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Publications - 6278

System identification with generalized Prony schemes

Ferenc Schipp Tamás Dózsa Bokor Jozsef Gergo Ungvari Alexandros Soumelidis

Publication Name: Proceedings of the American Control Conference

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 5086-5092

Description:

We propose a novel method to identify the transfer functions of single-input-single-output linear time invariant (SISO-LTI) dynamic systems. Our approach makes use of the operator based generalization of Prony's method. In particular, the operator based Prony algorithm is used to reconstruct the transfer function of the system as a linear combination of rational basis functions. A considerable benefit of the proposed method is its robustness against the estimated system order. That is, if system order is over estimated, the correct system order can be found naturally. Another important benefit is that the proposed method is shown to be asymptotically robust towards zero expectation noise with the correct choice of certain evaluation functionals. The effectiveness of the proposed method is demonstrated through numerical experiments.

Open Access: Yes

DOI: 10.23919/ACC63710.2025.11107575

Managing the resolution of simulation models

No authors available

Publication Name: ESM 2008 - 2008 European Simulation and Modelling Conference: Modelling and Simulation 2008

Publication Date: 2008-01-01

Volume:

Issue:

Page Range: 38-42

Description:

A novel approach based on inflation and deflation is proposed for managing the resolution of simulation models. Different methods are proposed for manual or automatic deflation. An example is given how a topology description language can be extended to support the inflation/deflation concept. Dynamic management of the model resolution is introduced using the method called inflate-the-next and also two of its possible improvements. © 2008 EUROSIS-ETI.

Open Access: No

DOI: DOI not available

Achieving Sustainable Supply Chains: Applying Group Concept Mapping to Prioritize and Implement Sustainable Management Practices

Edit Süle Thompson McDaniel Gyula Vastag

Publication Name: Logistics

Publication Date: 2025-09-01

Volume: 9

Issue: 3

Page Range: Unknown

Description:

Background: Sustainability in supply chain management (SCM) practices is becoming increasingly important as environmental responsibility and social concerns, as well as enterprises’ competitiveness in terms of innovation, risk, and economic performance, become increasingly urgent. This paper aims to identify and prioritize concepts for implementing sustainable supply chains, drawing on sustainable supply chain management (SSCM) and green supply chain management (GSCM) techniques. Corporate supply chain managers across various industries, markets, and supply chain segments brainstormed management practices to enhance the sustainability of their supply chains. Four industry sectors were surveyed across five different value chain segments. Methods: A group concept mapping (GCM) approach incorporating multi-dimensional scaling (MDS) and hierarchical cluster analysis (HCA) was used. A hierarchy of practices is proposed, and hypotheses are developed about achievability and impact. Results: A decision-making matrix prioritizes eight solution concepts based on two axes: impact (I) and ease of implementation (EoI). Conclusions: Eight concepts are prioritized based on the optimal effectiveness of implementing the solutions. Pattern matching reveals differences between emerging and developed markets, as well as supply chain segments, that decision-makers should be aware of. By analyzing supply chains from a multi-part perspective, this research goes beyond empirical studies based on a single industry, geographic region, or example case.

Open Access: Yes

DOI: 10.3390/logistics9030099

A multigroup analysis focusing on assessing the green behavior of university employees for greening the workplace: a signaling theory perspective

Gábor Szábo-Szentgróti Masum Miah Virág Walter

Publication Name: Asian Education and Development Studies

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Purpose – This study aims to examine the effects of Top Management Commitment to Greening the Workplace (TMCGW) on Employee Green Behavior (EGB) through the mediating role of Green Human Resource Management (GHRM) and employee Green Self-Efficacy (GSE) at universities in Bangladesh. In addition, it aims to find the differentiating impact of these relationships between academic and non-academic employees in the university. Design/methodology/approach – Researchers employed partial least squares structural equation modeling (PLS-SEM) in an empirical study grounded in a conceptual model derived from existing literature. The sample size is 288 Bangladeshi university employees: academics (171) and non-academics (117). The study is non-random, particularly convenient sampling with self-administered questionnaires for data collection. Findings – The overall sample’s findings have revealed that TMCGW positively affects GHRM, which in turn fully mediates the relationship with EGB. In the overall sample, TMCGW also positively affects employee GSE, which in turn fully mediates the relationship with EGB. In addition, in the academic sample, TMCGW has a positive influence on employee GSE, which in turn fully mediates the relationship with EGB; however, GHRM doesn’t mediate the relationship between TMCGW and EGB. Furthermore, in the non-academic sample, TMCGW positively affects GHRM, which in turn fully mediates the relationship with EGB, but TMCGW positively affects employee GSE, with GSE not mediating between TMCGW and EGB. The multi-group analysis also reveals that the differences are significant only in the relationship between GHRM and EGB. Originality/value – This study makes unique contributions in that TMCGW acts as an antecedent of GHRM, and employee GSE in the university, subsequently affecting EGB by applying signaling theory. The study also found a unique contribution of how this relationship varies between academics and non-academic employees in the university. This study helps university top management and HR professionals to develop appropriate policies to promote sustainable behavior among academics and non-academics.

Open Access: Yes

DOI: 10.1108/AEDS-02-2025-0084

Two Stages Outlier Removal as Pre-Processing Digitizer Data on Fine Motor Skills (FMS) Classification Using Covariance Estimator and Isolation Forest

Surya Sumpeno A. Tormási Nurul Zainal Fanani Festa Yumpi Rahmanawati K. Khamid Adri Gabriel Sooai Mauridhi Hery Purnomo László T. Kóczy

Publication Name: International Journal of Intelligent Engineering and Systems

Publication Date: 2021-08-01

Volume: 14

Issue: 4

Page Range: 571-582

Description:

The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data. In the data stream or the data from sensor readings that produce large data, it allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS). The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.

