K. Kovács

56252782100

Publications - 29

Advances in invasive and non-invasive glucose monitoring: A review of microwave-based sensors

Balázs Benyó J. Geoffrey Chase K. Kovács Ameer B. Alsultani

Publication Name: Sensors and Actuators Reports

Publication Date: 2025-06-01

Volume: 9

Issue: Unknown

Page Range: Unknown

Description:

Effective and continuous glucose monitoring is critical in managing diabetes, which remains a global health challenge affecting millions. Traditional invasive glucose monitoring methods, although accurate, cause discomfort and are unsuitable for frequent measurements necessary for optimal diabetes management. To overcome these limitations, microwave-based sensors have emerged as promising alternatives, providing both invasive and non-invasive monitoring capabilities. This review critically evaluates recent advancements in microwave-based glucose sensors, emphasizing their design methodologies, sensitivity, accuracy, and clinical applicability. By leveraging unique dielectric properties of blood and tissues affected by glucose concentrations, microwave sensors enable precise and potentially pain-free glucose measurements. Despite significant progress, existing sensor technologies face challenges including limited sensitivity ranges, interference from biological tissues, and practical considerations for clinical adoption. This paper aims to guide researchers and healthcare providers by highlighting recent technological innovations, addressing current limitations, and suggesting directions for future research to advance glucose monitoring technologies towards widespread clinical use.

Open Access: Yes

DOI: 10.1016/j.snr.2025.100332

Chatbot assistant based on Large-Language Models for University students

Csaba Hajdu K. Kovács Ami Otsuka

Publication Name: Ines 2025 29th IEEE International Conference on Intelligent Engineering Systems 2025 Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 77-82

Description:

Large-language models (LLMs) have recently gained significant traction in natural language processing (NLP) by accurately modeling and imitating human-like conversations. One standout application area involves chatbots, which leverage LLMs to provide context-aware, natural language interactions. However, existing solutions often target English and rely on external cloud-based platforms, raising concerns about data privacy and language coverage. In contrast, this paper presents a locally deployable, Hungarian-language chatbot developed to assist university students with education and examination regulations. The proposed system ensures in-house deployment, facilitating compliance with institutional data policies and offering cost-effective scalability. Beyond offering straightforward answers on deadlines and academic rules, our chatbot is designed to handle more nuanced student inquiries, enhancing user experience and administrative efficiency. Preliminary testing demonstrates robust performance in Hungarian context. Future plans include extending the chatbot's domain to more complex subjects, broader document sets, and additional institutions, as well as integrating high-performance computing resources for large-scale deployments.

Open Access: Yes

DOI: 10.1109/INES67149.2025.11078205

NeuralODE-based Parameter Identification of the Three Chamber Model of the Circulatory System

Balázs Benyó Ákos Antal Bálint Szabó K. Kovács J. Geoffrey Chase Á Szlávecz

Publication Name: Iccc 2025 IEEE 12th International Joint Conference on Cybernetics and Computational Cybernetics Cyber Medical Systems Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 161-166

Description:

In cases of Acute Circulatory Failure, fluid therapy is a commonly used intervention to stabilize heart function. However, the effectiveness of fluid therapy is not directly predictable, and the therapy can also be harmful. Physiological models can be used to predict fluid responsiveness - describing the effectiveness of fluid therapy for the patient - but require solving complex parameter identification problems. The current study aims to develop a Physics Informed Neural Network, specifically a NeuralODE-based parameter identification method for the Three Chamber cardiovascular model, which has the potential to be used to define a novel perfusion marker. The method is developed and validated on a clinical data set collected in model animal experiments.

