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E-Health and Bioengineering Conference (EHB)

作者： Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Grigore T. Popa University of Medicine and Pharmacy of Iasi Faculty of Medical Bioengineering Iaşi Romania Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Cardiac conditions are one of the leading causes of death worldwide. To address this, we designed a model that can detect cardiac conditions from a patient's electrocardiogram (ECG). This will convert the 1D ECG signal into a 2D scalogram using wavelet transforms with Morlet wavelets. With the help of a network based on the AlexNet convolutional neural network, it distinguishes between a normal ECG signal and one indicative of arrhythmia or congestive heart failure. The resulting model performs well, achieving a classification accuracy of 92.20% and providing a robust approach for detecting cardiac conditions.

关键词： Wavelet transforms Training Time-frequency analysis Accuracy Biological system modeling Electrocardiography Predictive models Congestive heart failure Convolutional neural networks Biomedical engineering

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jawhar Ghommam Raouf Fareh Ibrahim Al Naimi Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Laboratory of Control Energy Management ENIS Tunisa Electrical Engineering Dept. University of Sharjah UAE

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach.

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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International Conference on Computing Communication control and Automation (ICCUBEA)

作者： Supriya Shirsath Vaishali Vikhe Pratap Vikhe Dept. of Computer Engineering Pravara Rural Engineering College Ahmednagar India Dept. of Instrumentation & Control Engineering Pravara Rural Engineering College Ahmednagar India

ISBN: (纸本)9781665484527

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset.

关键词： Training Emotion recognition Computational modeling Face recognition Anxiety disorders Neural networks Machine learning

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Direct data-driven LPV control of nonlinear systems: An experimental result

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IFAC-PapersOnLine 2023年第2期56卷 2263-2268页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology 5600MB Eindhoven The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control 1111 Budapest Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

关键词： Data-Driven control Linear Parameter-Varying Systems

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Computationally efficient predictive control based on ANN state-space models

arXiv

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arXiv 2023年

作者： Hoekstra, Jan H. Cseppento, Bence Beintema, Gerben I. Schoukens, Maarten Kollár, Zsolt Tóth, Roland Dept. of Measurement and Information Systems The Budapest University of Technology and Economics Hungary The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system. © 2023, CC BY-SA.

关键词： Neural networks

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Fuzzy Logic controller for the Chemotherapy of Brain Tumor

Fuzzy Logic Controller for the Chemotherapy of Brain Tumor

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Muhammad Zubair Daniela Iacoviello Iqra Shafeeq Mughal Dept. Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. di Ingegneria Electrica Electronica e Informatica DIEEI University of Catania Catania Italy

An advanced Fuzzy Logic controller (FLC) that considers all the states of the brain tumor system is designed for the chemotherapy treatment. A Mamdani-type FLC is proposed for dynamically controlling the chemotherapy drug for the tumor system; the chemotherapy treatment of brain tumors requires advanced strategies which mainly depend upon the severity of the tumor. In this work, the advanced FLC designed aims both at determining the amount of chemotherapy to eliminate tumor cells, and at preserving the minimum amount of healthy and immune cells. The controller's performance is verified using MATLAB software based on different control parameters, showing its effectiveness in reducing the tumor cells. It has shown favorable results in terms of steady-state error, rate of convergence, and amount of drug consumed.

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops

A Framework Leveraging Robotics and Machine Learning Technol...

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Nikolaos Kounalakis Emmanouil Kalykakis Dimitrios Kosmopoulos John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework.

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Improvement of Protection Factor for a controlled Laboratory computer Network Using Artificial Intelligence

Improvement of Protection Factor for a Controlled Laboratory...

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engineering, Science and Advanced Technology (ICESAT), International Conference on

作者： Raid W. Daoud Enas F. Aziz Zaki M. Abdullah Ziyaad H. Saleh Electrical Tech. Dept. Northern Technical University Kirkuk Iraq Computer Systems Dept. Northern Technical University Kirkuk Iraq Petroleum Systems and Control Dept. College of Petroleum Processes Engineering Tikrit Iraq

computer networks provide exceptional privacy, primarily for testing and other educational activities. However, the security of computer networks is an exceptionally touchy subject, mainly when applied in labs and tests for educational purposes. Any software system created on a server typically requires a username and password to access a collection of computers on a particular network. The proposed work devised and implemented a neural network feedback network with four inputs (I.P. address, MAC address, user number, and time) and an output that represents the user's acceptance or rejection. A few instances of the data and numbers needed to train the neural network include the lab's address, the student's name, the academic stage's address, etc. Good results were obtained through the company's training and education, which was tested on more than 200 models, and the response aligned with the data. No user can access the network without first being recognized by the proposed network by comparing the pre-trained data to ensure they are authorized to take the exam or enter for any other reason.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, Science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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