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arXiv 2022年

作者： Bedroya, Olinka Li, Chenyang Wang, Wenyuan Hu, Jianyong Lo, Hoi-Kwong Qian, Li Centre for Quantum Information and Quantum Control Dept. of Physics University of Toronto TorontoONM5S 1A7 Canada Centre for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong 100 College St TorontoONM5G 1L5 Canada

Measurement-device-independent quantum key distribution (MDI-QKD) closes all the security loopholes in the detection system and is a promising solution for secret key sharing. Polarization encoding is the most common QKD encoding scheme, as it is straightforward to prepare and measure. However, implementing polarization encoding in MDI QKD imposes extra challenges, as polarization alignment must be maintained over both mutually unbiased bases and be maintained for both paths (Alice-Charlie and Bob-Charlie). Polarization alignment is usually done by interrupting the QKD process (reducing overall key generation rates) or using additional classical laser sources multiplexed with quantum channels for polarization alignment. Since low key rates and cost are the two most pressing challenges preventing wide adoption of QKD systems, using additional resources or reducing key rates runs contrary to making QKD commercially viable. Therefore, we propose and implement a novel polarization compensation scheme in the MDI-QKD system that avoids the aforementioned drawbacks by recycling part of discarded detection events. Our scheme evaluates the polarization drift in real-time based on single measurements corresponding to decoy intensities. Our fully automated experimental demonstration maintains the average polarization drift below 0.13 rad over 40 km of spooled fibre (without an insulating jacket) for at least four hours. The average quantum bit error rate is 3.8%, and we achieved an average key rate of 7.45 × 10−6 bits per pulse. © 2022, CC BY.

关键词： Polarization

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Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation

arXiv

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arXiv 2021年

作者： Irofti, Paul Romero-Ben, Luis Stoican, Florin Puig, Vicenç Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Romania Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya Barcelona Spain Politehnica University of Bucharest Dept. of Automation Control and Systems Engineering Romania

In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, these actual measurements, together with a subset of valuable estimated states, are used to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, showing that its performance is superior to either approach alone, even deriving different mechanisms to increase its resilience to classical problems (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed at BattLeDIM2020. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

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Situational management of production systems based on a balanced scorecard

Situational management of production systems based on a bala...

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2020 International Conference engineering and Telecommunication, En and T 2020

作者： Khanova, Anna Protalinsky, Oleg Bondareva, Irina Khanova, Yulya Astrakhan State Technical University Dept. Applied Informatics Astrakhan Russia Moscow Power Engineering Institute Dept. of Automated Control Systems of Thermal Processes Moscow Russia Saint-Petersburg Electrotechnical University 'Leti' Dept. of Mathematical Support and Computer Application St. Petersburg Russia

ISBN: (纸本)9781728188294

This paper describes the process of forming and selecting options for managerial decisions, including determining the problem situation from a set of indicators of a balanced scorecard and the presence or absence of similar situations in a set of typical situations. The structure of the management procedure is formalized, which includes the following blocks: preprocessing, state evaluation, managerial decision issuing, decision making. A situational model is built. The procedure for issuing managerial decisions is described, including the correction of a set of managerial decisions in terms of the composition and parameters, classification and parameterization of managerial decisions. This scorecard combines decision-making principles in fuzzy representations (assessing a problem situation) and in structured problem situations (issuing managerial decisions). The risk of not meeting the goals of the balanced scorecard decreased by an average of 76% and averaged 24% after implementing the recommended management decisions on the simulation model of the production systems. © 2020 IEEE.

关键词： Decision making

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Automated segmentation of microtomography imaging of Egyptian mummies

arXiv

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arXiv 2021年

作者： Tanti, Marc Berruyer, Camille Tafforeau, Paul Muscat, Adrian Farrugia, Reuben Scerri, Kenneth Valentino, Gianluca Solé, V. Armando Briffa, Johann A. Dept. of Comm. & Computer Engineering University of Malta Msida MSD 2080 Malta European Synchrotron Radiation Facility 71 Avenue des Martyrs CS-40220 Cedex 09 Grenoble38043 France Dept. of Systems & Control Engineering University of Malta Msida MSD 2080 Malta

Propagation Phase Contrast Synchrotron Microtomography (PPC-SRµCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94-98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97-99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in terms of usability to those from deep learning, justifying the use of these techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Tomography

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Exploiting Generative Self-Supervised Learning For The Assessment of Biological Images With Lack of Annotations: A COVID-19 Case-Study

Research Square

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Research Square 2021年

作者： Mascolini, Alessio Cardamone, Dario Ponzio, Francesco Di Cataldo, Santa Ficarra, Elisa Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy Università degli Studi di Torino Dept. of Physics Torino Italy Lab Fondazione Toscana Life Sciences Siena Italy Università degli Studi di Modena e Reggio Emilia Dept. of Engineering"Enzo Ferrari" Modena Italy

computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning-based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or late-stage clinical trials compounds to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks but also to effectively derive a dose-response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting of the categorization of four different cell types of the RxRx1 fluorescent images collection. © 2021, CC BY.