Open Access: Yes

DOI: 10.1109/ICCC64928.2025.10999147

Comparison of three artificial intelligence methods for predicting 90% quantile interval of future insulin sensitivity of intensive care patients

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Omer S. Alkhafaf Ameer B. Alsultani Á Szlávecz B. Paláncz

Publication Name: IFAC Journal of Systems and Control

Publication Date: 2024-12-01

Volume: 30

Issue: Unknown

Page Range: Unknown

Description:

Insulin dosing of hyperglycemic patients in the intensive care unit (ICU) is a complex and nonlinear clinical control problem. Recent model-based glycemic control protocols predict a patient-specific and time-specific future insulin sensitivity distribution, which defines the future patient state in response to insulin and nutrition inputs. The prediction methods provide a 90% confidence interval for a future insulin sensitivity distribution for a given time horizon, making the prediction problem more specific compared to common prediction problems where the aim is to predict the expected value of the given stochastic parameter. This study proposes three alternative artificial intelligence-based insulin sensitivity prediction methods to improve the prediction accuracy and make prediction parameters better fit the clinical requirements. The proposed prediction methods use different neural network models: a classification deep neural network model, a Mixture Density Network model, and a Quantile Regression-based model. A large patient data set was used to create the neural network models, including 2357 patients and 92646 blood glucose measurements from three clinical sites (Christchurch, New Zealand, Gyula, Hungary, and Liege, Belgium). Prediction accuracy was assessed by statistical metrics expressing clinical requirements, as well as via validated in-silico virtual patient simulations comparing the clinical performance of a proven glycaemic control protocol using the alternative prediction methods to assess impact on glycemic control performance and thus the need for these alternative models.

Open Access: Yes

DOI: 10.1016/j.ifacsc.2024.100284

In-Silico Validation of Insulin Sensitivity Prediction by Neural Network-based Quantile Regression

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Omer S. Alkhafaf Ameer B. Alsultani Alaa N. Roel Petra Pintár Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2024-09-01

Volume: 58

Issue: 24

Page Range: 368-373

Description:

High blood glucose levels and stress-induced hyperglycemia are common issues in intensive care units (ICU). Controlling blood glucose levels is crucial but challenging due to patient-specific variability. This challenge was addressed by developing model-based control protocols, which rely on identifying and predicting the patient-specific metabolic state. This study presents the in-silico simulation-based evaluation of a new artificial neural network-based insulin sensitivity (SI) prediction method. The models were trained on a dataset collected during clinical treatment using the stochastic-targeted (STAR) protocol and evaluated by simulating the clinical interventions on virtual patients created from retrospective clinical data. The results show the new models could be safely applied for SI prediction. Furthermore, the adopted method had very similar accuracy in the overall comparison of cohorts, with only minor differences noted in hypoglycemia events.

Open Access: Yes

DOI: 10.1016/j.ifacol.2024.11.065

Cardiovascular Model Identification Using Neural ODE

Balázs Benyó Ákos Antal J. Geoffrey Chase Bálint Szabó K. Kovács Cong Zhou Liam Murphy James Cushway Nicolas Davey Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2024-09-01

Volume: 58

Issue: 24

Page Range: 374-379

Description:

Acute circulatory failure (ACF) is a clinical syndrome when the heart and circulatory circulation cannot provide adequate blood supply to meet metabolic needs of the organs. ACF affects 30%- 50% of intensive care unit (ICU) patients. Fluid resuscitation is the primary treatment of ACF. However, it fails in a significant proportion (about 50%) of cases due to lack of clinically feasible non-invasive perfusion markers to assess the efficacy of the fluid therapy. Unfortunately, unsuccessful fluid therapy negatively affects patient outcome, increasing ICU length of stay and costs. Recent studies show identifying Stressed Blood Volume (SBV) of the cardiovascular system can be used to assess the potential efficacy of fluid therapy. The development of the diagnostic method requires the identification of the central arterial pressure curve based on the femoral arterial pressure, which is clinically available. This central arterial pressure curve can be used to identify the cardiovascular system parameters. In this study, the main goal was to develop a parameter-identification method for the Tube-load model-based transfer function connecting the femoral and central arterial pressure curve by using the so-called Physics-informed Neural Network methodology, namely the Neural ODE method. The study presents the adaptation of the Neural ODE method to the given parameter identification problem and the validation of the developed identification method. The robustness of the developed identification method was tested and used on a series of measurement data recorded in animal experiments.

