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Data-Driven Predictive control for Linear Parameter-Varying Systems ⁎

IFAC-PapersOnLine 2021年第8期54卷 101-108页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Sofie Haesaert Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600MB The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control (SZTAKI) Kende u. 13-17 1111 Budapest Hungary

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.

关键词： Predictive control Data-Driven control Linear Parameter-Varying Systems Non-Parametric Methods

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Overview of Ancillary Services in the Nigerian Power Sector

Overview of Ancillary Services in the Nigerian Power Sector

引用

Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Bemdoo Saka Suleiman U. Hussein Biliaminu A. Ishola Yekini S. Mohammed Nanen D. Hembe Umar M. Mustapha Sadiq Thomas Dept. of Electrical/Electronics Eng. Nile University of Nigeria Abuja Nigeria Centre for Satellite Tech. Development Nat. Space Research and Dev. Agency Abuja Nigeria National Control Center Transmission Company of Nigeria Osogbo Abuja Dept. of Electrical/Electronics Eng. Federal Polytechnic Nasarawa Nasarawa Nigeria Department of Geography Nasarawa State University Nasarawa Nigeria Advanced UAV Laboratory Nat. Space Research and Dev. Agency Abuja Nigeria Dept. of Computer Engineering Nile University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

An overview of the ancillary services in the Nigerian power sector is provided in this work. These services are essential in the power system because they are additional services required by the grid during critical times to enable the power grid to operate in a stable, safe, and reliable manner. The ancillary services provided as stipulated in the Grid Code are black start, frequency control, operating reserves, and voltage control. The System Operator procures the relevant ancillary service depending on the requirement of the grid at a particular time. The paper further presents some ways of improving the viability of ancillary services for the energy grid of Nigeria. These include making incentives more attractive for suppliers of ancillary services, utilizing more hydroelectric power plants for generation and load balancing, integrating distributed generation to provide support to the reactive power, and making necessary investments in the grid.

关键词： Codes Hydroelectric power generation Load management Power system reliability Reliability Voltage control Investment

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On the testing of special memories in GPGPUs

On the testing of special memories in GPGPUs

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Josie E. Rodriguez Condia Matteo Sonza Reorda Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9781728181875

ISBN: (纸本)9781728181882

Nowadays, data-intensive processing applications, such as multimedia, high-performance computing and safety-critical ones (e.g., in automotive) employ General Purpose Graphics Processing Units (GPGPUs) due to their parallel processing capabilities and high performance. In these devices, multiple levels of memories are employed in GPGPUs to hide latency and increase the performance during the operation of a kernel. Moreover, modern GPGPU architectures implement cutting-edge semiconductor technologies, reducing their size and power consumption. However, some studies proved that these technologies are prone to faults during the operative life of a device, so compromising reliability. In this work, we developed functional test techniques based on parallel Software-Based Self-Test routines to test memory structures in the memory hierarchy of a GPGPU (FlexGripPlus) implementing the G80 architecture of Nvidia.

关键词： Instruction sets Registers Radio frequency Kernel Performance evaluation computer architecture Power demand

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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

引用

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

引用

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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Distinguishing between Fault and Inrush Current in Presence of the CT Saturation: A New Method Based on Gravity Center in Time 19

Distinguishing between Fault and Inrush Current in Presence ...

引用

19th IEEE International Conference on Environment and Electrical engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

作者： Bagheri, Alireza Allahbakhshi, Mehdi Samet, Haidar Tajdinian, Mohsen Seifi, Alireza Dept. of Power Control Engineering Shiraz University School of Electrical and Computer Engineering Shiraz Iran

ISBN: (纸本)9781728106526

Failure in distinguishing between the internal fault and inrush phenomena in transformer especially in the presence of saturation in current transformer, may lead to mal-operation of the differential relays. This paper introduces a novel index that distinguishes inrush current from fault current even in the presence of saturation. This index utilizes the concept of gravity center mass (GCM) on the current signal and it is called gravity center in time (GCT). GCT is defined as a point that the energy of signal before and after of GCT is equal. Utilizing the concept of GCM on current signals it can be mathematically proven the proposed algorithm based on the GCT categorizes normal operation, fault condition and inrush current from each other in one cycle. The application of GCT in discrimination of internal fault and inrush current is investigated and further demonstrated for some simulation and experimental inrush and internal fault signals. The results show that proposed method classifies the disturbances quickly with notable accuracy under different conditions. © 2019 IEEE.

关键词： Gravitation

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Enhancing Immunity of Full-Cycle Discrete Fourier Transform Against Decaying DC Components: A Comparative Analysis 19

Enhancing Immunity of Full-Cycle Discrete Fourier Transform ...

引用

19th IEEE International Conference on Environment and Electrical engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

作者： Tajdinian, Mohsen Samet, Haidar Hamedi, Alireza Hadaeghi, Arsalan Bagheri, Alireza Allahbakhshi, Mehdi Seifi, Alireza School of Electrical and Computer Engineering Shiraz University Dept. of Power and Control Engineering Shiraz Iran

ISBN: (纸本)9781728106526

Decaying DC terms of the fault current signal indisputably affect the accuracy of the discrete Fourier transform (DFT) based fundamental phasor estimators of digital relays. This effects will lead to delay in the best case and mal operation in the worst case of the protective relays and it may threat the reliability and security of the power system. This paper aims to conduct a comparative analysis between some decaying DC removal filters for enhancing the immunity if full-cycle DFT against decaying DC components. This paper mainly discusses the effects of DC offsets in the fault current and CT transient responses, harmonics and off-nominal frequency on the performance of DFT. Applying different test signals and also simulated fault current, the effectiveness of the different DC removal filters in combination with DFT is investigated. Analyzing simulation results, it is observed that the DFT phasor estimator should be provided with an algorithm that removes DC offsets due to fault current and CT transient responses under harmonic, noisy and off-nominal frequency conditions. © 2019 IEEE.

关键词： Discrete Fourier transforms

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Resource-Efficient Real-Time Polarization Compensation for MDI-QKD with Rejected Data

arXiv

引用

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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Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P–Events to Polarization Reconstruction from P...

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tobi Delbruck Zuowen Wang Haiyang Mei Germain Haessig Damien Joubert Justin Haque Yingkai Chen Moritz B. Milde Viktor Gruev Sensors Group Institute of Neuroinformatics Univ. of Zurich and ETH Zurich Switzerland AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Intl. Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [...

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Automated segmentation of microtomography imaging of Egyptian mummies

arXiv

引用

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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