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METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS IN SANITARY INSTALLATIONS 21

METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS ...

21st International Multidisciplinary Scientific Geoconference: Water Resources, Forest, Marine and Ocean Ecosystems, SGEM 202

作者： Czajkowski, Adrian Remiorz, Leszek Pawlak, Sebastian Remiorz, Eryk Szygula, Jakub Miscea.pl Engineering Sp. z o.o. Poland Department of Power Engineering and Turbomachinery Faculty of Energy and Environmental Engineering Silesian University of Technology Poland Doctoral School The Silesian University of Technology Poland Faculty of Mechanical Engineering Scientific and Didactic Laboratory of Nanotechnology and Materials Technologies Poland Department of Mining Mechanization and Robotisation Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland Department of Distributed Systems and Informatic Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9786197603361

The article presents the issue of scale deposits in sanitary installations. Scale contributes to improper operation of sanitary devices and leads to their degradation. Scale build-up in pipes and measuring elements results in higher water consumption due to inaccurate estimation of this resource consumption. The consequence is that devices based on process automation do not work properly due to disturbances in the actual water flow. The formation of deposits also leads to economic and ecological losses, as sanitary installations elements have to be replaced, which affects the increased use of raw materials and the generation of waste. This is not in line with current trends and global objectives related to circular economy. Scale also results in increased energy consumption and reduced energy efficiency. This is due to the resistance of the flows and the increased work of the pumps. Scale has insulating properties, which results in less efficient heat transfer. In this stady, a mathematical model was developed to detect the formation of scale deposits in sanitary installations. On this basis, it is possible to control the quality status of sanitary installation components. In this way, by taking appropriate action at an early stage of the formation of deposits, it can extend the life of the installation. Having information on scale deposits, it will help avoid wasting water and lead to more accurate measurement of water flows. This is important for the proper operation of devices such as heat pumps, where their operation is automated and requires precise measurements of the energy processes taking place. Scale is a significant problem for households, and also for factories or public institutions that are exposed to hard water environments. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： Deposits

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Design of an Incremental Conductance Maximum power point tracking controller based on Automation PLC for PV Applications 3

Design of an Incremental Conductance Maximum power point tra...

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3rd International Conference on Electrical, Communication and Computer engineering, ICECCE 2021

作者： Yatlml, Hanane Ouberri, Youness Marah, Rim Aroudam, Elhassan Chahid, Sara Essaadi University Modeling and Simulation of Mechanical Systems Team Faculty of Sciences Physics Department Tetouan Morocco Essaadi University Computer Science and Systems Engineering Laboratory Faculty of Sciences Abdelmalek Tetouan Morocco Chapman University Advanced Physics Laboratory Institute for Quantum Studies BurtonsvilleMD United States

ISBN: (纸本)9781665438971

One of the most important concerns about the control of the PV system is the maximum power point tracking algorithms, their implementation in various electronic devices for multiple technologies, and then the improvement of the quality and the accuracy of the outcomes using different techniques. This paper presents a programmable logic controller software design for a standalone photovoltaic system, and a comparison analysis between the incremental conductance and the perturb and observe MPPT algorithms based on an automation approach. The simulation using an automation software (Siemens TIA portal) and the experimental implementation using an automation PLC (S7-1200 PLC) reveals that the later presents a good MPP tracker for both algorithms;the only difference was that the IC algorithm shows better performance and lower oscillation around the maximum power point. The implementation of the incremental conductance on the PLC program has many advantages compared to the perturb and observe algorithm: mended tracking of the maximum power point despite the various and the fast-changing on weather conditions, fewer oscillations around the maximum power point, fast response to the change on temperature and irradiation levels, better stability for battery charging signals. The entire PV system is simulating in Siemens total integration automation interface (TIA portal) and the results are plotting using Siemens Human Machine Interface (WinCC). © 2021 IEEE.

关键词： Maximum power point trackers

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Real-time Hybrid Gait Phase Recognition Algorithm Under Continuous Multimodal Locomotion for Lower Limb Exoskeleton

Real-time Hybrid Gait Phase Recognition Algorithm Under Cont...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Miao, Yinan Wang, Xingjian Wang, Jiaqi Wang, Shaoping Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Nowadays, exoskeletons' ability to operate in complex environments is increasingly important. It is challenging to obtain an accurate gait phase under continuous multimodal locomotion. A hybrid gait phase recognition algorithm is proposed combining the adaptive-oscillator-based algorithm and the model-based algorithm. The modification law is adopted to make two algorithms amend each other reciprocally. The design of the modification law combines the advantages of oscillator-based algorithms and model-based algorithms. The gait phase estimation results are more accurate via the hybrid gait phase prediction algorithm (GPRA). Therefore, appropriate assistance actuation and higher efficiency are obtained under the complicated multimodal locomotion. An experiment was designed to verify the performance of the hybrid GPRA. As a result, the hybrid GPRA can still produce a high-precision gait phase with low latency even when the locomotion mode switches. The hybrid GPRA can provide accurate gait phase and assistance timing under continuous multimodal locomotion. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Exoskeleton (Robotics)

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High Real-time Locomotion Recognition Algorithm Using Inertial Measurement Units

