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27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Andriushchenko, Ekaterina Kallaste, Ants Vaimann, Toomas Rassolkin, Anton Heidari, Hamidreza Demidova, Galina L. Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728141589

In recent years, there has been an increasing interest in renewable energy. Particularly, wind energy is gaining more and more popularity in many countries. However, the main obstacles to wind turbines usage are the vibration and noise close to wind farms. Therefore, this paper is focused on developing a new method for mathematical simulation of vibrations of a wind turbine in order to find out and reduce the vibration level. Particularly, low-frequency vibration of a wind turbine, composed of a tower and rotor in the out-of-balance condition is considered. The proposed method of vibration simulation is based on the theory of elastic beams and the general vibration theory. The calculations are made for a wind turbine VTR 50 Arctic. The results of the calculations are the displacement of the tower top and the stress at the tower foundation. Furthermore, it is worth emphasizing that the method is quite flexible. It allows adding complexity into the system, for instance, external loads such as wind pressure, aerodynamic loads, seismic loads or operational loads. © 2020 IEEE.

关键词： Wind power

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Charge process management of lithium-ion batteries based on digital twins: A new way to extend life

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Journal of Process control 2025年 152卷

作者： Chunhui Ji Guang Jin Ran Zhang The Department of Control Science and Engineering School of systems Engineering National University of Defense Technology Changsha 410003 China The Department of Management Science and Engineering School of systems Engineering National University of Defense Technology Changsha 410003 China

Effective management of the charging process is not only crucial for reducing costs and improving the efficiency of electric vehicles and renewable energy systems but also essential for enhancing the stability and safety of energy systems. Consequently, it has become a key focus in battery management system research. However, there is a contradiction between improving battery charging efficiency and extending service life, and the diversified battery use needs make this contradiction more prominent. This paper proposes a lithium-ion battery charging process management framework based on digital twin technology and Bayesian principle. A hybrid model is used to establish the digital twin of the lithium-ion battery charging state and health state. At the same time, the model evaluation reward and maturity evaluation reward are comprehensively considered in the decision-making process to improve the effectiveness of decision-making. Furthermore, the charging strategy is optimized according to the distinct characteristics of different battery life stages. The case analysis demonstrates that, compared to existing charging strategies, the proposed method effectively extends battery lifespan while meeting various battery performance requirements.

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Switching dynamics of memristive device under periodic pulse stimulation

Switching dynamics of memristive device under periodic pulse...

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School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR)

作者： Denis Bolshakov Alexey Belov Mikhail Mishchenko Victor Kazantsev Alexey Mikhaylov Department of Theory of Oscillations and Automatic Control Faculty of Radiophysics N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Solid State Electronics and Optoelectronics Laboratory of Physics and Technology of Thin Films Research Institute of Physics and Technology Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neurotechnologies Department N.I. Lobachevsky State University of Nizhny Novgorod Center for Neuroscience and Mashine Learning Immanuel Kant Baltic Federal University Nizhny Novgorod Russia

The study of switching dynamics of memristive devices is a topical direction both in fundamental and applied investigation of memristive technologies. The main problem in this field is a stochastic nature of switching between resistive states of memristive device. This feature leads to the large variance of resistive states during switching and difficulties in setting specific intermediate states of memristive device. We propose the novel method of setting intermediate states of memristive device by controlling parameters of external pulse stimulation. We have demonstrated experimentally the possibility of switching and holding the resistive states of memristive device by pulse-width modulation (PWM).

