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23rd International Conference on control systems and Computer Science Technologies, CSCS 2021

作者： Balaban, Beatrix-May Sacala, Ioan Stefan Petrescu-Nita, Alina Claudia Pop, Florin University Politehnica of Bucharest Department of System Engineering Faculty of Automatic Control and Computers Bucharest Romania

ISBN: (纸本)9781665439398

Nowadays, intelligent vehicles are becoming more and more important in the transportation field. What makes such a vehicle vulnerable is the number of interconnected components that individually have a strong security but can cause weakness points when they are connected. The challenge is to design and build a powerful system that uses both cyber-physical components. The proposed model is a system with integrated modules for fingerprint and face recognition to certify that a driver has the right to access the vehicle. The aim is to develop a security system for intelligent vehicles that can be also a secure keyless entry system using the power of biometrics. When a driver approaches the car, a sensor will activate the recognition modules. Only after the total completion of the recognition process the driver is authenticated and has the right to use the vehicle. We simulated real-time face detection and eye tracking using Matlab 2020a. Face detection is performed using Viola-Jones algorithms and the tracking is made with bbox-es. Besides normal conditions, other tests were made to challenge the algorithm, for example: angle rotations, the use of glasses, face mask, glasses and face mask together. © 2021 IEEE.

关键词： Cyber Physical system

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Risk-Aware Wasserstein Distributionally Robust control of Vessels in Natural Waterways

arXiv

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arXiv 2023年

作者： Nadales, Juan Moreno Hakobyan, Astghik de la Peña, David Muñoz Limon, Daniel Yang, Insoon The Department of Automatic Control and System Engineering University of Seville Spain The Department of Electrical and Computer Engineering Seoul National University Korea Republic of

In the realm of maritime transportation, autonomous vessel navigation in natural inland waterways faces persistent challenges due to unpredictable natural factors. Existing scheduling algorithms fall short in handling these uncertainties, compromising both safety and efficiency. Moreover, these algorithms are primarily designed for non-autonomous vessels, leading to labor-intensive operations vulnerable to human error. To address these issues, this study proposes a risk-aware motion control approach for vessels that accounts for the dynamic and uncertain nature of tide islands in a distributionally robust manner. Specifically, a model predictive control method is employed to follow the reference trajectory in the time-space map while incorporating a risk constraint to prevent grounding accidents. To address uncertainties in tide islands, a novel modeling technique represents them as stochastic polytopes. Additionally, potential inaccuracies in waterway depth are addressed through a risk constraint that considers the worst-case uncertainty distribution within a Wasserstein ambiguity set around the empirical distribution. Using sensor data collected in the Guadalquivir River, we empirically demonstrate the performance of the proposed method through simulations on a vessel. As a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. This stands in contrast to existing non-robust controllers, highlighting the robustness and practical applicability of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Dimensionality reduction of hyperspectral images using an ICA-based stochastic second-order optimization algorithm

Dimensionality reduction of hyperspectral images using an IC...

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European control Conference (ECC)

作者： Daniela Lupu Ion Necoara Tudor C. Ionescu Liliana Ghinea Joseph Garrett Tor A. Johansen Automatic Control and System Engineering Department University Politehnica Bucharest Bucharest Romania Automatic Control Department Dunarea de Jos University of Galati Romania Norwegian University of Science and Technology Trondheim Norway

Hyperspectral imaging is one of the advanced remote sensing techniques whose goal is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials or detecting processes. However, the high dimensional nature of hyperspectral images makes their analysis complex. Various methods have been developed to reduce the dimension of hyperspectral images. Most commonly used dimension reduction techniques are Principal Component Analysis (PCA) and Independent Component Analysis (ICA). PCA is a method to reduce the dimensionality by removing the correlation among the bands, while ICA finds additively independent components. FastICA is one of the most used ICA algorithms. It is based on maximizing a loss derived from the fourth order statistical moment (kurtosis) or negentropy, which are both non-convex functions. Moreover, FastICA can find irrelevant stationary points (no maxima) and is not scalable as it uses at each iteration the whole set of pixels. In this paper, we present a stochastic second-order Taylor-based algorithm adapted to such ICA non-convex loss functions. Our algorithm guarantees ascent, hence it usually identifies (local) maxima. Moreover, the algorithm since it is stochastic, is scalable. Detailed numerical simulations show the superior performance of our method compared to FastICA.

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Sliding Time Window And Linguistic Ordered Weighted Averaging Based Deep Learning Pooling for Fault Diagnosis in a Wastewater Treatment Plant

SSRN

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SSRN 2024年

作者： Beneyto-Rodriguez, Alicia Sainz-Palmero, Gregorio I. Galende-Hernández, Marta Fuente, María J. Department of System Engineering and Automatic Control School of Industrial Engineering Universidad de Valladolid Paseo Prado de la Magdalena 3-5 Valladolid47011 Spain

