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Adaptive Dead Time Compensation for Continuous Cross-Period Single Phase-Shift Control of Dual Active Bridge Converters

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Renewable Energy and Power Quality Journal

Renewable Energy and Power Quality Journal 2022年 20卷 256-262页

作者： Veréb, Szabolcs Futó, András Sütő, Zoltán Balogh, Attila Varjasi, István Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary

The load transient response of a power converter is an important property, especially in a test equipment application. The dynamic behavior of a dual active bridge converter mainly depends on the switching frequency, the transformer leakage inductance, and the capacitor bank size. Using the Continuous Cross-Period Single Phase-Shift control, an excellent load transient response can be achieved without the need for expensive hardware components. However, in practical applications, the switch delay and the required inserted dead time distort the current waveform, increasing the electromagnetic noise and reducing the effectiveness of the regulation. In this paper, an adaptive dead time compensation technique is presented, which eliminates such phenomenon. The effects of dead time are analyzed, and mathematical formulas are derived, from which the adaptive compensation technique is formalized. The algorithm is implemented on a TMS320F28075 DSP and tested in a HIL environment. The study revealed that the proposed adaptive technique could mitigate the current waveform distortions. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Power converters

A Fused Machine Learning Approach for Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

User Engagement with the User Manuals of Smart Medical Wearable Technologies and Relevant Questions of User Behavior

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User Engagement with the User Manuals of Smart Medical Weara...

34th Medical informatics Europe Conference, MIE 2024

作者： Simon, Aniko Xinyu, Qiu Maksimovic, Ana Talovics, Melinda Baranyai-Santa, Eszter Berze, David Odor, Viktoria Kara, Peter A. Sigma Technology Közraktár u. 30-32 Budapest1093 Hungary Department of Electrical Engineering The Fu Foundation School of Engineering and Applied Science Columbia University 2960 Broadway New YorkNY10027-6902 United States University of Donja Gorica Oktoih 1 Podgorica81000 Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. Budapest1111 Hungary

ISBN: (纸本)9781643685335

In this paper, engagement with smart medical wearables and with their user manuals, as well as related user behavior are studied. A research questionnaire containing 15 single-choice questions was completed by 1381 test participants to address relevant topics of the investigated area, including trust in measured medical data, device calibration, technical terminologies and function discovery in the documentation, information sources beyond the documentation, and wearing such devices to bed. The questionnaire particularly focused on device functionalities and characteristics that initially led to the purchase of the smart medical wearable. © 2024 The Authors.

关键词： Electronic health record

Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks

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Periodica polytechnica Electrical engineering and computer s...

Periodica polytechnica Electrical engineering and computer science 2022年第2期66卷 122-131页

作者： Mrad, Mohamed Azouz Csorba, Kristóf Galata, Dorián László Nagy, Zsombor Kristóf Nagy, Brigitta Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar Tudósok krt. 2 BudapestH-1117 Hungary Department of Organic Chemistry Technology Budapest University of Technology and Economics Faculty of Chemical Technology and Biotechnology Budapest University of Technology and Economics Muegyetem rkp. 3 BudapestH-1111 Hungary

In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy. © 2022 budapest university of technology and economics. All rights reserved.

关键词： Dissolution

Data Augmentation for Machine Translation via Dependency Subtree Swapping

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arXiv 2023年

作者： Nagy, Attila Lakatos, Dorina Petra Barta, Botond Nanys, Patrick Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Eötvös Loránd Research Network Hungary

We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graph-based similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub1 © 2023, CC BY.

关键词： Machine translation

HunSum-1: an Abstractive Summarization Dataset for Hungarian

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arXiv 2023年

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Eötvös Loránd Research Network Hungary

We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1.14M news articles. The dataset is built by collecting, cleaning and deduplicating data from 9 major Hungarian news sites through CommonCrawl. Using this dataset, we build abstractive summarizer models based on huBERT and mT5. We demonstrate the value of the created dataset by performing a quantitative and qualitative analysis on the models' results. The HunSum-1 dataset, all models used in our experiments and our code are available open source. © 2023, CC BY.

关键词： Open systems

A MATLAB virtual laboratory to support learning of auto-tuning PID approaches

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IFAC-PapersOnLine 2024年第7期58卷 73-78页

作者： J.A. Rossiter A. Visioli S. Dormido R. Bars Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Dipartimento di Ingegneria Meccanica e Industiale University of Brescia Italy Universidad Nacional de Educacion a Distancia Madrid Spain Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

This paper presents a small number of MATLAB APPs and livescript files designed to help students both understand and implement PID tuning. The paper presents the thinking behind the use of MATLAB and the topic itself before then describing the proposed resources in detail. The resources split into files with detailed mathematical and coding background students can use for self-study and assignments, and a virtual laboratory which is more intuitive and interactive and useful for familiarisation with core concepts. The files were recently added to the control101 toolbox (Rossiter, 2023).

关键词： Control101 toolbox PID compensation virtual laboratories independent learning

Importance of environment design in reinforcement learning: A study of a robotic environment

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arXiv 2021年

作者： Farsang, Mónika Szegletes, Luca Department of Automation and Applied Informatics Budapest University of Technology and Economics

An in-depth understanding of the particular environment is crucial in reinforcement learning (RL). To address this challenge, the decision-making process of a mobile collaborative robotic assistant modeled by the Markov decision process (MDP) framework is studied in this paper. The optimal state-action combinations of the MDP are calculated with the non-linear Bellman optimality equations. This system of equations can be solved with relative ease by the computational power of Wolfram Mathematica, where the obtained optimal action-values point to the optimal policy. Unlike other RL algorithms, this methodology does not approximate the optimal behavior, it gives the exact, explicit solution, which provides a strong foundation for our study. With this, we offer new insights into understanding the action selection mechanisms in RL by presenting various small modifications on the very same schema that lead to different optimal policies. © 2021, CC BY.

关键词： Dynamic programming

Decaying clipping range in proximal policy optimization

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arXiv 2021年

作者： Farsang, Mónika Szegletes, Luca Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through the clipping mechanism and the multiple epochs of minibatch updates. The aim of this research is to give new simple but effective alternatives to the former. For this, we propose linearly and exponentially decaying clipping range approaches throughout the training. With these, we would like to provide higher exploration at the beginning and stronger restrictions at the end of the learning phase. We investigate their performance in several classical control and locomotive robotic environments. During the analysis, we found that they influence the achieved rewards and are effective alternatives to the constant clipping method in many reinforcement learning tasks. © 2021, CC BY.

关键词： Reinforcement learning