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2020 International Conference on Applied Sciences, ICAS 2020

作者： Aluvihara, Suresh Kalpage, C.S. Lemle, L.D. University of Peradeniya Faculty of Engineering Department of Chemical and Process Engineering Peradeniya20400 Sri Lanka Politehnica University of Timisoara Department of Electrical Engineering and Industrial Informatics 5 Revolution street Hunedoara331128 Romania

Clay plays an important role in the most of industries that related with water treatment and food stowage industries. The adequate physic-chemical properties are the most recognized factor for the wide range of applications. The investigation of the fundamental physic-chemical and some mechanical properties of such clay is an insistent task before the selection of some material for some specific role. The analysis of the fundamental physic-chemical and grain size analysis of grain sizes of three different types of selected clays were the aims and objectives of the existing research. Clay samples were collected from three different locations in Sri Lanka and they were stored well. The clay samples were named as anthill clay, brick clay and roof tile clay. Some potion from each clay sample was separated and such separated clay samples were oven dried for 24 hours under the temperature 110 C. Each of dried clay sample was separated again and the separated portions were prepared for the test of moisture content, dry sieve analysis and wet sieve analysis. As the major results of the experiment, especially there were obtained 14.49%, 21.45% and 25.97% moisture contents with respect to anthill, brick and roof tile clays, well-graded particle arrangement in roof tile clay, gap graded particle arrangement in anthill clay and uniformly graded particle arrangement in brick clays. In addition, according to the obtained results there were seen the finer particle weight percentages as 59.90%, 37.36% and 72.38% with respect to anthill clay, brick clay and roof tile clay. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Moisture

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Prediction of the Navigation Angles Using Random Forest Algorithm and Real Flight Data of UAVs

Prediction of the Navigation Angles Using Random Forest Algo...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Huda Naji Al-sudany Béla Lantos Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781665489898

Unmanned aerial vehicles (UAVs) are becoming more common in aviation. Predicting and estimating of UAV attitude are important aspects of the communication strategy. Autonomous UAVs' attitude estimate is a crucial component for controlling in flight mission. The attitude can take many forms such as Euler angles and Quaternion. This paper explains attitude estimation of UAV using Random Forest (RF) algorithm based on real flight data of Accelerations, Angular velocity, Magnetic Field, and GPS measurements for prediction current attitude angles (roll, pitch and yaw). The developed algorithm uses features that are derives from sensors measurements. The developed RF model results showed accurate prediction of UAVs attitude angles with small error values.

关键词： Radio frequency Magnetic field measurement Current measurement Sensor phenomena and characterization Autonomous aerial vehicles Prediction algorithms Angular velocity

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Delivering Improved Care through Remote Health Monitoring Systems by Using Microservices. Case Study: Fall Detection Encompassed into Connected Care 24

Delivering Improved Care through Remote Health Monitoring Sy...

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24th International Conference on control Systems and Computer Science, CSCS 2023

作者： Ianculescu, Marilena Alexandru, Adriana Giura, Ionut-Emanuel Vasilevschi, Ana-Mihaela National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Bucharest Romania University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania Counselors Department National Institute for Research and Development in Informatics Târgovişte Romania Faculty of Electrical Engineering Electronics and Information Technology Târgovişte Romania Valahia University of Târgovişte Târgovişte Romania

ISBN: (纸本)9798350313390

Based on today's modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring Systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors' health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside 'Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)' research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved. © 2023 IEEE.

关键词： Fall detection

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Application of mixed graph traversal optimization for the vehicle routing problem

Application of mixed graph traversal optimization for the ve...

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European control Conference (ECC)

作者： Lá szló Kocsá ny Emese Gincsainé Szá deczky-Kardoss Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The Vehicle routing problem (VRP) is widely discussed in the literature. The goal of the VRP is to provide an optimized traversal of a graph. This paper presents a mixed graph traversal optimization for the VRP. The mixed graph is a graph that contains both directed and undirected edges. The problem to be solved is a parking space search problem in a mixed graph where, from an initial location a vehicle first navigates to a zone, where the selected edges are located, and plans the traversal driving through all selected edges, and returning to the start of the exploration so that the path is repeatable. The paper shows, that the parking space search problem in a mixed graph can be reduced to a travelling salesman problem (TSP), for which an ant colony optimization (ACO) is implemented, to provide a solution, that is minimizing the total cost of the mixed graph traversal.