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Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

arXiv

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

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Stochastic MPC for Energy Management in Smart Grids with Conditional Value at Risk as Penalty Function

Stochastic MPC for Energy Management in Smart Grids with Con...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Janani Venkatasubramanian Vahab Rostampour Tamás Keviczky Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Engineering and Technology Institute University of Groningen Groningen The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171012

This paper considers imbalance problems arising in Energy Management in Smart Grids (SG) as discrete-time stochastic linear systems subject to chance constraints, and proposes a Model Predictive control (MPC) approach to solve them. It is well-known that handling the closed-loop constraint feasibility of such systems is in general difficult due to the presence of a potentially unbounded uncertainty source. To overcome such a difficulty, we propose two new ideas. We first reformulate the chance constraint using the so-called Conditional Value at Risk (CVaR), which is known to be the tightest convex approximation for chance constraints. We then relax the CVaR constraint using a penalty function depending on a coefficient parameter. An optimal solution is therefore obtained by solving a single unconstrained problem which, intuitively, takes into consideration a risk of the system trajectories in an undesirable state. A case study using an academic example is presented to estimate the a-posteriori probability of the coefficient parameter in order to show when such a penalty function is exact by means of probabilistic constraint fulfillment.

关键词： Uncertainty Stochastic processes Probabilistic logic Smart grids Trajectory Energy management Predictive control

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices

arXiv

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arXiv 2022年

作者： Yao, Canqi Chen, Shibo Salazar, Mauro Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology EindhovenMB 5600 Netherlands

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator. © 2022, CC BY-NC-SA.

关键词： Sales

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Decentralized Stealth Attacks on Cyber-Physical systems

arXiv

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arXiv 2025年

作者： Ye, Xiuzhen Esnaola, Iñaki Perlaza, Samir M. Harrison, Robert F. Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Lyon France

Decentralized stealth attack constructions that minimize the mutual information between the state variables and the measurements are proposed. The attack constructions are formulated as random Gaussian attacks targeting Cyber-physical systems that aims at minimizing the mutual information between the state variables and measurements while constraining the Kullback-Leibler divergence between the distribution of the measurements under attacks and the distribution of the measurements without attacks. The proposed information metrics adopted measure the disruption and attack detection both globally and locally. The decentralized attack constructions are formulated in a framework of normal games. The global and local information metrics yield games with global and local objectives in disruption and attack detection. We have proven the games are potential games and the convexity of the potential functions followed by the uniqueness and the achievability of the Nash Equilibrium, accordingly. We proposed a best response dynamics to achieve the Nash Equilibrium of the games. We numerically evaluate the performance of the proposed decentralized stealth random attacks on IEEE test systems and show it is feasible to exploit game theoretic techniques in decentralized attack constructions. © 2025, CC BY.

关键词： Gaussian distribution

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Data Augmentation and the Improvement of the Performance of Convolutional Neural Networks for Heart Sound Classification

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第4期49卷 1033页

作者： Takezaki, Shumpei Kishida, Kazuya Graduate School of Systems Life Sciences Kyushu University 744 MotookaNishi-kuFukuoka Fukuoka-shi8190395 Japan Department of Electronic Control Engineering National Institute of Technology Kagoshima College 1460-1 ShinkoHayatoKagoshima Kirishima8995102 Japan

The effective management and early diagnosis of cardiovascular diseases (CVDs) are crucial to bring down the mortality associated these diseases. Because detecting CVDs can be a difficult task, especially when no symptoms are present, developing systems that can detect CVDs or heart abnormalities automatically has attracted significant research attention in recent times. For this purpose, several convolutional neural networks (CNNs) learned using heart sound signals (i.e., the phonocardiogram or PCG) have been proposed. Generally, CNNs require a high volume of annotated training data to achieve high performance. However, annotated PCG (i.e., PCGs labelled as abnormal or normal) dataset is not sufficient for training CNNs. To address this issue, the classification performance of CNNs need to be improved, so that they can be trained even on an insufficient PCG database. In this study, we consider two data augmentation (DA) methods: window slicing with spectrogram, which slices a single PCG to generate multiple signals and transforms the signals into spectrogram data;the other is a synthetic spectrogram based generative adversarial network, which generates synthetic data. To demonstrate the validity of the two DA methods, we performed experiments concerning heart sound detection and discussed the results of the experiments in terms of accuracy, sensitivity, and specificity. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Convolution

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

Design and Application of a One-Stop Mobile Application Shar...

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Chinese control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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