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E-Health and Bioengineering Conference (EHB)

作者： Ligia-Gabriela Boazu Marian-Alexandru Petraru Otilia Zvorișteanu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iași Iași Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Retinal blood vessel segmentation is an essential component in the diagnosis and monitoring of various ophthalmological conditions, such as diabetic retinopathy, glaucoma or macular degeneration, providing valuable information about the state of blood vessels and ocular blood circulation. Traditional methods for segmenting blood vessels from retinal images present certain limitations in terms of accuracy and robustness, especially under variable lighting conditions, the presence of artifacts or low-quality images. However, current techniques based on convolutional neural networks have demonstrated the ability to achieve precise and robust segmentations in a variety of conditions, outperforming traditional methods. Nevertheless, neural networks face challenges regarding the limited availability of retinal image datasets necessary for training and evaluating these solutions. In this paper we describe a solution for retinal blood vessel segmentation using the U-Net architecture. This neural network achieves impressive results even with very small datasets, demonstrating the efficiency and robustness of the U-Net architecture for retinal blood vessel segmentation.

关键词： Training Macular degeneration Image segmentation Neural networks Blood vessels Network architecture Retina Robustness Monitoring Biomedical imaging

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Enhancing Learning and Therapy for Children with Autism: Innovative Activities with the NAO Robot

Enhancing Learning and Therapy for Children with Autism: Inn...

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E-Health and Bioengineering Conference (EHB)

作者： Iustina-Bianca Bulai Otilia Zvorişteanu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Worldwide, autism affects 1 in 100 children, with diagnoses steadily rising since the 2000s. Romania lacks official statistics on autism prevalence, but global estimates suggest similarity. Children with autism struggle with social interactions and verbal communication, prompting interest in social robots for therapy and education. Tailored robot-assisted sessions offer potential for cognitive development, vocabulary enhancement, social interaction, and behavioral stimulation like concentration and attention. The NAO robot's user-friendly design, technical capabilities, and integrated features make it well-suited for these purposes. This paper presents activities that exemplify the concept of “learning through play,” allowing children to engage in exploration, experimentation, and effective learning in an enjoyable manner.

关键词： Autism Vocabulary Hospitals Social robots Education Biomedical engineering

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Real-time testing of vision-based systems for AGVs with ArUco markers

Real-time testing of vision-based systems for AGVs with ArUc...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Filus, Katarzyna Sobczak, Lukasz Domanska, Joanna Domanski, Adam Cupek, Rafal Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

ISBN: (纸本)9781665480451

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training. © 2022 IEEE.

关键词： Object recognition

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Real-Time Communication Model for IoT Systems

Real-Time Communication Model for IoT Systems

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Federated Conference on computer science and Information Systems (FedCSIS)

作者： Stanisław Deniziak Mirosław Płaza Łukasz Arcab Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland

Internet of Things solutions typically involve interaction between sensors, actuators, the cloud, embedded systems and user applications. Often in such cases, there are time constraints specifying the maximum response time to a request. This time depends on the calculation time and transmission time. Existing Internet communication solutions do not ensure the implementation of transmissions in a way that guarantees meeting the set time constraints. This paper proposes a new model of Internet communication dedicated to real-time Internet of Things systems, which includes a communication protocol, as well as a transmission scheduling and routing method. The protocol takes into account information about transmission time constraints, which is used for packet scheduling by routers, allowing to increase quality of service. In addition, the proposed static routing mechanism makes it possible to parallelize transmissions if time constraints are still exceeded. Also presented are preliminary results of experiments showing to what extent the proposed methods allow improving the quality of service in real-time Internet of Things systems.

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Risk Management in Designing and Developing Innovative, Sustainable Buildings 15th

Risk Management in Designing and Developing Innovative, Sust...

