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International Symposium on applied Computational Intelligence and informatics ( SACI)

作者： Dominik Csík Peter Sarcevic Richard Pesti Ákos Odry Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

With the spread of Internet of Things (IoT) different radio communication-based technologies are gaining more attention, and their application in indoor localization problems where GPS signal is not available, becomes more and more relevant. This paper addresses the trilateration algorithm-based position estimation approach in indoor localization; this approach uses Received Signal Strength Indication (RSSI) value with the Free Space Path Loss (FSPL) model. Comprehensive laboratory measurements were performed with different radio communication-based technologies namely 433 MHz RSSI, 2.4 GHz WiFi RSSI, ultra-wideband (UWB) RSSI, and UWB Time of Flight (TOF). Then, numerical optimization using the Particle Swarm optimization (PSO) algorithm to determine the parameters of the FSPL model for each anchor and each technology. This method enabled the utilization of trilateration to calculate the position of the measurement node. The obtained results show that the 433 MHz RSSI and the UWB TOF outperform the other two technologies. Using UWB TOF technology, the achieved accuracy was 165.97 cm. 433 MHz RSSI technology provided the second-best solution with 166.60 cm. WiFi RSSI provided 227.89 cm accuracy, while the worst case was obtained by UWB RSSI with 252.08 cm. The study experimentally validates that the most appropriate measurements are provided by UWB TOF and 433 MHz RSSI, which enables the implementation of these technologies in sensor fusion algorithms.

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Multivariable Control in General Anesthesia: A Comparative Study of Model Predictive and Fractional-Order Strategies for Effect-Site Concentration and Hemodynamic Regulation

Multivariable Control in General Anesthesia: A Comparative S...

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International Conference on Computational Cybernetics (ICCC)

作者： Erwin T. Hegedüs Anca Maxim Marcian D. Mihai Isabela R. Birs Nicoleta Badau Cristina I. Muresan Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

Managing anesthesia and hemodynamic regulation during surgical procedures poses significant challenges due to patient-specific variability and the complex interactions of administered drugs. While advanced monitoring systems and Target-Controlled Infusion (TCI) devices enhance precision in drug delivery, their reliance on manual adjustments limits real-time adaptability. This study investigates two advanced control strategies–centralized Model Predictive Control (MPC) and decentralized fractional-order control–within a multivariable framework designed to regulate hypnosis, analgesia, and hemodynamic stabilization. Virtual patient simulations were used to evaluate these approaches under diverse anesthetic and, notably, hemodynamic conditions. The findings reveal that MPC delivers superior precision in maintaining the Bispectral Index (BIS) but tends to apply more aggressive adjustments to hemodynamic variables. In contrast, fractional-order control ensures smoother responses, though its ability to handle disturbances is less robust in certain scenarios. This study highlights the trade-offs between these strategies and advocates for further exploration of hybrid solutions to enhance patient safety and surgical outcomes.

关键词： Stability criteria Surgery Anesthesia Regulation Real-time systems Hemodynamics Safety Reliability Transient analysis Predictive control

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

Periodica polytechnica Electrical engineering and computer s...

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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

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Deep Learning-Based Blood Pressure Measurement on a Low-Power Microcontroller

Deep Learning-Based Blood Pressure Measurement on a Low-Powe...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Bálint Tóth Szabolcs Torma Ákos Nagy Luca Szegletes Department of Measurement and Information Systems Budapest Hungary Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Blood pressure is one of the most vital signals characterizing the health status of the human body. Based on this physiological data, many current and future diseases can be detected. Therefore, having a device that can continuously measure blood pressure without a cuff or medical intervention would be highly beneficial. This study aims to investigate non-invasive blood pressure measurement with the help of a low-power microcontroller, namely STM32F446RE. These measuring methods are based on deep neural networks and utilize externally measured photoplethysmography (PPG) and electrocardiogram (ECG) signals. The performance of the proposed models is compared to similar solutions from the literature and inspected for memory usage and runtime. The results of this work show that a convolutional layer-based model can be used for proper blood pressure estimation in such an energy-efficient device.

