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21st Polish control Conference, PCC 2023

作者： Kozyra, Andrzej Lipczynska, Aleksandra Koper, Piotr Babisz, Radoslaw Madej, Damian Nowakowski, Konrad Karwatka, Jakub Tomczok, Dominik Department of Measurements and Control Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Heating Ventilation and Dust Removal Technology Faculty of Energy and Environmental Engineering Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031351693

"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the concept of the device, its construction and performed tests. The project was implemented by students of two faculties: Automatic control, Electronics and computer Science, and Energy and Environmental Engineering. Project supervisors from both faculties provided substantive supervision and support for students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Air quality

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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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2023 IEEE International Conference on Big Data, BigData 2023

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

ISBN: (纸本)9798350324457

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. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Iterative learning control in health care: Electrical stimulation and robotic-assisted upper-limb stroke rehabilitation

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IEEE control systems 2012年第1期32卷 18-43页

作者： Freeman, Chris T. Rogers, Eric Hughes, Ann-Marie Burridge, Jane H. Meadmore, Katie L. School of Electronics and Computer Science University of Southampton United Kingdom School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom Department of Control Systems Theory and Design School of Electronics and Computer Science University of Southampton United Kingdom Faculty of Health Sciences University of Southampton United Kingdom

Research into conventional therapy and motor learning theory provides evidence that the intensity of practice of a task and feedback are important for iterative control of health care. Iterative learning control (ILC) is one model-based approach to stroke rehabilitation that has progressed to a program of clinical trials, which constitute the first major stage towards the eventual transfer into practice. ILC exploits the repeating nature of the patients' tasks to improve performance by learning from past experience as compared with other approaches employed to control FES. ILC is able to respond to physiological changes in the system, such as spasticity and the presence of a patient's voluntary effort by updating the control input using data collected over previous attempts at the task. ILC can also closely regulate the amount of stimulation supplied, ensuring that minimum assistance is provided, promoting the patient's maximum voluntary contribution to the task completion.

关键词： Health care

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Decision support system based on pattern detection in iterative dermoscopic screening

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computer Methods and Programs in Biomedicine 2025年

作者： Natalia Salwowska Aleksander Kempski Piotr Zielonka Paweł Pietkiewicz Damian Borys Department of Dermatology School of Medicine Medical University of Silesia Katowice Poland Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dermatology Private Practice Polish Dermatoscopy Group Poznań Poland Biotechnology Centre Silesian University of Technology Gliwice Poland

Background and Objective: Videodermoscopy is the gold standard in melanoma diagnosis. A new research trend aims to support specialists’ work by automating this examination method, but most research focuses on developing detectors to replace dermatologists. Our study aims to present a novel idea which focuses on supporting the examination process instead of completely automating it. Instead of detecting and classifying all naevi each time, our approach focuses on full screening at the first visit and shortening the examination at subsequent visits. During the following examinations, images of all naevi are taken by supporting staff and are compared with previously taken images using feature-matching or co-registration-based algorithms. An unmatched naevus needs to be checked by the specialist, meaning that a new naevus appeared or an old one had changed. Methods: The similarity between images (ranging from 0% to 100%) was obtained with characteristic points searching (ORB method) and co-registration-based methods. No dataset presenting changes of naevi over time exists, so augmenting the images from PH2 and HAM10000 datasets was required to simulate the time flow. Results: The decision bound was set, so the true positive rate of each method was equal to 99%, giving the specificity of ORB equal to 97% (PH2) and 89% (HAM10000). Similar tests were performed using a co-registration algorithm giving specificity equal to 23% (PH2) and 19% (HAM10000). Conclusions: We believe that employing our approach will allow supporting staff to perform screening without experts’ help, decreasing examination time and costs and increasing the accessibility of dermoscopic examinations.

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Cost Oriented VR-Simulation Environment for computer Aided control Design

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IFAC Proceedings Volumes 2001年第29期34卷 40-46页

作者： Dirk Wollherr Martin Buss Control Systems Group Faculty of Electrical Engineering and Computer Science Technical University Berlin

In this paper we propose the use of inexpensive offthe- shelf hardware and software in place of high-cost commercial solutions to easily set up graphical VR environments and simulations with a human in the control loop. For this purpose we have developed a generic simulation environment which permits to rapidly implement control simulation and evaluation experiments. As an example setup a car simulator with a human in the control loop is presented. The user receives both 3D visual and haptic feedback while driving the simulated vehicle.

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Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains: A Simulation Study 9

Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains...

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9th International Conference on Engineering and Emerging Technology, ICEET 2023

作者： Farooghi, Kian Samadi, Mahdieh Khaloozadeh, Hamid K. N. Toosi University of Technology Department of Systems and Control Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350316926

This paper presents a novel approach for the online calculation of Linear Quadratic Regulator (LQR) gains using the Tabular Dyna-Q algorithm. By leveraging Q-learning, this technique enables the determination of gains for unknown dynamic systems by using input-output data only. The proposed algorithm is particularly advantageous for situations where acquiring input-output data is expensive or time-consuming, as it effectively reduces computation time by reusing previous data. To validate the accuracy of the calculations and control scheme, a simulation example is provided. The results indicate the effectiveness of the Tabular Dyna-Q algorithm in accurately computing LQR gains and its potential application in real-world scenarios. This research contributes to advancing the field of control theory by offering an efficient and reliable method for online gain calculation in dynamic systems. © 2023 IEEE.

关键词： Reinforcement learning

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Database technology and industrial transaction techniques in control education 6

Database technology and industrial transaction techniques in...

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6th IFAC Symposium on Advances in control Education, ACE 2003

作者： Flochova, Jana Galbavy, Miroslav Department of Automatic Control Systems Department of Computer Science Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava81219 Slovakia

New developments in computer networks and communications provide new possibilities for control purposes. control systems for highly complex plants are themselves very complex and heterogeneous. A new software infrastructure is needed that exploits these new emerging software technologies. An essential activity to be pursued is that of education and of including new technology in control education. The aim of this paper is to describe some of our activities and experiences in teaching of information techniques and technologies included in open control platforms of complex control systems. Copyright © IFAC Advances in control Education Oulu, Finland, 2003

关键词： Information management

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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computer systems Science & Engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Elitist behavior in differential ant-stigmergy algorithm

Elitist behavior in differential ant-stigmergy algorithm

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International Conference on Evolutionary Computation Theory and Applications, ECTA 2011 and International Conference on Fuzzy Computation Theory and Applications, FCTA 2011

作者： Şerbencu, Adrian Emanoil Minzu, Viorel Şerbencu, Adriana Cernega, Daniela Control Systems and Industrial Informatics Department Computer Science Faculty Dunarea de JosUniversity from Galati Str. Domneasca 111 800201 Galati Romania

ISBN: (纸本)9789898425836

This paper proposes two type of elitist variants for the differential ant-stigmergy algorithm (DASA). An elitist behaviour is inserted in genetic algorithms by keeping the best solution found in the population used at next generation. Another way to insert elitist behaviour in algorithms that construct solution is to use the most attractive components in order to obtain good quality solution, and may be the optimal ones. Based on particularities of differential ant-stigmergy algorithm this two type of elitist behaviour was successfully applied to studied algorithm. In this paper the efficiency of the proposed elitist variants of DASA algorithms is analyzed using experimental results. The analysis is applied to six benchmark functions from the class of high-dimensional real-parameter optimization problems.

关键词： Ant colony optimization

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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