Open Access: Yes

DOI: 10.1016/j.ifacol.2024.11.066

Fake News Classification on Social Media in the Field of Environmental Engineering

Ferenc Erdos K. Kovács Richard Nemeth

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 961-966

Description:

Environmental engineering plays a crucial role in addressing the sustainability challenges of the environment and everyday life. These areas cover a wide range of aspects, from environmental impacts, efficient use of natural resources, waste management and recycling, and improving air and water quality to preventing, managing and mitigating environmental hazards and disasters. For example, climate change, global warming, pollutant emissions, etc., are frequently discussed on social media. Unfortunately, however, these platforms, as the primary means of mass information, provide scope for misinformation, manipulation and influencing opinions through dubious or outright fake news and posts. Despite the prevalence and growing volume of research on detecting fake news, there is a significant lack of quality publications focusing specifically on this environmental engineering-related area. In this paper, the authors aim to bridge this gap by developing a supervised machine-learning model for detecting fake news in this field of science. To build a robust model, a training dataset was constructed by using a specific dataset of labelled social media posts containing keywords from Chemical Engineering Transactions journal articles, with a particular focus on sustainability and environmental aspects. This research contributes to enhancing the integrity of environmental engineering in social media discourse by providing a tool for identifying and mitigating the spread of misinformation, promoting informed decision-making and critical thinking, and improving public awareness of environmental matters.

Open Access: Yes

DOI: 10.3303/CET24114161

Applying NeuralODE-based Cardiovascular Model Identification for Experimental Data Analysis

Balázs Benyó Ákos Antal J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz

Publication Name: Saci 2024 18th IEEE International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 437-442

Description:

Recent model-based diagnostic methods have been found promising to provide non-invasive perfusion markers to assess the efficacy of fluid therapy, the most common treatment method for acute circulatory failure (ACF). The development of these model-based diagnostic methods requires the identification of the central arterial pressure curve based on the femoral arterial pressure. This current study presents improvements of the previously suggested NeuralODE-based identification method by suggesting the use of a physiologically interpretable parameter set of the Tube-load model-based transfer function for the physiological system analysis and suggesting a calculation method decreasing the measurement error-caused uncertainty of the identification parameter, called pulse transfer time.

Open Access: Yes

DOI: 10.1109/SACI60582.2024.10619737

Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: Computer Methods and Programs in Biomedicine

Publication Date: 2023-10-01

Volume: 240

Issue: Unknown

Page Range: Unknown

Description:

Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol – used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia – is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.

Open Access: Yes

DOI: 10.1016/j.cmpb.2023.107633

Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2023-07-01

Volume: 56

Issue: 2

Page Range: 2091-2095

Description:

Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied.

Open Access: Yes

DOI: 10.1016/j.ifacol.2023.10.1110

Modeling the Correlation of Human Vertebral Body Volumes

Balázs Benyó P. Somogyi Bálint Szabó K. Kovács Sándor Bazsó Á Szlávecz Árpád Viola

Publication Name: IFAC Papersonline

Publication Date: 2023-07-01

Volume: 56

Issue: 2

Page Range: 9030-9035

Description:

Anatomical parameters of the human body strongly correlate with each other. Modelling these dependencies enables the creation of a realistic anatomical human body model that can be parameterized. Such a model can be used for several diagnostic processes to identify abnormalities or even give guidance in surgical interventions. This paper proposes a probabilistic model describing the dependencies between the vertebral body volumes of humans from the Caucasian human race. As demonstrated, the proposed model can accurately describe the relationship between the vertebral body volumes and is used for the prediction of an unknown vertebral volume based on a known one. The probabilistic model is created by using the CT segmentation of 37 patients.