关键词： Coronavirus

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EXPLOITING GENERATIVE SELF-SUPERVISED LEARNING FOR THE ASSESSMENT OF BIOLOGICAL IMAGES WITH LACK OF ANNOTATIONS: A COVID-19 CASE-STUDY

arXiv

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arXiv 2021年

作者： Mascolini, Alessio Cardamone, Dario Ponzio, Francesco Cataldo, Santa Di Ficarra, Elisa Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy Università degli Studi di Torino Dept. of Physics Torino Italy Lab Fondazione Toscana Life Sciences Siena Italy Università di Modena and Reggio Emilia Dept. of Engineering Enzo Ferrari Modena Italy These authors contributed equally to this work

computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection. © 2021, CC BY-NC-ND.

关键词： Coronavirus

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An early prediction on mild cognitive impairment using point-to-point reaching task in a virtual reality 8

An early prediction on mild cognitive impairment using point...

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8th IEEE Global Conference on Consumer Electronics, GCCE 2019

作者： Shin, Dongryun Jeong, Chanyoung Hong, Soonchan Choi, Sieun Kim, Jihun Kim, Jaehyo Handong University Dept. Mechanical and Control Engineering Pohang Korea Republic of Handong University Dept. Computer Science and Electrical Engineering Pohang Korea Republic of

ISBN: (纸本)9781728135755

In this study, MMSE-K and a PTP reaching task in a VR space were conducted on groups of university students and senior citizens to find out the relationship between cognitive and motor performance indices. During controller-visible task, while seniors with relatively high MMSE-K scores (above 24) have shown no statistical difference with university students in terms of final position errors, seniors with low survey scores have shown significantly greater errors. Therefore, we observed cognitive functions are closely related with motor functions and spatial perception, so we expect that VR PTP reaching task would help early predictions on mild cognitive impairment. © 2019 IEEE.

关键词： Virtual reality

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V-Edge: Virtual edge computing as an enabler for novel microservices and cooperative computing

arXiv

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arXiv 2021年

作者： Dressler, Falko Chiasserini, Carla Fabiana Fitzek, Frank H.P. Karl, Holger Cigno, Renato Lo Capone, Antonio Casetti, Claudio Malandrino, Francesco Mancuso, Vincenzo Klingler, Florian Rizzo, Gianluca The School of Electrical Engineering and Computer Science TU Berlin Berlin Germany The Dept. of Electronics and Telecommunications Politecnico di Torino Torino Italy The School of Engineering Sciences as well as the Excellence Cluster CeTI TU Dresden Dresden Germany The Hasso-Plattner-Institute Potsdam Germany The Dept. of Information Engineering University of Brescia Brescia Italy The Dept. of Electronics Information and Bioengineering Politecnico di Milano Milano Italy The Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy The Institute of Electronics Computer and Communication Engineering of the National Research Council of Italy Torino Italy IMDEA Networks Madrid Spain The Dept. of Computer Science Paderborn University Paderborn Germany The Research Institute of Information Systems HES-SO Valais-Wallis Switzerland University of Foggia Italy

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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On the Role of Models in Learning control: Actor-Critic Iterative Learning control

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IFAC-PapersOnLine 2020年第2期53卷 1450-1455页

作者： Maurice Poot Jim Portegies Tom Oomen Control Systems Technology Group Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands

Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge.

关键词： Feedforward control iterative learning control learning algorithms reinforcement learning Markov decision problems function-approximation model-free control model-based control

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Enabling Conditions for the Deployment of Integrated Local Energy Communities in Europe

Enabling Conditions for the Deployment of Integrated Local E...

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Blockchain for Renewables Integration (BLORIN), Workshop on

作者： Martina Caliano Amedeo Buonanno Marialaura Di Somma Giorgio Graditi Christina Papadimitriou Chrysanthos Charalambous Przemysław Dopierała Leszek Bronk Dept. of Energy Technologies and Renewable Energy Sources ENEA Portici NA Italy Electrical Engineering dpt. Eindhoven University of Technology (TU/e) Eindhoven The Netherlands Department of Electrical and Computer Engineering University of Cyprus FOSS Research Centre for Sustainable Energy Nicosia Cyprus Department of Control Information and Communications Instytut Energetyki - Instytut Badawczy Gdansk Poland Department of Strategy and System Development Instytut Energetyki - Instytut Badawczy Gdansk Poland

ISBN: (纸本)9781665472500

The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of integrated local energy communities (ILECs) in Europe, performed within the project. The enabling conditions are addressed by defining the key actors and their interests in the implementation of an energy community at local level and through a detailed mapping of the enabling and emerging energy and information and communication technologies at both household and community levels. Special focus is also on mapping of the demand-side flexibility technologies to understand the benefits of local flexibility and impacts on the larger systems. In such analysis, the key issues for implementation and adaptation of this new energy paradigm are investigated through covering technological, socio-economic, environmental, and regulatory aspects.

关键词： Renewable energy sources Conferences Europe Information and communication technology Distributed power generation Blockchains

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