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IEEE IVMSP Workshop

作者： Jon Alvarez Justo Daniela Lupu Milica Orlandić Ion Necoara Tor Arne Johansen Department of Electronic Systems Norwegian University of Science and Technology Department of Automatic Control and Systems Engineering University Politehnica Bucharest Department of Engineering Cybernetics Norwegian University of Science and Technology

ISBN: (数字)9781665478229

ISBN: (纸本)9781665478236

Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images $ 2.5\times$ compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover.

关键词： Greedy algorithms Multidimensional signal processing Image coding Conferences Signal processing algorithms Data processing Sensors

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A Cyclic Small Phase Theorem for MIMO LTI systems*

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IFAC-PapersOnLine 2023年第2期56卷 1883-1888页

作者： Chao Chen Wei Chen Di Zhao Jianqi Chen Li Qiu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Mechanics and Engineering Science & State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between -π and π. The proposed theorem complements the celebrated small gain theorem.

关键词： Small phase theorem segmental phase cyclic feedback systems stability analysis lead-lag compensation

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Aspects on errors-in-variables identification: Some ways to mitigate a large bias

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IFAC-PapersOnLine 2023年第2期56卷 4019-4024页

作者： Torsten Söderström Umberto Soverini Division of Systems and Control Department of Information Technology Uppsala University P O Box 337 SE-751 05 Uppsala Sweden Dept. of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

Standard identification methods give biased parameter estimates when the recorded signals are corrupted by noise on both input and output sides. When the system is close to be non-identifiable, the bias can be large. The paper discusses the possibilities and potential benefits when using either a reduced model structure or a full errors-in-variables model. The case of using an instrumental variable estimator is also treated.

关键词： System Identification Errors-in-variables Bias

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Risk-Averse Certification of Bayesian Neural Networks

arXiv

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arXiv 2024年

作者： Zhang, Xiyue Wang, Zifan Gao, Yulong Romao, Licio Abate, Alessandro Kwiatkowska, Marta Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Wind and Energy Systems Technical University of Denmark Denmark

In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In this work, we propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN, represented using a set of template polytopes. To enhance robustness evaluation, we integrate a coherent distortion risk measure—Conditional Value at Risk (CVaR)—into the certification framework, providing probabilistic guarantees based on empirical distributions obtained through sampling. We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method. The results show that RAC-BNN effectively quantifies robustness under worst-performing risky scenarios, and achieves tighter certified bounds and higher efficiency in complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Fatemeh Ahmadvand Mahmood Hosseini Imani Hamid Taheri Andani Mansour Taheri Andani Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Energy (DENERG) Politecnico di Torino Torino Italy Department of Electronic Engineering Isfahan University of Technology Isfahan Iran School of Manufacturing Systems and Networks Arizona State University Tempe AZ

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price.

关键词： Machine learning algorithms Accuracy Electricity Neural networks Predictive models Prediction algorithms Robustness Forecasting Long short term memory Optimization

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Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 179-188页

作者： Zhou He Ziyue Ma Zhiwu Li Alessandro Giua the College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Institute of Systems Engineering Macao University of Science and TechnologyTaipa 999078MacaoChina the Department of Electrical and Electronic Engineering University of CagliariCagliari 09124Italy

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.

关键词： Discrete event system resource allocation timed Petri net weighted marked graph

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Dc Leakage Current in Isolated Grid-Connected dc Nanogrid - Origins and Elimination Methods

Dc Leakage Current in Isolated Grid-Connected dc Nanogrid - ...

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Compatibility in Power electronics (CPE)

作者： Mohammadreza Azizi Oleksandr Husev Oleksandr Veligorskyi Marek Turzvński Ryszard Strzelecki Department of Radiotechnic and Embedded Systems Chernihiv Polytechnic National University Chernihiv Ukraine Department of Electric Electronic and Automation Engineering University of Extremadura Badajoz Spain Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Faculty of Electrical and Control Engineering Gdansk University of Technology Gdansk Poland

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

The LV dc system is a relatively new trend in the distribution sector, which seems to grow widely in the near future due to its promising advantages. In this context, LV dc protection and grounding are challenging issues. Although the galvanically isolated connection mode of dc nanogrid to the ac grid has high reliability, the leakage current can still be injected into the ac grid through the interwinding capacitors and the insulation resistance between the primary and secondary windings of the transformer. The way of grounding the dc nanogrid can also be a determining factor in the leakage current and its dc components. This study deals with the leakage current in the galvanically isolated dc nanogrid. Then, it examines the dc leakage current and its relationship with the dc nanogrid grounding and finally provides solutions to remove the dc components in the leakage current.

关键词： Resistance Insulation Grounding Simulation Windings Capacitors Leakage currents

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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第35届中国控制与决策会议

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is ***,a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external ***,the unknown regulator equations are solved using the iterative recurrent neural network ***,the state-based data-driven distributed control law is synthesized to solve the *** optimal gains are derived by solving convex optimization problems using input and state ***,a numerical example is presented to verify the feasibility of the proposed framework.

关键词： Linear systems multi-agent systems cooperative output regulation data-driven control optimal control

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Contrast enhancement of optical videoscope images

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Iran Journal of Computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Learning-Based Predictive control with Gaussian Processes: An Application to Urban Drainage Networks

Learning-Based Predictive Control with Gaussian Processes: A...

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2022 American control Conference, ACC 2022

作者： Balla, Krisztian Mark Eringis, Deividas Al Ahdab, Mohamad Dimon Bendtsen, Jan Kallesoe, Carsten Skovmose Ocampo-Martinez, Carlos Aalborg University Section for Automation and Control Department of Electronic Systems Fredrik Bajers vej 7c AalborgDK-9220 Denmark Grundfos Holding A/S Technology Innovation Controls Department Poul Due Jensens vej 7 BjerringbroDK-8850 Denmark Universitat Politècnica de Catalunya Automatic Control Department Llorens i Artigas 4-6 Planta 2 Barcelona08028 Spain

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Many traditional control solutions in urban drainage networks suffer from unmodelled nonlinear effects such as rain and wastewater infiltrating the system. These effects are challenging and often too complex to capture through physical modelling without using a high number of flow sensors. In this article, we use level sensors and design a stochastic model predictive controller by combining nominal dynamics (hydraulics) with unknown nonlinearities (hydrology) modelled as Gaussian processes. The Gaussian process model provides residual uncertainties trained via the level measurements and captures the effect of the hydrologic load and the transport dynamics in the network. To show the practical effectiveness of the approach, we present the improvement of the closed-loop control performance on an experimental laboratory setup using real rain and wastewater flow data. © 2022 American Automatic control Council.

关键词： Stochastic models

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