Open Access: Yes

DOI: 10.1016/j.ifacol.2023.10.133

Increasing Patient Specificity of the Recurrent Neural Network Based Insulin Sensitivity Prediction by Transfer Learning

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: Ines 2022 26th IEEE International Conference on Intelligent Engineering Systems 2022 Proceedings

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 27-32

Description:

Insulin therapy is a frequently applied treatment in intensive care to normalize the patient's blood glucose level increased by stress-induced hyperglycaemia. This therapy is generally referred to as Tight Glycaemic Control (TGC). The STAR (Stochastic-TARgeted) protocol is a TGC which uses the patient's insulin sensitivity (SI) as a key parameter to describe the patient's actual state. Prediction of the future patient's state, i.e. prediction of the patient's future SI value, is a crucial step of the protocol currently implemented by using the so-called Intensive Care INsulin Glucose (ICING) model of the human glucose-insulin system and an associated stochastic model. In our previous studies, we have shown that the Recurrent Neural Network (RNN) models are efficient alternative methods of SI prediction. In this paper, we suggest applying the so-called transfer learning technique to further enhance the accuracy of the SI prediction by using the SI history of the current patient. The paper presents the proposed methodology for applying transfer learning in SI prediction and the evaluation of the method's accuracy by comparing the outcomes with the currently applied solution. Insilico validation using real patients' data is involved in this validation.

Open Access: Yes

DOI: 10.1109/INES56734.2022.9922645

Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Yahia Anane Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2020-01-01

Volume: 53

Issue: 2

Page Range: 16335-16340

Description:

Stress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most widely applied TGC protocol is the STAR (Stochastic-TARgeted) protocol which uses the insulin sensitivity (SI) for the assessment of the patients state. The patient-specific metabolic variability is managed by the so-called stochastic model allowing the prediction of the 90% confidence interval of the future SI value of the patients. In this paper deep neural network (DNN) based methods (classification DNN and Mixture Density Network) are suggested to implement the patient state prediction. The deep neural networks are trained by using three years of STAR treatment data. The methods are validated by comparing the prediction statistics with the reference data set. The prediction accuracy was also compared with the stochastic model currently used in the clinical practice. The presented results proved the applicability of the neural network based methods for the patient state prediction in the model based clinical treatment. Results suggest that the methods' prediction accuracy was the same or better than the currently used stochastic model. These results are the initial successful step in the validation process of the proposed methods which will be followed by in-silico simulation trials.

Open Access: Yes

DOI: 10.1016/j.ifacol.2020.12.659

Finite Element Simulation Based Analysis of Valve-sparing Aortic Root Surgery

Tamás Umenhoffer Balázs Benyó P. Somogyi K. Kovács Róbert Nagy Bernhard Laufer Anikó Kubovje Á Szlávecz Tamás Szerafin

Publication Name: IFAC Papersonline

Publication Date: 2020-01-01

Volume: 53

Issue: 2

Page Range: 16037-16042

Description:

The valve-sparing aortic root surgery is frequently used in the treatment of aortic root enlargement or aortic root aneurysm. The currently used common surgical practice assumes that the valve leaflets are distributed evenly around the circle defined by the aorta wall which is frequently a false assumption according to hart anatomy studies. A finite element simulation based method is proposed in this study for the analysis of the alternative surgical outcomes of the valve-sparing aortic root surgery. The simulation methods allow the definition of the aortic valve leaflet commissure positions and the diameter of the graft used to replace the aortic root. The suggested methods are able to estimate and quantitatively compare the hemodynamic functions and the robustness of the aortic valve functions. The corresponding modeling environment makes possible the easy definition of the patient specific aortic root model that is used as an input of the simulation. The initial validation of the simulation method was done by a real patient data based simulation study. These results suggest that the currently used surgical practice can be improved.

