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Model Identification and Control of Electromagnetic Actuation in Continuous Casting Process With Improved Quality

IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 203-215页

作者： Isabela Birs Cristina Muresan Dana Copot Clara Ionescu Research Group of Dynamical Systems and Control Faculty of Engineering and ArchitectureGhent University and with EEDT Decision&ControlFlanders Make ConsortiumTech Lane Science Park 125 and 131B-9052 GhentBelgium.I.Birs the Automation Department Faculty of Automation and Applied InformaticsTechnical University of Cluj-NapocaRomania IEEE

This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.

关键词： Electromagnetic actuator fractional order control fractional order system model non-Newtonian material

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Comparison of The Accuracy of Predicting The Number Of Positive Covid-19 Between The Neural Network and LSTM Methods

Comparison of The Accuracy of Predicting The Number Of Posit...

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2023 International Conference on informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Riyanto, Eko Amalia, Rifka Dwi Stmik Himsya Informatics Engineering Semarang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350307306

The COVID-19 pandemic (Coronavirus) is likely to be one of the most serious global problems in the last year. Countries do not have similar experiences with the spread of the virus and its impact from various fields. Estimating the number of COVID-19 cases in the past can help in decision-making about action and plan for virus prevention. The study aims to provide accurate predictive model in predicting confirmed cases of COVID-19 in Semarang city. In predicting the number of people exposed to Covid-19 positive need to be accurate and accurate methods, in some previous studies the two methods most recommended are neural network and LSTM (Long Short Term Memory) which have high levels of accuracy. Based on research that has been carried out that the results of accurate prediction comparison between RNN and LSTM obtain the smallest MSE result is the method. © 2023 IEEE.

关键词： Forecasting

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

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11th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

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XgCPred: Cell type classification using XGBoost-CNN integration and exploiting gene expression imaging in single-cell RNAseq data

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Computers in Biology and Medicine 2024年 181卷 109066-109066页

作者： Abu-Doleh, Anas Al Fahoum, Amjed Hijjawi Faculty for Engineering Technology Biomedical Systems and Informatics Engineering Department Yarmouk University Irbid21163 Jordan

Background: The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the exploration of gene expression at an individual cell level. This advancement sheds light on how cells differentiate and evolve over time. Effectively classification cell types within scRNA-seq datasets are essential for understanding the intricate cell compositions within tissues and elucidating the origins of various diseases. Challenges persist in the field, emphasizing the need for precise categorization across diverse datasets, addressing aggregated cell data, and managing the complexity of high-dimensional data spaces. Methodology: XgCPred is a novel approach combining XGBoost with convolutional neural networks (CNNs) to provide cell type classification with better accuracy in single-cell RNA-seq data. This combo reveals how well CNNs can detect spatial hierarchy in gene expression images and how XGBoost performs with large volumes of data. XgCPred utilizes an imaging representation of gene expression that is based on the hierarchical organization of genes found in the KEGG BRITE database. Results: Rigorous testing of XgCPred across multiple scRNA-seq datasets, each presenting unique challenges such as varying cell counts, gene expression diversity, and cellular heterogeneity, has demonstrated its superiority compared to earlier methods. The algorithm shows remarkable accuracy and precision in cell type annotation, achieving near-perfect classification scores in some cases. These results underscore its capability to effectively manage data variability. Conclusions: XgCPred distinguishes itself through its dependable and accurate cell type classification across a range of scRNA-seq datasets. Its effectiveness stems from sophisticated data handling and its ability to adapt to the complexities inherent in scRNA-seq data. XgCPred delivers reliable cell annotations essential for further biological analysis and research, marking a significant ad

关键词： scRNA-seq classification Gene expression profiling Machine learning in genomics Automated cell type annotation And high-dimensional data analysis

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights 11

Machine Learning-Based Anomaly Detection in Smart City Traff...

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11th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Bawaneh, Mohammad Simon, Vilmos Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9789897587450

In recent years, urban roads have suffered from substantial traffic congestion due to the rapidly increasing number of road users and vehicles. Some traffic congestion patterns on specific roadways, such as the recurring congestion during morning and evening rush hours, can be foreseen. However, unexpected events, such as incidents, may also cause traffic congestion. Monitoring traffic status poses vital importance for city traffic operators. They can leverage the monitoring system for resource allocation, traffic lights adjusting, and adapting the public transport schedules to alleviate traffic congestion. Machine learning-based methods for anomaly detection are valuable tools for monitoring traffic status and promptly detecting congestion on city roads. In this paper, we comprehensively study the performance of the common machine learning methods for anomaly detection in the traffic congestion detection use case. In addition, we provide methods usage insights based on the study findings by examining the accuracy, detection speed, and computation overhead of the methods to guide the researchers and city operators toward a suitable method based on their needs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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Autonomous Exploration System of Electrical Stimulus Condition for Maturating in vitro Skeletal Muscle Tissue with Bayesian Optimization

