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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Information and Intelligent Forecasting systems Based on the Methods of Neural Network Theory

Information and Intelligent Forecasting Systems Based on the...

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Yuliia Kostiuk Olena Kryvoruchko Alona Desyatko Yuliia Samoilenko Kateryna Stepashkina Rostislav Zakharov Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyiv Ukraine Department of automation and computer technologies of control systems National University of Food Technologies Kyiv Ukraine

This paper is dedicated to the problem of the research of the information and intelligent forecasting systems which solve the problems of regression analysis. This class of the systems includes the systems that use different mathematical apparatus for forecasting, for the implementation of which a wide range of methods and algorithms is used. The paper also deals with the problems of regression analysis using elements of neural network theory. Developing a set of functions to form an approximating function allows you to create models that work efficiently. In existing information and intelligence systems, either implemented functions adapted to solve specific forecasting tasks, or, as in the case of neuropackages, it is considered that the proposed functions can be used to solve a wide range of tasks, adaptation to the conditions of the task occurs during the design of the neural network architecture. To create an approximating function, it is possible to implement linear and non-linear functions, which have proven themselves well in neural network analysis for solving a wide range of tasks and are obtained within the framework of deterministic chaos theory. To implement the function that approximates the dependence of the predicted value on the forecast arguments, the use of a two-layer heterogeneous, artificial neural network without feedback is proposed.

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Improving PPG-based Heart-Rate Monitoring with Synthetically Generated Data

Improving PPG-based Heart-Rate Monitoring with Synthetically...

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2022 IEEE Biomedical Circuits and systems Conference, BioCAS 2022

作者： Burrello, Alessio Pagliari, Daniele Jahier Bianco, Marzia MacIi, Enrico Benini, Luca Poncino, Massimo Benatti, Simone Dei Università di Bologna Bologna Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Integrated Systems Laboratory Eth Zurich Switzerland Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Turin Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Improving the quality of heart-rate monitoring is the basis for a full-time assessment of people's daily care. Recent state-of-the-art heart-rate monitoring algorithms exploit PPG and inertial data to efficiently estimate subjects' beats-per-minute (BPM) directly on wearable devices. Despite the easy-recording of these signals (e.g., through commercial smartwatches), which makes this approach appealing, new challenges are arising. The first problem is fitting these algorithms into low-power memory-constrained MCUs. Further, the PPG signal usually has a low signal-to-noise ratio due to the presence of motion artifacts (MAs) arising from movements of subjects' arms. In this work, we propose using synthetically generated data to improve the accuracy of PPG-based heart-rate tracking using deep neural networks without increasing the algorithm's complexity. Using the TEMPONet network as baseline, we show that the HR tracking Mean Absolute Error (MAE) can be reduced from 5.28 to 4.86 BPM on PPGDalia dataset. Noteworthy, to do so, we only increase the training time, keeping the inference step unchanged. Consequently, the new and more accurate network can still fit the small memory of the GAP8 MCU, occupying 429 KB when quantized to 8bits. © 2022 IEEE.

关键词： Heart

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TEMPORAL LOGIC control FOR NONLINEAR STOCHASTIC systems UNDER UNKNOWN DISTURBANCES

arXiv

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arXiv 2024年

作者： Gracia, Ibon Laurenti, Luca Mazo, Manuel Abate, Alessandro Lahijanian, Morteza Smead Department of Aerospace Engineering Sciences CU Boulder BoulderCO United States Delft Center for Systems and Control Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands Department of Computer Science University of Oxford United Kingdom

In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic specifications. The proposed framework is data-driven and abstraction-based: leveraging observations of the system, our approach learns a high-confidence abstraction of the system in the form of an uncertain Markov decision process (UMDP). The uncertainty in the resulting UMDP is used to formally account for both the error in abstracting the system and for the uncertainty coming from the data. Critically, we show that for any given state-action pair in the resulting UMDP, the uncertainty in the transition probabilities can be represented as a convex polytope obtained by a two-layer state discretization and concentration inequalities. This allows us to obtain tighter uncertainty estimates compared to existing approaches, and guarantees efficiency, as we tailor a synthesis algorithm exploiting the structure of this UMDP. We empirically validate our approach on several case studies, showing substantially improved performance compared to the state-of-the-art. © 2024, CC BY.

关键词： Temporal logic

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Making children read more and better by integrating natural language processing and gamification techniques into mobile applications

Making children read more and better by integrating natural ...

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International Conference on control systems and computer Science (CSCS)

作者： George Cătălin Voicu Sorin N. Ciolofan Monica Drăgoicea Manager eMAG Bucharest Romania Department of Computer Science Politehnica University of Bucharest Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania

It is well known that nowadays children tend to spend lot of time playing games on mobile devices at the expense of reading books. The current research aims to explore ways to design and develop a mobile application, attractive for children, that uses voice recognition technology and gamification techniques to help children read more and better and to track the progress of reading. The application is implemented with Flutter, Dart and Firebase and is addressed to children, the books being made available in encyclopedia format. By integrating technologies such as NLP and gamification the application aims to improve reading fluency among children, but also to give them the opportunity to accumulate new and exciting information in the same time.

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Optimized Model Predictive control for Unmanned Aerial Vehicles with Sensor Uncertainties

Optimized Model Predictive Control for Unmanned Aerial Vehic...

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2022 International Conference on control, Automation and Diagnosis, ICCAD 2022

作者： Taher Azar, Ahmad Serrano, Fernando E. Ahmad Kamal, Nashwa Qureshi, Basit Al Mhdawi, Ammar K. Humaidi, Amjad J. Kasim Ibraheem, Ibraheem Ben Njima, Chakib Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Faculty of Engineering and Architecture Tegucigalpa Honduras Cairo University Faculty of Engineering Egypt Hill University United Kingdom University of Technology Control and Systems Engineering Department Baghdad Iraq Dijlah University College Department of Computer Techniques Engineering Baghdad10001 Iraq University of Sousse Tunisia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

In this paper, a novel control strategy based on model predictive control is proposed for tracking the trajectory of unmanned aerial vehicles (UAV). First, the dynamic model of an unmanned aerial vehicle UAV is established by considering the linear velocities in terms of the analyzed system's attitude angles. The UAV's dynamic model is suitable for kinematic control by using two attitude angles as control inputs. The controller design is made up of an output feedback controller formed by the error variables, which are the difference between the measured output and the reference variables. Then, a model predictive control is established by considering an optimization problem with a performance index and the corresponding constraints. Furthermore, the stochastic properties of the UAV dynamic model are taken into account in this research study. The measured outputs provided by the sensors are constrained by the topological characteristics of the unmanned aerial vehicle to be analyzed. Finally, a numerical experiment and its conclusions are presented, demonstrating that the proposed control strategy provides optimal trajectory tracking. © 2022 IEEE.

关键词： Model predictive control

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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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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PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zihan Li Xiaodong Wang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Finance Operations and Information Systems (FOIS) Brock University Canada Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

关键词： Training Privacy Data privacy Protocols Federated learning Training data Servers Security Global communication Homomorphic encryption

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