Open Access: Yes

DOI: 10.1016/j.ifacol.2020.12.409

Extension of a Glycaemic Control Medical Application with New Functions and Ergonomic User Interface Elements

Mihaela Lascu Balázs Benyó K. Kovács Brigitte Beatrix Schweisthal Á Szlávecz

Publication Name: 2018 13th International Symposium on Electronics and Telecommunications Isetc 2018 Conference Proceedings

Publication Date: 2018-12-19

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The human body is composed of numerous highly complex metabolic processes. Abnormal variation of the blood glucose concentration is a common complication in the intensive care units. This paper presents a model-based method of glycaemic control and a medical application implementing the glycaemic control protocol. The principal cases were to extend the functionality of the application in order to automatically create multiple episodes for a patient already in the database of the application and an accessibly view for previous treatments calculation. These functions are very helpful and convenient because allow to the clinicians to have new measurements and a treatment calculation without entering the data of the patient again in the system. The visualization of previous treatments can be efficiently used to define optimized dosing for the subsequent treatment period. The paper concludes with the results of the extension of the medical application.

Open Access: Yes

DOI: 10.1109/ISETC.2018.8583885

Initial value selection of the model parameters in the curve fitting phase of the dynamic SPECT imaging

Balázs Benyó P. Somogyi G. Hesz K. Kovács Zsófia Barna Á Szlávecz

Publication Name: IFAC Papersonline

Publication Date: 2018-01-01

Volume: 51

Issue: 27

Page Range: 241-246

Description:

The dynamic SPECT (Single Photon Emission Computed Tomography) reconstruction algorithm developed in our prior work reconstructs the parameters of the time activity curve for each image voxel directly from the projection images. In each iterations of the SPECT reconstruction beyond the static 3D MLEM (Maximum Likelihood Expectation Maximization) step, the algorithm performs a fitting process for each voxel in order to estimate the parameters describing the function of the examined organ considering that the time frames are not independent from each other. In real cases the fitted curve is nonlinear function of these parameters, it is usually described as the sum of exponential functions. In order to estimate the parameters properly, an iterative root-finding method is applied. In the current study the Newton-Raphson method is used. The selection of a proper initial value for the root-finding method is critical in order to achieve convergence of the fitting process. If the initial guess is not appropriate, the root-finding algorithm can diverge or converge to an inappropriate parameter set that can result in unacceptable reconstructed parameters. This affects then the subsequent MLEM iterations, also neighboring voxels and breaks the reconstruction. In this work we investigated different methods to calculate the initial values of the fitting process and evaluated the reconstructed parameter set of the dynamic SPECT reconstruction algorithm. Three different methods are investigated, one that uses the fitted parameters of the previous MLEM iteration, one that is based on the sum of the geometrical series of the exponentials and one that calculates the best guess using both methods. The three methods were compared by benchmark reconstruction cases using a mathematical phantom. In each reconstruction different initial value selection method was applied then the time activity curves of the voxels belonging to the same tissue were statistically evaluated using the reconstructed parameters. In the study no significant differences were found in the mean value of the reconstructed parameters. The standard deviation of the parameters was similar between the two simple approaches, however, the combination of the methods resulted in better statistical performance.

Open Access: Yes

DOI: 10.1016/j.ifacol.2018.11.635

Safe and secure implementation of the global platform conform infrastructure supporting the customer centric model based ecosystem

Balázs Benyó G. Fördös K. Kovács B. Sodor A. Vilmos

Publication Name: Ines 2016 20th Jubilee IEEE International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2016-08-26

Volume: Unknown

Issue: Unknown

Page Range: 131-140

Description:

Global Platform (GP) as a cross industry, nonprofit association develops and publishes specifications promoting secure deployment and management of multi-Application environments of smart card technology based applications and ensuring their interoperability. The GP specifications define the business and technological processes and the associated security requirements. This publication introduces the design and development of a fully functioning smart card application management infrastructure implementing all the related GP processes and corresponding security requirements. The infrastructure supports all the phases of the application life cycle and all the partners in the ecosystem. The system is tested by several trials and certified by independent security assessment.

