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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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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Cyber Physical Systems Architecture for Collaborative Services 23

Cyber Physical Systems Architecture for Collaborative Servic...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Pop, Eugen Gifu, Daniela Stanescu, Aurelian Mihai Moisescu, Mihnea Alexandru Sacala, Ioan Stefan RandD Institute for Automation IPA SA ICT Department Bucharest Romania Alexandru Ioan Cuza University of Iasi Institute of Computer Science Romanian Academy Faculty of Computer Science Iasi Branch Romania Politehnica University of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9781665439398

Cyber Physical Systems (CPS) represents an autonomous system which integrates sensing devices, actuators, hardware equipments and software applications, having also communication functionality. A CPS can realize data transmission and cooperation with remote systems and IoT networks. Within smart factories, having the ability of involving business scenarios, CPS autonomously exchange data, control and monitor processes, perform actions in the Industry 4.0 context. Correlated with CPS interoperability capabilities, a new paradigm in manufacturing appeared, based on collaborative, service-oriented, customer-centric industries. Various new business models involve a large, exponentially growing number, of interconnected devices. In this paper is presented a CPS services framework, which integrates an e-business service group within a collaborative infrastructure, thus offering to the users the possibility to valorify on the market, through services integration and collaboration, the remarkable technical advantages of CPS. As e-business example, an e-payment service, developed in a Service Oriented Architecture (SOA), is considered. The data transactions are realized on mobile and wireless communication channels, through Soap and Rest Web services, which are accessed by mobile client software application, installed on Android OS Smartphones. © 2021 IEEE.

关键词： Cyber Physical System

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On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

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Towards experimental insight into magnetic flux distribution within the air gap of permanent magnet synchronous drive

Towards experimental insight into magnetic flux distribution...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Hubert Milanowski Adam Krzysztof Piłat Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.

关键词： Solid modeling Magnetic flux density Automation Atmospheric modeling Air gaps Rotors Drives

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Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer

Awakened at CheckThat! 2022: Fake News Detection using BiLST...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian Department of Information Technology Uppsala University Lägerhyddsvägen 1 Uppsala75105 Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei 313 Bucharest060042 Romania Fraunhofer Institute for Open Communication Systems Berlin10589 Germany

In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task);and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19. © 2022 Copyright for this paper by its authors.

关键词： Statistical tests

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ML-based Expert Products Scoring System

ML-based Expert Products Scoring System

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Progress in Applied Electrical Engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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Consideration on RT software implementation for algorithm switching in MM structures

Consideration on RT software implementation for algorithm sw...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Ciprian Lupu Catinca Secuianu Luu Duch Lich Faculty of Automatic Control and Computers University Politehnica Bucharest Bucharest Romania Faculty of Chemical Engineering and Biotechnologies University Politehnica Bucharest Bucharest Romania Faculty of Transportation Mechanical Engineering University of Science and Technology The University of Danang Da Nang Vietnam

Real-time (RT) implementation of control systems for non-linear processes raises numerous theoretical and practical challenges. Multi-model structures (MM) represent one of the successful solutions for these processes. However, switching of control algorithms constitutes the specific problem of MM structures. Even if reducing the sampling period can increase the precision in the control of some processes, for the case of algorithms switching in the MM structures is not improving significantly. The tests carried out in this work confirm this behavior for some numeric PIDs algorithms. Moreover, these tests can be an example for reducing the computational effort by changing the sampling period up to a maximum limit, specific to the ***, superior performances in switching control algorithms can be obtained by using a common database for all switched algorithms in the respective MM structure. The architecture proposed in the paper and the tests performed on a laboratory implementation prove these solutions.

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Eddy Current Method in Non-Magnetic Aluminide Coating Thickness Assessment

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Journal of Nondestructive Evaluation 2025年第3期44卷 1-11页

作者： Tytko, Grzegorz Adamczyk-Habrajska, Malgorzata Li, Yong Pengpeng, Shi Kopec, Mateusz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland Faculty of Science and Technology University of Silesia Chorzów41-500 Poland State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Engineering Research Centre of NDT and Structural Integrity Evaluation Xi’an Jiaotong University Shaanxi Xi’an China School of Mathematics and Statistics Ningxia University Ningxia Yinchuan750021 China Institute of Fundamental Technological Research Polish Academy of Sciences Pawińskiego 5B Warsaw02-106 Poland

This study investigates the use of eddy current testing (ECT) as a non-destructive technique to evaluate the thickness and structural variations of non-magnetic aluminide coatings on MAR-M247 nickel-based superalloy. Coatings with thicknesses of 20 μm and 40 μm were applied to substrates exhibiting fine, coarse, and columnar grain structures. Using sensors of different geometries, impedance measurements were performed within a frequency range of 11.5 MHz to 12.5 MHz. Results demonstrated the designed sensor’s superior sensitivity, with the highest values of absolute resistance difference significantly exceeding the threshold for reliable distinction due to coating thicknesses or grain structures. The study highlights the impact of eddy current penetration depth and edge effects on the measurement accuracy, emphasizing the need for optimized sensor design and frequency selection. Findings confirm the efficacy of ECT in differentiating coatings of varying thicknesses and substrate structures, offering a reliable tool for quality control in high-temperature applications. © The Author(s) 2025.

关键词： Eddy current testing

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A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition

arXiv

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arXiv 2024年

作者： Ion, David-Gabriel Smădu, Răzvan-Alexandru Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Bucharest Romania Academy of Romanian Scientists Bucharest Romania Paris 1 Panthéon-Sorbonne University Paris France University of Bucharest Bucharest Romania

Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is even more prevalent in speech emotion recognition. Our work explores the model’s performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. This result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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