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A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems

arXiv

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arXiv 2023年

作者： Bast, Hannah Hertel, Matthias Prange, Natalie University of Freiburg Department of Computer Science Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

Existing evaluations of entity linking systems often say little about how the system is going to perform for a particular application. There are two fundamental reasons for this. One is that many evaluations only use aggregate measures (like precision, recall, and F1 score), without a detailed error analysis or a closer look at the results. The other is that all of the widely used benchmarks have strong biases and artifacts, in particular: a strong focus on named entities, an unclear or missing specification of what else counts as an entity mention, poor handling of ambiguities, and an over- or underrepresentation of certain kinds of entities. We provide a more meaningful and fair in-depth evaluation of a variety of existing end-to-end entity linkers. We characterize their strengths and weaknesses and also report on reproducibility aspects. The detailed results of our evaluation can be inspected under https://***/emnlp2023. Our evaluation is based on several widely used benchmarks, which exhibit the problems mentioned above to various degrees, as well as on two new benchmarks, which address the problems mentioned above. The new benchmarks can be found under https://***/ad-freiburg/fair-entitylinking-benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Petroleum reservoir evaluation

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TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping

arXiv

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arXiv 2023年

作者： Nagy, Attila Lakatos, Dorina Barta, Botond Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary

Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based on the dependency parse trees of the source and target sentences. We name this method TreeSwap. Our results show that TreeSwap achieves consistent improvements over baseline models in 4 language pairs in both directions on resource-constrained datasets. We also explore domain-specific corpora, but find that our method does not make significant improvements on law, medical and IT data. We report the scores of similar augmentation methods and find that TreeSwap performs comparably. We also analyze the generated sentences qualitatively and find that the augmentation produces a correct translation in most cases. Our code is available on Github. © 2023, CC BY.

关键词： Neural machine translation

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Robust Controller Design with Two-Degree-of-Freedom Feedback Loop Factorization for Oscillating Plant with Uncertain Time Delay*

Robust Controller Design with Two-Degree-of-Freedom Feedback...

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International Conference on Control, automation and Diagnosis (ICCAD)

作者： Marek Dlapa Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlin Zlin Czech Republic

Application of Robust Control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm of global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proved to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach.

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On resilience of future decarbonised energy systems in Europe

On resilience of future decarbonised energy systems in Europ...

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8th International Hybrid Power Plants & Systems Workshop (HYB 2024)

作者： Jonas Lotze Massimo Moser Veit Hagenmeyer Department of Strategic Grid Planning TransnetBW GmbH Stuttgart Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781837241484

The general scope the present model-based research is to analyse climate-neutral scenarios for Europe and to assess the respective resilience of the energy system. Two scenarios are therefore examined, one with an unrestricted global hydrogen market and the other with improved European energy resilience that limits pipeline imports of hydrogen from non-European countries. Key findings include increasing electrification of the heat and transport sectors, widespread use of Power-to-X appliances and, as a result, a doubling of total electricity consumption in Europe. On the power generation side, there is a need for massive expansion of renewables such as wind and photovoltaics. The analysis shows that a more resilient energy system will require additional electrolysis capacity, hydrogen storage and hydrogen grid capacity. The marginal cost of hydrogen will also increase as a result, and a higher price level will subsequently reduce the demand for hydrogen. The overall conclusion is that a transition from fossil fuels to renewables will, in any case, make the energy system much less dependent on imports and thus more resilient.

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Four-Channel Buck Converter in Asymmetrical Continuous Current Conduction Mode

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Canadian Journal of Electrical and Computer Engineering 2020年第4期43卷 251-260页

作者： Litvani, Lilla Hamar, Janos Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary

This article is concerned with the asymmetrical control strategies of a four-channel dc/dc buck converter in the continuous current conduction mode (CCM). The converter has two input and four output channels. Energy equations of the converter are studied, and asymmetry of input voltages, output voltages, and loads are examined in the CCM. The proposed control strategy allows us to directly control the power flow in each channel without any unwanted consequences for the rest of the channels among these conditions. The main advantage is the direct control of the total transferred power by the positive sequence of the capacitor voltage, the power exchange among the input channels by the negative sequence of the capacitor voltage, and the power exchange among the output channels by the inductor currents. Calculating these currents also helps to determine whether the converter at a certain point operates in the CCM or in the boundary mode (where zero current switching is possible). Feasibility of zero-voltage switching is simplified by this method. Converter efficiency is showed to be 83% at asymmetrical output voltages in the CCM. Theoretical studies are confirmed by both numerical simulations and laboratory experiments on a 100-W prototype. The studied converter can be applied in supplying multilevel inverters. © 2003-2010 IEEE Canada.

关键词： Zero current switching

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Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

arXiv

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arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

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