Fault diagnosis is a key activity for any industrial process. Its frequently high complexity and large-scale require smart methodologies for fault diagnosis to optimize operation and avoid catastrophic faults. Nowadays, these large-scale and complex industrial processes are usually monitored. One of the most popular approaches for fault diagnosis is the deep learning based methodologies, which have shown a good ***, a methodology for fault diagnosis in a wastewater treatment plant is proposed based on a sliding time window and a linguistic Ordered Weighted Averaging (OWA) pooling based deep convolutional neural network. This sliding window over input data, based on the sampling time, allows an earlier fault diagnosis of the plant using a few monitoring samples. The diagnosis is carried out by the linguistic OWA pooling based Deep Convolutional Neural Network. This alternative linguistic pooling uses the well-known linguistic OWA quantifiers, which permit terms such as Most, Atleast, etc., supplying new intuitive options for the pooling *** sliding time window permits a better diagnosis performance as well as an earlier diagnosis with less training. Several linguistic OWA operators have been checked with a benchmark for wastewater treatment plants. A collection of 5 fault types has been used, taking into account 140 variables sampled at 15 minutes time intervals for sampling. The performance has been over 91% for Accuracy, Recall, F1-score, etc., and better than other methodologies. Moreover, these linguistic OWA operators for pooling have shown a better performance than the standard max and average options. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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Computing the explicit MPC solution in the constrained zonotope case

Computing the explicit MPC solution in the constrained zonot...

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IEEE Conference on Decision and control

作者： Florin Stoican Ştefan S. Mihai Martin Mönnigmann Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest 313 Splaiul Independenţei 060042 Sector 6 Bucharest Romania Martin Mönnigmann is with Automatic Control and System Theory Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem’s constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution.

关键词： State-space methods Predictive control

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization

Coordinate projected gradient descent minimization and its a...

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IEEE Conference on Decision and control

作者： Flavia Chorobura Daniela Lupu Ion Necoara Automatic Control and System Engineering Department University Politehnica Bucharest Bucharest Romania

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper we consider large-scale composite nonconvex optimization problems having the objective function formed as a sum of three terms, first has block coordinate-wise Lipschitz continuous gradient, second is twice differentiable but nonseparable and third is the indicator function of some separable closed convex set. Under these general settings we derive and analyze a new cyclic coordinate descent method, which uses the partial gradient of the differentiable part of the objective, yielding a coordinate gradient descent scheme with a novel adaptive stepsize rule. We prove that this stepsize rule makes the coordinate gradient scheme a descent method, provided that additional assumptions hold for the second term in the objective function. We also present a worst-case complexity analysis for this new method in the nonconvex settings. Numerical results on orthogonal nonnegative matrix factorization problem also confirm the efficiency of our algorithm.

关键词： Linear programming Minimization Complexity theory Optimization Convergence

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Security issues in MCPS when using Wireless Sensor Networks

Security issues in MCPS when using Wireless Sensor Networks

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E-Health and Bioengineering Conference (EHB)

作者： Balaban Béatrix-May Sacală Ioan Ştefan Petrescu-Niţă Alina-Claudia Simen Radu Department of System Engineering Faculty of Automatic Control and Computers UPB Bucharest Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear.

关键词： Wireless sensor networks Privacy Exchange rates Data privacy Medical services Cyber-physical systems Security

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Data-driven modeling of heat pumps and thermal storage units for MPC

Data-driven modeling of heat pumps and thermal storage units...

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2023 International Conference on the Built Environment in Transition, CISBAT 2023

作者： Brandes, M. Cai, H. Vivian, J. Croci, L. Heer, P. Smith, R. Urban Energy Systems Laboratory Empa Dübendorf Switzerland Dept. of Industrial Engineering University of Padova Padua Italy Dept. of Power System Development Ricerca Sistema Energetico SpA Milan Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland

Heat pumps can play a crucial role in the European energy strategy 2050, which aims to achieve net-zero greenhouse gas emissions. When coupled with thermal energy storage and integrated with advanced control strategies, heat pump operation can be optimized to reduce carbon footprint and respond to the needs of system operators. However, to scale in a multitude of buildings, the transferability of the modeling into heterogeneous systems is crucial. In this paper, two different interpretable linear models, a hybrid (grey-box) and a fully data-driven (black-box) model are investigated. Specifically, two regression-based identification methods (SINDYc and DMDc) are used for dynamic models and the LASSO regression is used for static models. The transferability of the approach is evaluated using two real-world facilities with heterogeneous sizing and configuration. The results show a similar simulation performance for both cases with a maximum normalized RMSE of 0.41 and 0.60, respectively. This confirms the transferability of the approach that is necessary for large-scale implementation. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Carbon footprint

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On Convergence in Distribution of Stein's Unbiased Risk Hyper-parameter Estimator for Regularized system Identification 41

On Convergence in Distribution of Stein's Unbiased Risk Hype...

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第41届中国控制会议

作者： Yue Ju Tianshi Chen Biqiang Mu Lennart Ljung School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of Sciences Division of Automatic Control Department of Electrical EngineeringLink?ping University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Asymptotic theory for the regularized system identification has received increasing interests in recent *** this paper,for the finite impulse response(FIR) model and filtered white noise inputs,we show the convergence in distribution of the Stein's unbiased risk estimator(SURE) based hyper-parameter estimator and find factors that influence its convergence *** particular,we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator,and then demonstrate their relation numerically.

关键词： Asymptotic theory Stein's unbiased risk estimator Hyper-parameter estimator Regularized system identification

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