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23rd International Conference on control Systems and computer Science Technologies, CSCS 2021

作者： Shihab, Ahmed Salah, Hussein Mocanu, Mariana College of Nursing University of Baghdad Department of Basic Sciences Baghdad Iraq Department of Computer Systems Technical Institute-Suwaira University of Middle Technical Iraq Faculty of Automatic Control and Computer University POLITEHNICA of Bucharest Bucharest Romania Faculty of Automatic Control and Computer University POLITEHNICA Baghdad Iraq

ISBN: (纸本)9781665439398

Early detection of skin cancer can contribute significantly in preventing the exacerbation of the condition and its transformation into a benign tumor, reducing the number of deaths in skin cancer. This paper introduces the application of K-means cluster for detecting the skin tumor and the Convolutional Neural Network (CNN) for automatic diagnosis of skin tumor. The specific steps are as follows: The first step reads the skin cancer image, the second step 'preprocessing, which is achieved by applying the Gaussian filter, to improve the skin image and reduce noise, the third step: utilizing K-means cluster applied for segmentation image into two groups. The first group is represented by the background (largest area) and second group represents the skin tumor (least area). The last step (The fourth one): applying CNN to classify the skin tumor as benign or malignant. The results show that the proposed technique functions more efficiently by achieving 99.7% accuracy, obtained from the total of 22, 080 medical images based on DermQuest Digital Dataset. © 2021 IEEE.

关键词： Image segmentation

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Application of Discrete-Time Analytic Signals in Power System Analysis

Application of Discrete-Time Analytic Signals in Power Syste...

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XXII International Symposium on Information, Communication and Automation Technologies, ICAT

作者： Dženita Džafić Izudin Džafic Department for Automatic Control and Electronics University of Sarajevo Faculty of Electrical Engineering Sarajevo Sarajevo B&H

Power system analysis often involves studying the dynamics of oscillatory signals and transient events. Analytic signals provide a useful representation of real-meaured signals that contain both oscillatory and non-oscillatory components. This work examines the application of discrete-time analytic signals to analyze power system signals using Kung's and Prony's method. These methods are used to estimate the oscillatory modes present in power system signals and their associated parameters. The performance of the methods is evaluated on simulated and real power system data. The analytic signal-based techniques are shown to produce accurate results and offer benefits for automated power system monitoring and disturbance analysis.

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Deep Learning of Dynamic Systems using System Identification Toolbox™

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IFAC-PapersOnLine 2024年第15期58卷 580-585页

作者： Tianyu Dai Khaled Aljanaideh Rong Chen Rajiv Singh Alec Stothert Lennart Ljung MathWorks 1 Apple Hill Drive Natick MA 01760 USA Aeronautical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden

MATLAB® releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the System Identification Toolbox™. The emphasis has been on integrating deep learning architectures and training techniques that facilitate the use of deep neural networks as building blocks of nonlinear models. The toolbox offers neural state-space models which can be extended with auto-encoding features that are particularly suited for reduced-order modeling of large systems. The toolbox contains several other enhancements that deepen its integration with the state-of-art machine learning techniques, leverage auto-differentiation features for state estimation, and enable a direct use of raw numeric matrices and timetables for training models.

关键词： System Identification Toolbox machine learning deep learning reduced order modeling

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Wireless control of a D.C. Motor Used for Driving a Conveyor Belt

Wireless control of a D.C. Motor Used for Driving a Conveyor...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Livint, Petru Banu, Ioan-Viorel Culea, George Manale, Bouderbala Majout, Btissam Dusa, Alexandru University 'Vasile Alecsandri' of Bacau Department of Energetics and Science Computer Romania Sidi Mohammed Ben Abdellah University Fez Limas Laboratory Faculty of Sciences Dhar El Mahraz Morocco Sidi Mohammed Ben Abdellah University Fez Laboratory of Engineering Modellingand Systems Analysis Morocco 'Dunarea de Jos' University of Galati Department of Automatic and Electrical Engineering Romania

