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3rd International Conference on Intelligent Engineering and Management, ICIEM 2022

作者： Anitha Christy Angelin, P. Samuel Devakumar, P.V. Al-Farhani, Liwa H. Kumar, Gandam Vijay Deep, Shaurya Mohammad, Shoaib PSNA College of Engineering and Technology Tamilnadu Dindigul India Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India RUDN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Career Point University Computer Science and Engineering Rajasthan India IIMT College of Management Greater Noida India IMS Unison University Department of Law Uttarakhand Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge. © 2022 IEEE.

关键词： Fog computing

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Path-Integral Formula for Computing Koopman Eigenfunctions

arXiv

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arXiv 2023年

作者： Deka, Shankar A. Narayanan, Sriram S.K.S. Vaidya, Umesh The Decision and Control Systems division The Dept. of Electrical Engr. and Computer Science KTH Royal Institute of Technology Sweden The Dept. of Mechanical Engr. Clemson University SC Clemson United States

The paper is about the computation of the principal spectrum of the Koopman operator (i.e., eigenvalues and eigenfunctions). The principal eigenfunctions of the Koopman operator are the ones with the corresponding eigenvalues equal to the eigenvalues of the linearization of the nonlinear system at an equilibrium point. The main contribution of this paper is to provide a novel approach for computing the principal eigenfunctions using a path-integral formula. Furthermore, we provide conditions based on the stability property of the dynamical system and the eigenvalues of the linearization towards computing the principal eigenfunction using the path-integral formula. Further, we provide a Deep Neural Network framework that utilizes our proposed path-integral approach for eigenfunction computation in high-dimension systems. Finally, we present simulation results for the computation of principal eigenfunction and demonstrate their application for determining the stable and unstable manifolds and constructing the Lyapunov function. © 2023, CC BY.

关键词： Eigenvalues and eigenfunctions

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories 14

Assessment and recognition in technical massive open on-line...

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14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Martin, Sergio Castro, Manuel Sancristobal, Elio Diaz, Gabriel Garcia-Loro, Felix Perez, Clara Quintana, Blanca Gil, Rosario Plaza, Pedro Lopez, Africa Carro, German Robles, Antonio Tobarra, Llanos Rodriguez-Artacho, Miguel Ruiperez-Valiente, Jose A. Universidad Nacional de Educación A Distancia Electrical and Computer Engineering Department Madrid Spain Universidad Nacional de Educación A Distancia Dep. Sistemas de Comunicación y Control Madrid Spain Universidad Nacional de Educación A Distancia Dep. Lenguajes y Sistemas Informáticos Madrid Spain Masschusetts Institute of Technology Teaching Systems Lab United States

ISBN: (纸本)9798350399431

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done. © 2023 IEEE.

关键词： Drops

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Real-time gesture control UAV with a low resource framework

Real-time gesture control UAV with a low resource framework

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2021 International Symposium of Asian control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Bhat, Manohar Mahto, Gopikishan Kesaria, Smit Femandes, Vikrant Arya, Kavi BMS College of Engineering Dept. of Electrical and Electronics Bangalore560019 India UPU Government Polytechnic Dept. of Computer Science and Engg. Durg491001 India Indian Institute of Technology Dept. of Systems and Control Mumbai400076 India Indian Institute of Technology Dept. of Computer Science and Engg. Mumbai400076 India

ISBN: (纸本)9781665433235

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving depth information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.

关键词： Drones

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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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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Aperiodic Communication for MPC in Autonomous Cooperative Landing ⁎

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IFAC-PapersOnLine 2021年第6期54卷 113-118页

作者： Dženan Lapandić Linnea Persson Dimos V. Dimarogonas Bo Wahlberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous agents, with nonlinear dynamics, are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on distributed Model Predictive control (MPC). The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm only requires the agents to exchange information when they can not guarantee to rendezvous. Hence, the exchange of information occurs aperiodically, which reduces the necessary communication between the agents. Furthermore, we prove that the algorithm guarantees recursive feasibility. The simulation results illustrate the effectiveness of the proposed algorithm applied to the problem of autonomous cooperative landing.

