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IEEE International Conference on Human-Machine systems

作者： Ghazaleh Kianfar S. Jamal Seye Dmohammadi Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Concordia Institute of Information Systems Engineering (CIISE) Concordia University Montreal Canada Dept. of Electrical Engineering Yazd University Yazd Iran The Edward S. Rogers Sr. Dept. of Electrical & Computer Engineering University of Toronto Canada

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Federated edge learning (FEEL) has been introduced for training machine learning models on distributed datasets for applications such as human monitoring. However, some challenges exist concerning the number of communication rounds and communication energy consumption involved in transmiting gradients during the training process. Moreover, over-the-air computation (AirComp) technology has gained attention recently, benefiting from superposition characteristic of wireless channels to compute functions over the air. While primarily designed for analog systems, there is potential for applying this technology in digital systems with embedded modulation schemes. This paper addresses these challenges by proposing a digital AirComp system for federated learning aggregation. The system employs a multi-bit quantization scheme to modulate gradients, adhering to a maximum transmission power constraint. An adaptive quantization scheme is also introduced, which considers the impact of quantization error and the error induced by additive white Gaussian noise. We derive closed-form expressions for pre-processing coefficients at devices and post-processing scaler at the access point (AP) to minimize the mean-squared error between high-precision and quantized qradients under the maximum transmission power constraint. Finally, the performance of the proposed scheme is evaluated in terms of achieved test accuracy, mean-squared error (MSE), and energy consumption, demonstrating its potential effectiveness compared to the benchmark schemes.

关键词： Training Performance evaluation Energy consumption Quantization (signal) Accuracy Federated learning Wireless networks

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

the paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis; binary classification decision tree; feed-forward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes.

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DETReg Incorporating Semi-Supervised Learning for Object Detection in the Advanced Driver-Assistance systems

DETReg Incorporating Semi-Supervised Learning for Object Det...

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Robotics, engineering, Science, and Technology (RESTCON), International Conference on

作者： Keita Nakano Kousuke Matsushima Advanced Engineering School National Institute of Technology Kurume College Fukuoka Japan Dept. of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

In Advanced Driver-Assistance systems (ADAS) and automatic driving, it is important to accurately recognize objects around the vehicle. DETReg is one of the unsupervised pre-training methods using Transformer, which is self-supervised by combining localization and categorization. DETReg performs self-supervised learning on unlabeled images. Then, it extracted a wide range of features from rich aspects of the data and gained the flexibility to adapt to many variations. Fine tuning then used the labeled dataset of the target task to fine tune the model to fit the specific dataset. This allowed DETReg to achieve higher accuracy in the object detection task. However, it is difficult to learn DETReg efficiently because of its slow learning time. In this paper, we propose a new pre-training method for object detection, called Semi-DETReg, that utilizes a few supervised labels during self-supervised learning. We incorporate semi-supervised learning into DETReg by using a portion of the supervised training data in the pre-training to improve efficiency. We demonstrate the effectiveness of our method by conducting experiments and comparing our method to a similarly trained DETReg.

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Attenuation Resilient Underwater Ranging Using Petal-Like Structured Light with Tailorable Longitudinal Intensity

Attenuation Resilient Underwater Ranging Using Petal-Like St...

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2024 Laser Science, LS 2024

作者： Wang, Yingning Duan, Yuxiang Zeng, Ruoyu Zhou, Huibin Jiang, Zile Zuo, Yue Ramakrishnan, Muralekrishnan Bock, Robert Mojahedi, Mo Tur, Moshe Willner, Alan E. Dept. of Electrical Engineering University of Southern California Los AngelesCA90089 United States R-DEX Systems Inc. WoodstockGA30188 United States Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada School of Electrical Engineering Tel Aviv University Ramat Aviv 69978 Israel

We experimentally demonstrate a petal-like attenuation-resilient ranging beam with a ~9.5-dB central power enhancement at a 0.4-meter ranging distance, achieving 5 mm average ranging errors over 0-0.4 m in scattering ...

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region

Analysis of Energy Potential of Micro Wind Turbines at Three...

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Communications, Information, Electronic and Energy systems (CIEES), International Conference on

作者： Nikolay Valov Katerina Gabrovska-Evstatieva Ivaylo Stoyanov Tsvetelina Kaneva Seher Kadirova Boris Evstatiev Nicolay Mihailov Dept. of Automation and Electronics University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Science University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Electrical Power Engineering University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Systems and Technologies University of Ruse "Angel Kanchev" Ruse Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations – Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m 2 in February, followed by Calarasi with 0.38 kWh/day/m 2 . The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m 2 . The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines.

关键词： Hands Temperature distribution Renewable energy sources Wind energy Wind speed Urban areas Wind turbines Rivers Wind forecasting Investment

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Pose-free visual servoing from 3D measurements

Pose-free visual servoing from 3D measurements

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European control Conference (ECC)

作者： Adrian Burlacu Radu L. Rosca Andrei I. Iancu Enric Cervera Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Romania Dept. of Computer Science and Engineering Jaume I University Spain

In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.

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Nonlinear Repetitive control for Mitigating Noise Amplification

Nonlinear Repetitive Control for Mitigating Noise Amplificat...

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IEEE Conference on Decision and control

作者： Leontine Aarnoudse Alexey Pavlov Johan Kon Tom Oomen Dept. of Mechanical Engineering Control Systems Technology Eindhoven University of Technology Eindhoven The Netherlands Dept. of Geoscience and Petroleum NTNU Norwegian University of Science and Technology Trondheim Norway Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

Repetitive control can lead to high performance by attenuating periodic disturbances completely, yet it may amplify non-periodic disturbances. The aim of this paper is to achieve both fast learning and low errors in repetitive control. To this end, a nonlinear learning filter is introduced that distinguishes between periodic and non-periodic errors based on their typical amplitude characteristics to adapt the extent to which they are included in the repetitive controller. Convergence conditions for the nonlinear repetitive control system are derived by casting the resulting closed-loop as a discrete-time convergent system. Simulation results of the proposed approach demonstrate fast learning and small errors.

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A Code-Driven Tutorial on Encrypted control: From Pioneering Realizations to Modern Implementations

A Code-Driven Tutorial on Encrypted Control: From Pioneering...

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European control Conference (ECC)

作者： Nils Schlüter Junsoo Kim Moritz Schulze Darup Dept. of Mechanical Engineering Control and Cyberphysical Systems Group TU Dortmund University Germany Department of Electrical and Information Engineering Seoul National University of Science and Technology South Korea

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and service-based decision-making. At the same time, connected systems are susceptible to cyberattacks and data leakages. Against this background, encrypted control aims to increase the security and safety of cyber- physical systems. A central goal is to ensure confidentiality of process data during networked controller evaluations, which is enabled by, e.g., homomorphic encryption. However, the integration of advanced cryptographic systems renders the design of encrypted controllers an inter-disciplinary challenge. This code-driven tutorial paper aims to facilitate the access to encrypted control by providing exemplary realizations based on popular homomorphic cryptosystems. In particular, we discuss the encrypted implementation of state feedback and PI controllers using the Paillier, GSW, and CKKS cryptosystem.

关键词： Wireless communication State feedback PI control Process control Europe Tutorials Elliptic curve cryptography

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