ISBN: (纸本)9798350365306

This article presents an experimental stand for the remote control of a D.C. motor used to drive a conveyor belt. This is based on two development boards Arduino Uno and two shields NRF24L01. The D. C. motor is supplied from a D.C. voltage source by means of the H type bridge. The emitter circuit sends the control signal generated through a potentiometer to the receptor circuit by means of radio waves at a frequency of 2.4 GHz. The receptor circuit receives the analogue signal emitted by the emitting circuit, transforms it into a PWM (pulse width modulation) signal and uses it for controlling the transistors of the H-bridge. For implementing and validating the remote control system of the D.C. motor two programs have been issued in the programming environment Arduino IDE for the two circuits. emitter and receptor. The remote control system of the D.C. motor has been running in the same way as the direct control of this. The remote control is used in the case of the electric actuation systems made with D.C. motors continuous used in spaces inaccessible to the human operator. © 2024 IEEE.

关键词： Belts

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Vibration Mitigation of Hybrid Oscillating Water Column-Floating Offshore Wind Turbine Using ANFIS-Based Airflow control 16th

Vibration Mitigation of Hybrid Oscillating Water Column-Floa...

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16th APCA International Conference on automatic control and Soft Computing, controlO 2024

作者： M’zoughi, Fares Aboutalebi, Payam Ahmad, Irfan Rouch, Tahereh Bagheri Garrido, Izaskun Garrido, Aitor J. De La Sen, Manuel Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Po Rafael Moreno no. 3 Bilbao48013 Spain Automatic Control Group—ACG Institute of Research and Development of Processes IIDP Department of Electricity and Electronics Faculty of Science and Technology University of the Basque Country—UPV/EHU Bo Sarriena s/n Leioa48080 Spain

ISBN: (纸本)9783031817236

This paper introduces a novel approach to model and stabilize a Floating Offshore Wind Turbine (FOWT) by employing Oscillating Water Columns (OWC) as an active structural control system. The innovative concept involves designing a new floating barge-like platform with integrated OWCs on opposite sides of the platform to mitigate undesired oscillations of the system. These OWCs counteract the bending forces caused by wind on the tower and waves on the barge platform. To synchronize the opposing forces with the system’s tilting, a proposed Adaptive Neuro-Fuzzy Inference System-based airflow control strategy is employed. Through manipulation of the barge platform’s pitch angle, the ANFIS airflow control system adjusts the valves on either side, opening one and closing the other accordingly. Simulation results, compared with a standard FOWT demonstrate the effectiveness of the ANFIS airflow control. It is shown to be superior in reducing platform pitching and the fore-aft translation of the tower top. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active Structural control Air Valve Airflow control ANFIS Barge Platform Floating Offshore Wind Turbine Oscillating Water Column

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems

On the Stabilization of a Class of OSL Conformable Fractiona...

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International Conference on Systems and control (ICSC)

作者： Ali Omar M. Alsharif A. Jmal O. Naifar Ausama H. Ahmed Hamdi Gassara A. Ben Makhlouf Electrical Engineering Department Control and energy management laboratory (CEM lab) Sfax University Engineering National School Sfax Tunisia Department of Electrical & Computer Engineering Faculty of Engineering Elmergib University Alkhums Libya Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Tunisia Department of Mathematics Faculty of Sciences of Sfax Sfax Tunisia

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class.

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Bluetooth Communications in Educational Robotics 23

Bluetooth Communications in Educational Robotics

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23rd International Conference on control Systems and computer Science Technologies, CSCS 2021

作者： Oprea, Marin Mocanu, Mariana University Politehnica of Bucharest Calarasi Romania Faculty of Automatic Control and Computer Science Computer Department University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

In a world in a continuous and rapid change, it is absolutely necessary for our students to keep up with the rapid progress of new technologies: Internet of Things (IoT), Robotics, Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR) etc. The rapid evolution and diversification of these emerging technologies has recently led to their introduction into the educational offer of the school curriculum for the gymnasium. The discipline of Information and Communication Technology (ICT) has already been implemented, a discipline that involves both the formation of skills to use new technologies and the formation of computational thinking necessary for the efficient and intelligent use of these technologies. In order to teach and learn Physics from a STEM (Science, Technology, engineering and Mathematics) educational perspective, we initiated optional school courses of IoT, Robotics and AI (approached through Machine Learning). These courses stimulate, at the level of students, computational thinking, creativity and innovation and lead, from an interdisciplinary perspective, to the development of emerging specializations such as Mathematics-Physics-Automation, Mathematics-Physics-Electronics, Mathematics-Physics-Informatics-Robotics etc. In this paper we presented a method of approaching, in the school educational space, the study of wireless communication technologies between smart devices, through an Educational Robotics project. The project consisted of creating a wireless controlled mobile robotic platform (robot car) via a Bluetooth module connected to an Arduino Uno board. © 2021 IEEE.