关键词： Space vehicles Costs Navigation Roads Vehicle routing Europe Directed graphs

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients

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IFAC-PapersOnLine 2023年第2期56卷 2091-2095页

作者： Bálint Szabó Ákos Szlávecz Béla Paláncz Katalin Kovács J. Geoffrey Chase Balázs István Benyó Budapest University of Technology and Economics Faculty of Electrical Engineering and Information Technology Department of Control Engineering and Information Technology Budapest Hungary Department of Informatics Széchenyi István University Győr Hungary Department of Mechanical Engineering University of Canterbury Christchurch New Zealand

Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied.

关键词： Insulin Sensitivity Tight Glycaemic control Prediction Method Artificial Intelligence Artificial Neural Network

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Specific Transfer Learning to Improve Quantile Registration Neural Network Performance for Insulin Sensitivity Temporal Forecasting

Specific Transfer Learning to Improve Quantile Registration ...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Omer S. Alkhafaf J. Geoffrey Chase Balázs Benyó Dep.of Control Eng. and Information Tech. Faculty of Electrical Eng.and Informatics Budapest University of Tech. and Economics Budapest Hungary Department of Mechanical Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

Tight glycemic control needs to be applied to keep the patient's blood glucose within normal physiological range to reduce the effect of stress-induced hyperglycemia for intensive care unit patients. The Stochastic-TARgeted (STAR) protocol with intensive care insulin-nutrition-glucose model uses insulin sensitivity as a key parameter to describe the patient's actual state. For clinical relevance, it is crucial to forecast future insulin sensitivity distribution for the patient. STAR needs to use future insulin sensitivity to achieve a specific treatment plan. Previous studies have shown that quantile regression-based neural network models are efficient alternative methods of predicting insulin sensitivity distribution. This paper suggests applying the transfer learning technique to improve the insulin sensitivity distribution prediction efficacy by adopting the finetuning to the pre-trained model using age-specific insulin sensitivity trajectories. The proposed model was evaluated using case-specific metrics. Results show applying fine-tuning on pretrained models using age-specific trajectories achieves higher prediction performance for each age cohort, thus crossing the entire dataset on average.

关键词： Sensitivity Transfer learning Refining Stars Artificial neural networks Predictive models Trajectory Glucose Insulin Blood

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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Adaptive Mpc-Based Optimization of Battery Discharge Strategy for Autonomous Pmsm Vehicles

SSRN

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SSRN 2024年

作者： Ismail, Moustafa Magdi Al-Dhaifallah, Mujahed Rezk, Hegazy Ur Rahman Habib, Habib Khalique Junejo, Abdul Hamad, Samir A. Electrical Engineering Department Faculty of Engineering Minia University EL-Minya61517 Egypt Dhahran31261 Saudi Arabia King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Department of Electrical Engineering College of Engineering in Wadi Alddawasir Prince Sattam bin Abdulaziz University Saudi Arabia Department of Electrical Engineering Faculty of Electrical and Electronics Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Electrical Engineering Department Qaid-eAwam University of Engineering Science and Technology Nawabshah67450 Pakistan Department of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni Suef Egypt Prince Sattam bin Abdulaziz University Saudi Arabia Minia University Egypt

Effective battery management is critical for the performance and lifespan of smart electric vehicle (EV) systems. Therefore, reducing battery discharge is key to improving the range of EVs. This paper proposes an adaptive model predictive control (AMPC) strategy for optimizing battery discharge of EVs. Specifically, AMPC autonomously controls the smart EV by managing the lateral position, yaw angle, and PMSM to provide the required traction torque at the desired speed. The strategy updates the operating points of the predictive model at each time step to accurately represent the nonlinear plant across different speed regions, using vehicle linear velocity and stator currents. Meanwhile, the stator inductances and induced motor torque are estimated via a third order generalized integrator. Finally, simulation results demonstrate that the AMPC significantly reduces battery discharge current, watt-hour per kilometer, capacity rate, and parallel cell units. Meanwhile, the AMPC improves lane-keeping assistance in the electric power steering system and ensures precise tracking of speed and torque. A finite element method parameterized PMSM simulation model enhances motor capture. Consequently, AMPC shows superior performance in vehicle dynamics and PMSM control compared to the classic MPC approach. © 2024, The Authors. All rights reserved.

关键词： Adaptive control systems

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