Open Access: Yes

DOI: 10.1109/INES.2016.7555107

Investigation of 3D SPECT reconstruction with multi-energy photon emitters 1

Balázs Benyó G. Hesz K. Kovács Á Szlávecz

Publication Name: IFAC Papersonline

Publication Date: 2015-09-01

Volume: 28

Issue: 20

Page Range: 30-35

Description:

Parallel projection based Single Emission Computed Tomography is a widely used nuclear imaging technique. Non-homogeneous attenuation medium and the distance dependent spacial resolution (DDSR) of the parallel imaging cause serious artefacts during image reconstruction. Both effects are dependent on the energy of gamma photons used for imaging. In this paper an efficient parallel reconstruction algorithm is introduced that is executed on the Graphics Processing Unit. The aim of the presented study was to investigate the possibilities of reconstruction techniques when multi-energy photon emitters are used. An analytical 3D projector with attenuation and detector response modelling was used to generate projection sets for Gallium-67 isotope studies. Data were reconstructed from one photopeak only using the corresponding attenuation map. The projection data sets were added together and reconstructed using average attenuation and DDSR compensation values as well as simulating every photopeak individually. The third combined reconstruction technique was using every projection set as separate measurement. In this study we showed that all three strategies result in similar image quality, however the average attenuation correction method is computationally less demanding.

Open Access: Yes

DOI: 10.1016/j.ifacol.2015.10.110

Altered blood glucose dynamics during and after anhepatic phase of liver transplantation: A model-based approach

Balázs Benyó J. Geoffrey Chase Jozsef Homlok Paul Docherty K. Kovács

Publication Name: Ines 2013 IEEE 17th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2013-12-12

Volume: Unknown

Issue: Unknown

Page Range: 65-68

Description:

During liver transplantation (LT) the glucose metabolism is effected by a crucial disturbance. The blood glucose level is extremely hard to control by conventional clinical protocols during this phase. Model based approach can enhance the blood glucose control during the anhepatic phase (absence of liver) and post-anhepatic phase. The physiological constants of validated clinical metabolic model were slightly modified based on previous studies. The model fitting errors and the sufficient capture of the blood glucose (BG) dynamic evinced the applicability of the model. However the sufficient per-patient estimation of endogenous production could more enhance the performance of the model based BG prediction. © 2013 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2013.6632784

Video based urban traffic analysis in signalized intersections

Balázs Benyó Jozsef Homlok K. Kovács

Publication Name: Ines 2011 15th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2011-08-22

Volume: Unknown

Issue: Unknown

Page Range: 319-324

Description:

Nowadays it is a frequent problem that, the infrastructure-development of land transportation comes up against space limits in many cases, moreover the growth of traffic doesn't slack up. This is particularly typical for urban traffic, effecting traffic jams, lags; not to mention the problem of air pollution. In this case the requirements of sustainable development can be satisfied by the optimization of the existing infrastructure. This procedure always begins by observing, collecting and processing information, followed by model creation. In the next step the identified model will help us to analyze even the most extreme situations through different simulation environments and to prepare different traffic analyses. © 2011 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2011.5954766

Model-based analysis of traffic flow in signalized intersections

Jozsef Homlok K. Kovács

Publication Name: Ines 2010 14th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2010-07-26

Volume: Unknown

Issue: Unknown

Page Range: 189-194

Description:

Traffic systems are man-made systems in which the human component as a complex factor plays a significant part. [1] The aim of the research was to offer a method which is applicable for setting the real traffic situations at a signalized intersection against the simulated ones, using a traffic model with similar statistical features. The simplest technical apparatus has been used to monitor the real traffic situations. Only one video camera is needed for data collection and it is located in the observable area. Hence time-dependent parameters have been collected which are available for the analysis of the traffic situations. In the case of both traffic models the analysis is based on a behavior model of inhomogeneous driver-populations and an estimation method. The behavior model of inhomogeneous driver-populations handles the time-dependent parameters of driver behavior as the most important predictors, describes the movement and the behavior of the inhomogeneous driver-populations approaching a signalized intersection, passing the amber signal light. The estimation method developed using the model parameters opens the door to estimating the composition of drivers and measuring the capacity of an intersection on a more sensitive scale. © 2010 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2010.5483849

A novel behavior model based estimation method for the traffic capacity of signalized intersections

Jozsef Homlok K. Kovács

Publication Name: 17th ITS World Congress

Publication Date: 2010-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Traffic systems are man-made systems in which the human component as a complex factor plays a significant part. (1) The realistic modeling of them is extremely complicated. The aim of the paper is to present a traffic behavior model of inhomogeneous driver-populations and a corresponding estimation method. The traffic behavior model is developed for describing traffic flow in a signalized intersection. It works with inhomogeneous driver populations; different type of drivers may make different decisions in the same traffic situation. This way various driver types can be defined like aggressive, normal or conservative etc. drivers. The model focuses on the movement and the behavior of the inhomogeneous driver-populations approaching a signalized intersection, passing the amber signal light. The developed estimation method is intended to define the distribution of different driver types. This theory can allow an extensive and more complex traffic analysis on a more sensitive scale. The paper details the steps of the estimation process and the validation and introduces the results of the application of the estimation method with real experiments.

Open Access: Yes

DOI: DOI not available

Estimating "level of safety" in traffic modeling using human error

Zsuzsanna Tóth-Szabó K. Kovács

Publication Name: 15th World Congress on Intelligent Transport Systems and ITS America Annual Meeting 2008

Publication Date: 2008-12-01

Volume: 4

Issue: Unknown

Page Range: 2562-2573

Description:

The aim of the paper is to present the results of a study about involving the human error into traffic modeling. The analysis of driver's behavior in a selected traffic situation (drivers do or do not stop at red light) provided us to define the meaning human error in the context. Based on Bayesian theorem, the probability of bad decision is one of the results of the first part of this paper. Having built the junctions in VISSIM environment, and after a detailed investigation of output parameters (in simulated traffic there is no car passing at red), a software module was developed for having the bad decision probabilities using VISSIM. Grouping drivers by driving style (aggressive, normal, slow) gives opportunity (defining numbers of driver passing at red) to find the missing link. The trial version of this software now is able to define the probability of bad decision using VISSIM outputs.

Open Access: Yes

DOI: DOI not available

Do we have to take into account the human mistakes during traffic simulation?

Zsuzsanna Tóth-Szabó K. Kovács

Publication Name: Ines 2007 11th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2007-12-01

Volume: Unknown

Issue: Unknown

Page Range: 45-49

Description:

This paper deals with an actual question of traffic modeling: the human mistakes are implicable and is it needed to involve them into a traffic model or not? The first part of the paper gives a short survey of the role of traffic simulation. The second part of the paper contains our approach and represents the first step of our method aimed at the development of a driver model applicable in the widely used simulation systems to describe practical traffic situations. ©2007 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2007.4283670

NFC applications and business model of the ecosystem

Balázs Benyó L. Kutor K. Kovács A. Vilmos

Publication Name: 2007 16th Ist Mobile and Wireless Communications Summit

Publication Date: 2007-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

StoLPaN, a pan-European consortium of companies, universities and user groups co-funded by the European Commission (EU), Information Society Technologies (IST) Programme aims to define open commercial and technical frameworks for the remote management of NFC-enabled services on mobile devices. These frameworks will facilitate the deployment of NFC-enabled mobile applications across a wide range of vertical markets, regardless of the phone type and the nature of the services required. This paper introduces the business aspects of the NFC based service development and the technical infrastructure implementing the core of the NFC-enabled services.