Open Access: Yes

DOI: 10.22266/ijies2021.0831.50

Experimental Investigation of Preloaded Bolted Connections

Zsolt Bider

Publication Name: Strojnicky Casopis

Publication Date: 2026-04-01

Volume: 76

Issue: 1

Page Range: 1-6

Description:

The results of the experiments confirms that the torque-force graphs are made of passive and active sections. If we could determine a generalizable relation between the two sections, which would enable the programming of screwdriving machines in mass production in such a way that, based on the measured torque and considering passive resistances, a defined torque or angle could be applied to reduce the scatter of preload forces.

Open Access: Yes

DOI: 10.2478/scjme-2026-0001

Experimental Investigation of Vibroacoustic Behaviour of an Automotive Turbocharger with Semi-floating Bearing

Balázs Rácz Péter Sass Márk Pesthy J. Rohde-Brandenburger

Publication Name: Lecture Notes in Mechanical Engineering

Publication Date: 2021-01-01

Volume: 22

Issue: Unknown

Page Range: 245-255

Description:

Due to the strict European emission standards and the constant aspiration for the higher power density, turbochargers became essential components of the modern internal combustion engines. Turbochargers are high-speed operating machines thus the design of the rotor and the bearing system requires special attention. The motions of the rotor are affected by several parameters, such as bearing design, clearances, structure of the surface and also the quality and the physical properties of the used lubricant. If the motions of the rotor are intensive in a wide rotational speed range, the bearing load increases, resulting in a reduced lifespan. The motion of the rotor induces vibrations, which leads to audible noise emission to the environment. In this article, the vibrations of a four-cylinder spark ignition engine’s turbocharger are presented, based on component test-bench experiments. Furthermore, the main vibration components and their influencing factors are briefly introduced. During the experiments, the noise and vibrations of the turbocharger have been measured with different viscosity grade oils from 20 °C to 140 °C inlet temperature. The results showed that the amplitudes of both the synchronous and subsynchronous vibrations changed significantly and the volumetric flow is highly dependent on the temperature. The effect of the changing oil temperature will be analyzed with an emphasis on the subsynchronous vibrations and the possible cause of the phenomenon will be presented. Finding the optimal parameters with the lowest possible vibration response could result in an extended lifetime and provides important information for the balancing process during production.

Open Access: Yes

DOI: 10.1007/978-981-15-9529-5_21

THE RISE OF AI IN TOURISM - A SYSTEMATIC LITERATURE REVIEW

Rajermani Thinakaran Ferenc Erdos László Koloszár Bayboboeva Firuza

Publication Name: Geojournal of Tourism and Geosites

Publication Date: 2025-01-01

Volume: 60

Issue: Unknown

Page Range: 1254-1265

Description:

Tourism ranks among the world's largest industries, and its sustained expansion has paralleled swift advancements in technology. Artificial Intelligence (AI) is increasingly recognized as a transformative force in tourism, offering human-like capabilities that enhance decision-making and service automation. Its application across the sector improves operational efficiency and personalizes customer experiences, thereby fostering innovation and competitiveness. However, the rapid integration of AI also presents conceptual, theoretical, and societal challenges that require critical examination. The research aims to synthesize the conceptual and theoretical research on AI in tourism from 2019 onwards. It examines key themes, theoretical perspectives, methodological rigor, and research gaps in the existing literature. Further goal is to identify thematic areas with a specific focus on AI applications. The study followed the PRISMA guidelines to conduct a systematic literature review (SLR). Academic databases, including Scopus and Web of Science, were searched to identify scientific-relevant peer-reviewed articles. From an initial pool of over 400 studies, we identified 45 significant journal articles and selected them for an in-depth analysis, that collectively illuminate how AI is reshaping tourism research and practice. Studies have drawn on innovation diffusion theory to explain adoption patterns, technology acceptance models to gauge user and employee attitudes, and service quality and co-creation theories to understand how AI can add value to the customer experience. It also highlighted the evolution of AI research in tourism, from conceptual discussions to empirical investigations. Gaps and challenges in the research were identified, including a limited focus on human-AI interaction, ethical concerns, and methodological rigor. The review concludes that AI has the potential to transform tourism by enhancing efficiency, personalization, and sustainability. The findings reveal that AI has been envisioned as a catalyst for transformation in the tourism industry, with applications ranging from intelligent forecasting and revenue management to service automation via robots and hyper-personalized travel experiences. AI-driven analytics can improve decision support for revenue management, capacity planning, and marketing strategy. However, realizing this potential requires addressing the improvement of technological competence of human resources, ethical issues, and implementation strategies.

Open Access: Yes

DOI: 10.30892/gtg.602spl22-1498

Review and conceptual design of FPGA-based application for data-driven power electronic systems

Adrienn Dineva Adam Zsuga

Publication Name: 2021 17th Conference on Electrical Machines Drives and Power Systems Elma 2021 Proceedings

Publication Date: 2021-07-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

AI-based data-driven methods are an emerging research direction in the field of power electronics. However, because of the absence of large datasets, the development of these solutions have some barriers to overcome. To properly train machine learning algorithms and neural networks a large amount of training data is necessary. This dataset can be a union of simulation and measured data. Generating simulation data with computer simulations can be slow process and gathering real data is not cost-effective. Real-Time simulators based on FPGAs can be powerful tools to accelerate simulation, and create datasets for AI applications in a cost-effective and accurate way. In this paper the possible FPGA-based solutions, which can be applicable for the problems, have been reviewed. Their applicability have been discussed, moreover a simplified FPGA-based concept have been designed and embedded into two possible AI-based application area.

Open Access: Yes

DOI: 10.1109/ELMA52514.2021.9503033