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Simulation-based Assessment of Energy Efficiency in Industry: Comparison of Hybrid Simulation Approaches

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IFAC-PapersOnLine 2018年第2期51卷 689-694页

作者： Heinzl, Bernhard Raich, Philipp Preyser, Franz Kastner, Wolfgang Institute of Computer Engineering Automation Systems Group Vienna University of Technology Vienna Austria

In order to analyze and optimize energy efficiency of complex industrial systems, methods for hybrid simulation are necessary that incorporate discrete as well as continuous aspects while at the same remain manageable for large-scale applications. To this end, we compare two approaches that facilitate hybrid simulation: Co-simulation couples multiple simulation environments that form a single model and exchange data at runtime. Typical coupling strategies that introduce communication delays are problematic if discrete variables are involved. The second approach is based on an extended DEVS formalism that facilitates integration of hybrid aspects on the component level and avoids communication delays during simulation. Both approaches are demonstrated on an example of an industrial conveyor oven. DEVS-based hybrid simulation shows to enable better integration of discrete/continuous model aspects also on the semantic level and can potentially achieve higher simulation speed and accuracy. © 2018

关键词： Energy efficiency computer simulation Discrete event simulation Distributed computer systems Hybrid systems Semantics Communication delays Complex industrial systems Continuous system Discrete event dynamic systems Distributed models Large scale applications Model management Simulation environment

The Impact and Potential of Block chain Applications Based on IoT

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The Impact and Potential of Block chain Applications Based o...

Simulation, Automation & Smart Manufacturing (SASM), International Conference on

作者： Sandeep Srivastava Anurag Aeron Thulasi Bikku Manisha Kaur Varsha Satish Rupraoji Billewar GL Bajaj of Institute of Technology &#x0026 Management Greater Noida India Dept. of Computer Science and Engineering DIT University Dehradun India Dept. of Computer Science and Engineering Vignan&#x2019 s Nirula Institute of Technology and Science for Women Guntur Andhra Pradesh India Department of Computer Science and Engineering CT Group of Institute (CTIEMT) Shahpur Jalandhar India I.K.Gujral Punjab Technical University Jalandhar India Information Technology / Systems Vivekananda Institute of Management Studies and Research Chembur (E) Mumbai Maharashtra India

ISBN: (数字)9781665403573

ISBN: (纸本)9781665447768

The Block chain is a course of action of recording information to such an extent that makes it irritating or hard to alter, slash, or swindle the system. In blockchain decentralized amend the proceedings that could be used to indicate, join up also send wide scope of agreement to various social affairs in association. The scope of block chain applications stretches out to open and public administrations including wellbeing, financial, automotive, Internet of Things (IoT) and risk management. This paper presents a thorough outline on blockchain innovation. Lately, block chain innovation has become extremely stylish and infiltrated various areas, generally because of the crypto currencies determination of rotator for the particular sort of suspension needs some fundamental information.

关键词： Technological innovation Education Blockchains Recording Cryptocurrency Internet of Things Risk management

HUNTER: AI based holistic resource management for sustainable cloud computing

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications

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arXiv 2021年

作者： Tekbıyık, Kürşat Kurt, Güneş Karabulut Ekti, Ali Rıza Yanikomeroglu, Halim Department of Electronics and Communications Engineering Istanbul Technical University Istanbul Turkey Poly-Grames Research Center Department of Electrical Engineering Polytechnique Montréal Montréal Canada Grid Communications and Security Group Electrification and Energy Infrastructure Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Systems and Computer Engineering Carleton University Ottawa Canada

Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible. © 2021, CC BY-NC-SA.

关键词： Complex networks

Phase-Coded FMCW Automotive Radar: Application and Challenges

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Phase-Coded FMCW Automotive Radar: Application and Challenge...