High Real-time Locomotion Recognition Algorithm Using Inerti...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Wang, Jiaqi Wang, Xingjian Miao, Yinan Wang, Shaoping An, Mailing Miao, Yiqi Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Human-machine interaction devices (HMI), such as assisted exoskeletons and assisted prostheses, that improve human motion behaviours are playing an increasingly important role in various fields. In order for these HMI devices to provide accurate and appropriate assistance to wearers, the real-time recognition of locomotion with accuracy is among the key issues to be solved. This paper proposes a high real-time motion pattern recognition algorithm using inertial measurement units (IMU) that can accurately and quickly identify the current motion state by collecting various motion parameters of the lower limb during human walking. The algorithm uses a trajectory solving and motion direction discrimination method based on the shank zero velocity point and a terrain classification method based on an F-score improved support vector machine (F-score-SVM). In this paper, experimental data was used to verify the performance of the algorithm. Results show that the algorithm performs better than traditional recognition algorithms in complex environments. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Support vector machines

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Semiconductor Electrochemistry for Clean Energy Conversion and Storage

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Electrochemical Energy Reviews 2021年第4期4卷 757-792页

作者： Bin Zhu Liangdong Fan Naveed Mushtaq Rizwan Raza Muhammad Sajid Yan Wu Wenfeng Lin Jung‑Sik Kim Peter D.Lund Sining Yun Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and EnvironmentSoutheast UniversityNanjing 210096JiangsuChina Shenzhen Key Laboratory of New Lithium‑Ion Batteries and Mesoporous Materials College of Chemistry and Environmental EngineeringShenzhen UniversityShenzhen 518060GuangdongChina Clean Energy Research Lab(CERL) Department of PhysicsCOMSATS University IslamabadLahore CampusLahore 54000Pakistan Engineering Research Center of Nano‑Geo Materials of Ministry of Education Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan 430074HubeiChina Department of Chemical Engineering School of AeronauticalAutomotiveChemical and Materials EngineeringLoughborough UniversityLoughborough LE113TUUK Department of Aeronautical&Automotive Engineering School of AeronauticalAutomotiveChemical and Materials EngineeringLoughborough UniversityLoughborough LE113TUUK Department of Engineering Physics/Advanced Energy Systems School of ScienceAalto University00076 AaltoEspooFinland Functional Materials Laboratory(FML) School of Materials Science and EngineeringXi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina

Semiconductors and the associated methodologies applied to electrochemistry have recently grown as an emerging field in energy materials and *** example,semiconductor membranes and heterostructure fuel cells are new technological trend,which differ from the traditional fuel cell electrochemistry principle employing three basic functional components:anode,electrolyte,and *** electrolyte is key to the device performance by providing an ionic charge flow pathway between the anode and cathode while preventing electron *** contrast,semiconductors and derived heterostructures with electron(hole)conducting materials have demonstrated to be much better ionic conductors than the conventional ionic *** energy band structure and alignment,band bending and built-in electric field are all important elements in this context to realize the necessary fuel cell *** review further extends to semiconductor-based electrochemical energy conversion and storage,describing their fundamentals and working principles,with the intention of advancing the understanding of the roles of semiconductors and energy bands in electrochemical devices for energy conversion and storage,as well as applications to meet emerging demands widely involved in energy applications,such as photocatalysis/water splitting devices,batteries and solar *** review provides new ideas and new solutions to problems beyond the conventional electrochemistry and presents new interdisciplinary approaches to develop clean energy conversion and storage technologies.

关键词： Semiconductor electrochemistry Fuel cells Lithium-ion batteries Solar cells Built-in electric field Energy system integration

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on Computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments

Numerical approach to design of low frequency magnetic field...

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ELEKTRO

作者： Roman Radil Marek Bajtoš Lucia Č arnecká Klaudia Hargaš ová Patrik Kamencay Department of Electromagnetic and Biomedical Engineering University of Zilina Zilina Slovakia Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dept. of Electromagnetic and Biomedical Engineering Organization University of Zilina Zilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments.

关键词： Radiation effects Numerical models Magnetic fields Electromagnetics

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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Fully Automated Bi-Hormonal Intraperitoneal Artificial Pancreas Using a Two-Layer PID control Scheme

Fully Automated Bi-Hormonal Intraperitoneal Artificial Pancr...

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European control Conference (ECC)

作者： Jana Langholz Karim Davari Benam Bindu Sharan Sebastien Gros Anders Lyngvi Fougner Institute of Control Systems Technical University of Hamburg Eißendorfer Straße 40 Hamburg Germany Department of Engineering Cybernetics Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) O. S. Bragstads Plass 2D Trondheim Norway

Treatment of type 1 diabetes mellitus is significantly improved by using commercially available hybrid closed-loop systems to deliver insulin. These systems, also called artificial pancreas (AP), use the subcutaneous (SC) route to deliver insulin. However, meal announcements are necessary due to the slow insulin absorption from the SC tissue. Thus due to the need for human intervention, it is called “hybrid closed loop” AP. In this work, a bi-hormonal AP with intraperitoneal (IP) infusion is designed to increase the time within the range of 3.9-10.0 mmol/l and alleviate the burden of meal announcements. A two-layer controller is designed to provide safe and effective insulin and glucagon delivery. The primary layer is based on classical PID controllers for insulin and glucagon, and the supervisory layer includes four parts: (A) Zone-based control settings, (B) Extrapolation of sensor data to compensate for sensor delay in SC tissue, (C) Auto-tuning of the PID parameters in the primary layer through simulation in an animal model, and (D) Safety barriers. The controller is designed to prevent hypoglycemia after meals and during physical activity, as well as prevent postprandial hyperglycemia. The designed AP achieved 92.5% of the time within the range of 3.9-10.0 mmol/l on a simulator trained on data from animal experiments. The results indicate that this two-layer control structure with IP infusions makes it feasible to achieve a fully automated artificial pancreas without the need for meal announcements, i.e. without human intervention.

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