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检索条件"机构=Faculty of Control and Automation Engineering"

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Robust controller Design with Two-Degree-of-Freedom Feedback Loop Factorization for Oscillating Plant with Uncertain Time Delay

Robust Controller Design with Two-Degree-of-Freedom Feedback...

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2023 International Conference on control, automation and Diagnosis, ICCAD 2023

作者： Dlapa, Marek Tomas Bata University in Zlin Faculty of Applied Informatics Department of Automation and Control Engineering Zlin760 05 Czech Republic

ISBN: (纸本)9798350347074

Application of Robust control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm of global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proved to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach. © 2023 IEEE.

关键词： Global optimization

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration 12

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

引用

12th International Electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Software Interference Prevention and SWaP-C Minimization in Cyber-Physical Systems Using Hypervisor Technology 5

Software Interference Prevention and SWaP-C Minimization in ...

引用

5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Sridadi, Bambang Joelianto, Endra Nazaruddin, Yul Yunazwin Institut Teknologi Bandung Doctoral Program in Engineering Physics Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350364743

Currently, hypervisor technology is an emerging topic of research. Virtualization technology, especially hypervisors and virtual machines (VM), has been widely studied but is still little applied in the cyber-physical systems (CPS) field. A hypervisor, or VM monitor (VMM), is a software program used to run multiple VMs on a single physical machine through space and time partitioning in a real-time operating system. This paper discusses the hypervisor technology to prevent software interference and to minimize size, weight, and power-cost (SWaP-C) in cyber physical systems, as well as, the application of hypervisor technology in the aerospace, automotive and control fields. By partitioning, a hypervisor can increase software safety because application programs with different criticality levels do not affect each other in failure when run in partitioned VMs. © 2024 IEEE.

关键词： Application programs

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A Microservices-Driven Digital Twin Model for Precision Farming in a controlled Indoor Environment 5

A Microservices-Driven Digital Twin Model for Precision Farm...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Lubis, Ramot Widyotriatmo, Augie Albert Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350364743

This paper presents a microservices-driven digital twin (DT) model for precision farming in controlled indoor environments, addressing the critical need for scalable, modular, and real-time agricultural systems. As the global demand for food rises, integrating Industry 4.0, IoT, edge, and cloud computing technologies becomes essential to improve farming process. The proposed model enhances scalability, modularity, and real-time decision making through decentralized data processing. A master-agent containerized computing architecture was employed to reduce latency and optimize environmental control. Moreover, the integration of a Kalman filter improved the accuracy of key environmental parameter adjustments. This research explored how this model can be scaled for larger farming operations and addresses challenges in system interoperability and implementation. Future work will expand this research to diverse agricultural settings, real-world validation, and ensuring data privacy and security. This study provides a framework for advancing precision farming through digital twin technology. © 2024 IEEE.

关键词： Kalman filters

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Molecular Imprinted Polymer Based Optical Sensor for Methanol Vapor Detection

Molecular Imprinted Polymer Based Optical Sensor for Methano...

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Optica Sensing Congress 2023, AIS, FTS, HISE, Sensors, ES 2023 - Part of Optical Sensors and Sensing Congress 2023

作者： Leelasirinuwong, Mintra Denkasineecharoen, Phebe Yungyuentrrakoon, Pin Jitpratak, Pannathorn Kongsawang, Nuntaporn Pathak, Akhilesh Kumar Viphavakit, Charusluk Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Biomedical Engineering Program Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Intelligent Control Automation of Process Systems Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

A molecular imprinted polymer-based optical sensor is proposed to detect methanol. The sensor is tested with different concentrations of methanal vapour for sensitivity and ethanol for selectivity. The result shows a ... 详细信息

ISBN: (纸本)9781957171241

关键词： Optical sensors

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on control and automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Influences of Inhomogeneous Spatial Distribution of Global Oceanic Atmospheric Refractivity on LF Groundwave Propagation Delay

Influences of Inhomogeneous Spatial Distribution of Global O...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Wang, Dan-Dan Lu, Qi-Zirui Han, Chao Pu, Yu-Rong Yang, Rui-Hong Bai, Pei-Rui Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao China Xi'an University of Technology Faculty of Automation and Information Engineering Xi'an China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350355581

Atmospheric refractivity affects the propagation delay of low-frequency (LF) groundwave in the realistic ocean environment. In this paper, a parabolic equation (PE) model based on the ERA5 meteorological reanalysis dataset is applied to analyze the influences of inhomogeneous spatial distribution of global oceanic atmospheric refractivity on LF groundwave propagation delay. The global oceanic atmospheric refractivity is derived from the meteorological quantities given by the ERA5 reanalysis dataset. It is found that the global oceanic surface atmospheric refractivity varies systematically with latitude in the range of 290∼300 N-unit to 380∼400 N-unit, which in turn leads to the variation of LF groundwave propagation delay reaching hundredth nanosecond for 1000 km-long propagation path. © 2024 IEEE.

关键词： global oceanic atmospheric refractivity low-frequency (LF) groundwave propagation parabolic equation (PE)

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