IEEE International Conference on Radar

作者： Faruk Uysal Simone Orru Faculty of Electrical Engineering Mathematics and Computer Science (EEMCS) Microwave Sensing Systems and Signals (MS3) group Delft University of Technology Delft The Netherlands

ISBN: (数字)9781728168135

ISBN: (纸本)9781728168142

Phase-Coded frequency-modulated continuous-wave (PC-FMCW) radar is an emerging radar system with its unique features such as enabling joint sensing and communication or advanced interference mitigation. Even though radars with phase coding capabilities are available in the automotive radar market today, there are some challenges in phase coded radar applications. With this paper we demonstrate multiple bit coding per single transmit chirp waveform in a FMCW automotive radar system for the first time. We concentrate on the sensing aspect of PC-FMCW radar and point out the challenges due to instantaneous phase change. To overcome these issues and improve sensing performance, this paper proposes a smart filtering method which consists of rejection filtering and signal recovery steps.

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Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

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arXiv 2021年

作者： Nápoles, Gonzalo Salgueiro, Yamisleydi Grau, Isel Espinosa, Maikel Leon The Department of Cognitive Science & Artificial Intelligence Tilburg University Netherlands The Department of Computer Science Faculty of Engineering Universidad de Talca Campus Curicó Chile The Information Systems Group Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands The Department of Business Technology Miami Herbert Business School University of Miami United States

Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose a Long-Term Cognitive Network for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models. © 2021, CC BY-NC-ND.

关键词： Recurrent neural networks

Satellite-based InSAR Geodesy and Collocation with GNSS

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Procedia computer Science 2024年 239卷 2329-2340页

作者： Juraj Papco Matus Bakon Lukas Kubica Gabika Belicova Branislav Droscak Martin Ferianc Martin Rovnak Antonio M. Ruiz Joaquim J. Sousa Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 813 62 Bratislava Slovakia insar.sk Ltd Konstantinova 3 080 01 Presov Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 080 01 Presov Slovakia Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro Quinta de Prados 5000-801 Vila Real Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 827 45 Bratislava Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Center for Advanced Studies in Earth Sciences Energy and Environment (CEACTEMA) University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Research Group RNM-282 Microgeodesia Jaén University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC 4200-465 Porto Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame.

关键词： InSAR geodesy collocation GNSS

SoK: Cross-border Criminal Investigations and Digital Evidence

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arXiv 2022年

作者： Casino, Fran Pina, Claudia López-Aguilar, Pablo Batista, Edgar Solanas, Agusti Patsakis, Constantinos Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Spain Information Management Systems Institute Athena Research Center Artemidos 6 Marousi15125 Greece European Judicial Cybercrime Network Eurojust The Hague Netherlands Anti-Phishing Working Group-Europe Av. Diagonal 621-629 Barcelona08028 Spain Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece

Digital evidence underpin the majority of crimes as their analysis is an integral part of almost every criminal investigation. Even if we temporarily disregard the numerous challenges in the collection and analysis of digital evidence, the exchange of the evidence among the different stakeholders has many thorny issues. Of specific interest are cross-border criminal investigations as the complexity is significantly high due to the heterogeneity of legal frameworks which beyond time bottlenecks can also become prohibiting. The aim of this article is to analyse the current state of practice of cross-border investigations considering the efficacy of current collaboration protocols along with the challenges and drawbacks to be overcome. Further to performing a legally-oriented research treatise, we recall all the challenges raised in the literature and discuss them from a more practical yet global perspective. Thus, this article paves the way to enabling practitioners and stakeholders to leverage horizontal strategies to fill in the identified gaps timely and accurately. © 2022, CC BY-NC-SA.

关键词： Crime

QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

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QuantYOLO: A High-Throughput and Power-Efficient Object Dete...

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Gohar Javed Minahil Raza Muhammad Mohsin Ghaffar Christian Weis Norbert Wehn Muhammad Shahzad Faisal Shafait School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Germany Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Pakistan Data Science in Earth Observation TU Munich Germany

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.

关键词： Quantization (signal) Power demand Network topology Object detection computer architecture Throughput Topology