关键词： Students

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Data-Driven Model for Human Tracking and Prediction Using Kalman Filter with Particle Swarm Optimization 3rd

Data-Driven Model for Human Tracking and Prediction Using Ka...

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3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ajasa, Abiodun Afis Nawawi, Sophan Wahyudi Department of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Electronics and Computer Engineering Faculty of Engineering Lagos State University Epe Campus Lagos Nigeria

ISBN: (纸本)9789811939228

Intelligent monitoring systems have evolved due to technological improvement and innovation in biometric identification technology and protecting lives and property. As a result, intelligent monitoring systems are becoming increasingly prevalent. Consequently, this paper proposes the development of an IoT-based Human Tracking system that incorporates the Kalman Filter (KF) Algorithm. Apart from the deployment of the Kalman Filter, analysis was also done with an optimized Kalman filter with PSO (KF-PSO) using Particle Swarm Optimization. Rather than manually tuning the process noise error, R, and measurement error, Q, which are involved in KF, PSO was also used to adjust the errors to obtain their best values for optimal estimation. As a result, a two-dimensional (2D) Kalman filter is developed. The positions and velocities of the object being tracked (i.e., humans) are estimated in x- and y-directions. The proposed system has been evaluated using ten different human datasets, each consisting of 100 samples. The quality performance of KF and KF-PSO models was also compared using accuracy analysis. In comparison, the KF-PSO model yielded an average Mean-Square error of 17 mm (i.e., 1.7% error), while the conventional KF model gave an average Mean-Square error of 22 mm (i.e., 2.2% error). Hence, the KF-PSO model is a better filter than the conventional KF model because of its higher accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Kalman filters

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A CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients 5

A CNN Approach To Predicting Non-Invasive Blood Pressure of ...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Ko, Pin-You Lai, Lien-Fu Ku, Tien-Hsiung Lin, Yue-Der National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan Changhua Christian Hospital Ai Development Center Department of Anesthesiology Changhua Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The occurrence of intraoperative hypertension/ hypotension may cause danger to a patient. Therefore, the monitoring of blood pressure change during surgical operation is momentous for anesthetized patient undergoing surgical procedures. The invasive method of measuring arterial blood pressure (ABP) provides accurate and much information, but it requires evaluation by an anesthesiologist and with its long-term use, it also brings a higher risk of infection, and even some patients have pain and discomfort at the catheter placement site after surgery, so it is not suitable for all types of patients. On the other hand, non-invasive method of measuring cuff blood pressure provides little information because of non-continuous measurement. With the emerging of deep learning, there has been more and more discussion on whether non-invasive Photoplethysmography (PPG) can replace invasive ABP as an alternative to continuous BP measurement and even BP prediction. This paper aims to implement a deep learning model to predict SBP/DBP signal by PPG, PPG extended data and Electrocardiography (ECG) data. We trained the CNN-based architecture on preprocessed data including ECG, PPG, 1st and 2nd derivative PPG signal extracted from MIMIC-III(Medical Information Mart for Intensive Care III) waveform database matched subset to predict continuous ABP signals using real world data. The value of SBP and DBP are directly predicted to effectively estimate the accuracy of the predictions. The prediction results fulfill the grade A in British Hypertension Society (BHS) standard and most part of Association for the Advancement of Medical Instrumentation (AAMI)'s standard. The proposed model has been applied for CHANGHUA CHRISTIAN HOSPITAL's IRB NO:191240. The results depict that our proposed model could effectively predict SBP/DBP signal and be deployed to the operating room realistically. © 2022 IEEE.

关键词： Blood pressure

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