关键词： Autonomous cooperative landing Nonlinear predictive control Model predictive optimization-based control Distributed nonlinear control UAVs Tracking

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Fuzzy System of Wind Turbine Defect Image Processing

Fuzzy System of Wind Turbine Defect Image Processing

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadia Vasylkiv Department of Computer Engineerin West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Wind turbines are an important component of the global strategy for the transition to renewable energy sources and the fight against climate change. Their implementation contributes to sustainable development and to improving the ecological situation of the planet. Detecting and elimination of defects helps reduce the wear of the components and prevent premature failure. This can significantly extend the life of wind turbines, which is economically beneficial for owners and operators. The use of UAVs allows for minimizing the risks for personnel, as inspection and diagnosis of defects can be carried out without the need to lift people to great heights or stay in dangerous areas. This is especially important for turbines located in places of difficult access. The images taken by the UAV contain many extraneous objects and fuzzy data. Fuzzy logic allows you to create models for pattern recognition and object classification in images, taking into account uncertainty and variation in the data. This increases the accuracy and reliability of pattern recognition systems. This article proposes an image processing system to detect wind turbine defects, with a fuzzy system module that selects images from a video stream for further processing.

关键词： Fuzzy logic Renewable energy sources Costs Accuracy Uncertainty Image processing Streaming media Wind turbines Pattern recognition Fuzzy systems

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Application of Petri Nets for Modeling Ore Flows to Create Dynamic Management and Quality control System in Mineral Resource Complexes

Application of Petri Nets for Modeling Ore Flows to Create D...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Pavel Larichev Vadim Tynchenko Ivan Nekrasov Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Laboratory of Biofuel Composition Siberian Federal Univercity Krasnoyarsk Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

The article discusses the issue of creating a dynamic system for managing and monitoring the quality of ore flow in mineral resource complexes using simulation modeling. The purpose of this study is to adapt the mathematical apparatus of Petri nets to such modeling. The article describes the choice and justification of the simulation modeling apparatus and provides a conceptual representation of the ore flow model in the form of an adaptation of a continuous Petri net. An architectural solution is proposed that covers a number of processes, such as geological exploration, mining, moving loads in the business processes of enterprises, but also to support the rock mass. The proposed architecture is being coordinated with information systems and software and hardware of mining enterprises, which makes it possible to identify data needs for the formation of technical specifications for the correction of business processes. The proposed control system will make it possible to implement simulation modeling both in model and real time for timely and adequate assessment of the progress of work being carried out and clarifying the reasons for the lag behind planned schedules using a Petri net.

关键词： Adaptation models Ores Petri nets Mathematical models Real-time systems Delays Data mining

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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Formal safety verification of unknown continuous-time systems: A data-driven approach 24

Formal safety verification of unknown continuous-time system...

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24th ACM International Conference on Hybrid Systems Computation and control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021

作者： Lavaei, Abolfazl Nejati, Ameneh Jagtap, Pushpak Zamani, Majid Institute for Dynamic Systems and Control Eth Zurich Switzerland Department of Electrical Engineering Technical University of Munich Germany Department of Electrical Engineering Kth Royal Institute of Technology Sweden Department of Computer Science University of Colorado Boulder United States

ISBN: (纸本)9781450383394

This work studies formal verification of continuous-time continuous-space systems with unknown dynamics against safety specifications. The proposed framework is based on a data-driven construction of barrier certificates using which the safety of unknown systems is verified via a finite set of data collected from trajectories of systems with a priori guaranteed confidence. In the proposed scheme, we first cast the original safety problem as a robust convex program (RCP). Since the unknown model appears in one of the constraints of the proposed RCP, we provide the scenario convex program (SCP) corresponding to the original RCP by collecting finite numbers of data from systems' evolutions. We then establish a probabilistic closeness between the optimal value of SCP and that of RCP. Accordingly, we formally quantify the safety guarantee of unknown systems based on the number of data and the required level of safety confidence. Motivations. In the past few years, formal methods have become a promising approach to analyze dynamical systems against high-level logic properties, e.g., those expressed as linear temporal logic (LTL) formulae, in a reliable way. In this regard, barrier certificates, as a discretization-free approach, have received significant attention as a useful tool for formal analysis of complex dynamical systems. In particular, barrier certificates are Lyapunov-like functions defined over the state space of systems subjected to a set of inequalities on both the function itself and its time derivative along the flow of the system. The existence of such a function provides a formal certificate for the safety of the system [1, 2]. To employ the proposed approaches in the setting of barrier certificates, one needs to know precise models of dynamical systems and, hence, those approaches are not applicable where the model is unknown. Although there are some identification techniques in the relevant literature to first learn the model and then provide the analys

关键词： Continuous time systems

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