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IEEJ Transactions on Sensors and Micromachines 2025年第3期145卷 33-40页

作者： Miyata, Daiki Kasahara, Keitaro Taguchi, Yujin Tokuoka, Yuta Yamada, Takahiro G. Kurashina, Yuta Funahashi, Akira Onoe, Hiroaki Department of Mechanical Engineering Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Department of Biosciences and Informatics Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei Tokyo184-8588 Japan

This paper describes an autonomous exploration system to optimize electrical stimulation parameters for the maturation of in vitro skeletal muscle tissues with Bayesian optimization. We established a cyclic experimental procedures, where (i) C2C12 myoblasts were cultured under multiple electrical stimulus conditions in parallel to promote the maturation, (ii) the cell maturation levels were scored by microscopic image analysis with immunofluorescence staining, and (iii) each maturation score with electrical stimulus condition were then applied to Bayesian optimization to suggest new experimental stimulus conditions for the next cycle of the C2C12 culture to expect higher maturation scores. We performed this experimental cycle repeatedly and derived the trend of electrical stimulus conditions to obtain high maturation scores. The C2C12 myoblasts cultured under the derived condition were also verified by the increase of myogenic gene expression. We believe that our proposed method could reveal optimal application method for tissue maturation, and would contribute to regenerative medicine or other medical technologies using tissue culture. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Regenerative medicine

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Enhancing School and College Attendance Using Advanced Technology 3

Enhancing School and College Attendance Using Advanced Techn...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Budiarto, Mukti Audiah, Sipah Astuti, Eka Dian Sanjaya, Yulia Putri Ayu Firli, M. Zaki University of Raharja Faculty of Informatics Engineering Tangerang Indonesia Cai Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia Asosiasi Dosen Indonesia Faculty of Digital Business Tangerang Indonesia Alfabet Inkubator Indonesia Faculty of Retail Management Tangerang Indonesia Rey Faculty of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

In today digital era traditional manual attendance systems in educational institutions are both inefficient and outdated. This study assesses the impact of implementing technology-driven attendance systems developed using the Waterfall Model, which promises to revolutionize educational settings by enhancing operational efficiency and reducing administrative burdens. The research involved deploying automated attendance software across various institutions and comparing its efficacy with traditional methods in terms of time efficiency, accuracy, and cost. The results indicate that technology-based systems significantly reduce the time and effort required for attendance recording, minimize manual errors, and offer real-time data that helps quickly identify attendance patterns and issues. These improvements lead to enhanced student engagement and accountability. Moreover the transition to automated systems results in considerable administrative cost savings by decreasing reliance on extensive paperwork, thereby allowing for a better allocation of resources towards improving the educational experience. Ultimately, the study confirms that adopting modern attendance monitoring technologies not only streamlines processes but also enhances the educational framework, making institutions more efficient and responsive. This shift benefits administrative and educational outcomes and sets a robust foundation for future educational practices, highlighting the critical role of technology in advancing educational environments. © 2024 IEEE.

关键词： Educational technology

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Predicting QoE for Delay-critical services in Mobile Networks: A video conferencing case study 30

Predicting QoE for Delay-critical services in Mobile Network...

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30th International Conference on systems, Signals and Image Processing, IWSSIP 2023

作者： Dobreff, Gergely Szalay, Mark Molnar, Marton Varga, Laszlo Ladoczki, Bence Bader, Attila Pasic, Alija Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Hungary Ericsson Hungary Hungary ELKH-BME Information Systems Research Group Hungary

ISBN: (纸本)9798350337297

There is an increasing demand to support high-quality data communications for delay-critical services such as Augmented/Virtual Reality (ARNR), cloud gaming, drone control or video conferencing. Mobile network operators need to estimate quality of experience (QoE) values based on available QoS parameters such as radio environment and core network metrics, to respond to performance degradations and maximize revenue, as the QoE of users is more important than the QoS metrics. To develop accurate QoE models, operators need a large amount of versatile data under different network conditions. We propose a standardized and automated data collection framework to gather fine-grained transport and radio data, along with user experience values assigned through a standardized survey, for model training purposes in delay-critical services. Our study identifies valuable QoS parameters and relationships between mobile network degradations and service quality impacts, demonstrating the possibility of building accurate QoE models with an R2 of 0.78 based solely on transport and radio data. Our finding can be beneficial to MNOs in designing new data collection and QoE estimation services for various delay-critical services. © 2023 IEEE.

关键词： Wireless networks

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