Open Access: Yes

DOI: 10.1109/ISTMWC.2007.4299324

The design of NFC based applications

Balázs Benyó L. Kutor K. Kovács A. Vilmos

Publication Name: Ines 2007 11th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2007-12-01

Volume: Unknown

Issue: Unknown

Page Range: 277-280

Description:

StoLPaN, a pan-European consortium of companies, universities and user groups co-funded by the European Commission (EU), Information Society Technologies (IST) Programme aims to define open commercial and technical frameworks for the remote management of NFC-enabled services on mobile devices. These frameworks will facilitate the deployment of NFC-enabled mobile applications across a wide range of vertical markets, regardless of the phone type and the nature of the services required. This paper introduces the business aspects of the NFC based service development and the technical infrastructure implementing the core of the NFC-enabled services. ©2007 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2007.4283711

Human factor in traffic models

Zsuzsanna Tóth-Szabó Richard Horváth K. Kovács

Publication Name: 2006 IEEE International Conference on Computational Cybernetics Iccc

Publication Date: 2006-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper deals with modeling of human behavior in traffic systems. The first part of the paper gives a short survey of the corresponding literature introducing the most important problems of the area. The second part of the paper contains our approach aiming the development of a driver model applicable in the widely used simulation systems to describe practical traffic situations and analyze the safety aspects.

Open Access: Yes

DOI: 10.1109/ICCCYB.2006.305738

Design and implementation of Enum-based services

F. Hatwágner T. Heckenast Á Varga N. Varjasi Balázs Benyó K. Kovács

Publication Name: Journal of Universal Computer Science

Publication Date: 2006-11-23

Volume: 12

Issue: 9

Page Range: 1128-1138

Description:

ENUM is a technology based on a procedure that assigns a sequence of traditional telephone numbers to Internet domain names. It specifies a rule that makes it possible to relate a domain to a telephone number without any risk of ambiguity. This domain can then be used to identify various communication services like fax, mobile phone numbers, voice-mail systems, e-mail addresses, IP telephone addresses, web pages, OPS coordinates, call diverts or unified messaging. In our paper we deal with three main problem areas in connection with the business model of the ENUM service and with the introduction of new services, i.e. the questions of tariffs, legal regulations and financial return. For the ENUM procedure to spread out in use specific services have to be implemented that can exploit the advantages of the ENUM and efficient methods have to be elaborated to base existing services on ENUM. We will outline the two new services invented by our group and that we have implemented in our project. © J.UCS.

Open Access: Yes

DOI: DOI not available

Novel communication services based on ENUM technology

F. Hatwágner T. Heckenast Á Varga N. Varjasi Balázs Benyó K. Kovács

Publication Name: Ines 05 IEEE 9th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2005-01-01

Volume: 2005

Issue: Unknown

Page Range: 217-220

Description:

ENUM is the short name for a protocol for connecting resources of telecommunication and the Internet to one another. It specifies a rule that makes it possible to relate a domain to a telephone number without any risk of ambiguity. This domain can then be used to identify various communication services like fax, mobile radio, voice-mail systems, e-mail addresses, IP telephony addresses, web pages, GPS coordinates, call diverts or unified messaging. Several countries in the world examine the possibilities of the use of ENUM in national trials. Recently two Hungarian universities and an Internet service provider established a research project aiming the development of ENUM based communication services. In Hungary we have to deal not only with the questions of creating and providing the technological background, but also with the business aspects of ENUM based service development. The article will firstly give an overview about the ENUM itself. Then it will deal with ENUM services and business models. After that we discuss the situation of ENUM in different European countries. It will present the planned and accomplished ENUM based services in these countries. Then we are going to give the aims of the project in Hungary, which is led by the Szechenyi Istvan University in Gy6r. In setting these aims we can rely on the projects of other countries, and learn from them. © 2005 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2005.1555160