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15th KES International Conference on Sustainability and Energy in Buildings, SEB 2023

作者： Krechowicz, Maria Krechowicz, Adam Faculty of Management and Computer Modelling Kielce University of Technology Al. 1000-Lecia Państwa Polskiego 7 Kielce25-314 Poland Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Al. 1000-Lecia Państwa Polskiego 7 Kielce25-314 Poland

ISBN: (纸本)9789819985005

Nowadays, we can observe a growing interest in sustainable buildings. Sustainable development cannot be achieved without introducing innovations in green building;therefore, innovations are important component of sustainable engineers’ job. Innovative, sustainable buildings have numerous advantages, but it should be remembered that their design, and construction is more complex and associated with higher risk and uncertainty than traditional buildings. The goal of this work is to propose a new model of risk management which is based on fault tree analysis and Fuzzy sets theory. Sixteen unwanted events in innovative, sustainable building projects were identified, a fuzzy fault tree was developed. The application of the proposed approach was shown on one example of innovative, sustainable building in Poland. Two variants of project execution were compared: with and without introducing risk management strategy. Application of fuzzy sets by using linguistic variables by experts when assessing risks of particular unwanted events enabled to decrease uncertainty and avoid problems with obtaining the crisp values of the basic events probability. Application of the proposed model allowed us to identify the critical risks of the project, calculate top event risk, and compare the project’s risk level after introducing risk management strategies. The proposed model is helpful for designers, constructors, owners, and clients to develop successful innovative, sustainable buildings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault tree analysis

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of sciences: Technical sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Image segmentation

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Two approaches to car sequencing in the paint shop

Two approaches to car sequencing in the paint shop

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2020 International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2020

作者： Bysko, Sara Krystek, Jolanta Bysko, Szymon Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

The problem of car sequencing has been considered in the literature many times, but very often with the assumption of many simplifications, which meant that the discussed issue was far from the problems occurring in reality. The article presents an attempt to capture the real problem of sequencing, in particular in paint shop, because from the point of view of economic indicators, painting process is today a complex, multistage, extremely energy consuming and cost intensive operation. The main goal is therefore to minimize the number of costly changeovers of painting guns, resulting from color changes and synchronize those with periodic cleanings, forced by technological requirements. For this purpose, a buffer located in the paint shop is applied. In the paper a game-theoretic framework is presented to analyze the considered problem. Two games: Buffer Slot Assignment Game and Buffer-OutShuttle are compared with algorithms based on priority rules. Based on the performed simulations the effectivity of presented algorithms is verified using several datasets. © Published under licence by IOP Publishing Ltd.

关键词： Paint

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A DRL-Based Cluster Scheduling Algorithm Configuration Optimization Approach for Artificial Intelligence Jobs

A DRL-Based Cluster Scheduling Algorithm Configuration Optim...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Shilin Wen Wei Liang Siyan Zhu Ke Qiu Xiaoning Wu Department Three of Command and Control North Automatic Control Technology Institute Taiyuan China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the widespread application of machine learning technology across multiple domains, artificial intelligence (AI) jobs have become significant workloads for cloud service providers. However, current cloud platforms rely on pre-set scheduling algorithms, making it difficult to adjust configurations in real-time according to characteristics of AI jobs, thereby limiting job completion efficiency. Additionally, the heterogeneity and dynamism of AI jobs increase the difficulty of resource prediction and scheduling. In order to address this issue, this paper proposes an effective approach based on Deep Reinforcement Learning (DRL), by using DRL technology to adjust scheduling algorithm configurations online, converting the optimization process into a Markov process for model training, so as to optimize configurations in real-time and reduce job completion time. Simultaneously, to shorten the data sampling time for DRL training, this paper develops a cluster simulation scheduler that can efficiently simulate cluster running states, which can accelerate DRL training. Experimental results demonstrate that the simulation scheduler exhibits fast simulation rates and high simulation accuracy, and our proposed approach effectively reduces job completion time in small-scale clusters, thereby enhancing cluster resource utilization.

关键词： Training Electrical engineering Limiting Scheduling algorithms Clustering algorithms Markov processes Real-time systems Resource management Artificial intelligence Optimization

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