Search in Publications

Found 6423 publications

Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation

Dragan Pamucar Fatih Ecer Alptekin Ulutaş Ömer Faruk Görçün Ejder Ayçin Hakan Aşan

Publication Name: Virtual Reality

Publication Date: 2026-03-01

Volume: 30

Issue: 1

Page Range: Unknown

Description:

Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research’s novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.

Open Access: Yes

DOI: 10.1007/s10055-025-01282-2

Unlocking the insights of dynamics through experimentation of an electric drive

Balázs Vehovszky Zoltán Gazdagh B. Fukker

Publication Name: Proceedings of ISMA 2024 International Conference on Noise and Vibration Engineering and Usd 2024 International Conference on Uncertainty in Structural Dynamics

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 2022-2030

Description:

This experimental example shows a study of the e-drive lamellae package, where the layered, punched sheets are axially compressed and the permanent magnets are embedded in the laminations. When studying such systems, using Restoring Force Surface (RFS) method allows users to create a snapshot of the behavior of the dynamic system. Due to the potential impact on the end user's perception of vehicle quality, the consideration of the changes in resin stiffness over time, the evolution of the time-dependent dynamic response in systems like Laminated Rotor Cores (LRC) remains an aspect in development. Technology development of nonlinear characterization of Laminated Rotor Cores (LRC) under different conditions allows engineers to understand how the dynamics evolve in time. Aging and hardening of polymer resin coating change the dynamics of the system. This change may be accelerated through thermal and centrifugal stress cycles or micro crack propagation of the resin coating. This investigation guides through the approach of nonlinear detection, and characterization and gives suggestions on how to linearize such systems.

Open Access: Yes

DOI: DOI not available

Support for universal basic income: A cross-disciplinary literature review

Gábor Szábo-Szentgróti Bence Végvári Virág Walter

Publication Name: Journal of Infrastructure Policy and Development

Publication Date: 2024-01-01

Volume: 8

Issue: 10

Page Range: Unknown

Description:

The technological development and the rise of artificial intelligence are driving a significant transformation of the labor market. The technological unemployment predicted by Keynes poses challenges for the global labor market that require new solutions. Basic income research has become a significant field of study, attracting attention from various disciplines such as political science, law, economics, and sociology. The aim of this paper is to explore on the basis of a literature review, what factors influence the support for basic income among the population. A systematic literature review based on the Web of Science and Scopus databases, after screening 2623 publications, identified 23 articles that contained findings relevant to the research question. A significant number of authors (12/23) analyzed data from the same source, the European Social Survey 2016 (ESS Round 8, 2020), conducted in 2016, first published in 2017 and updated several times since then. The paper shows that the study of the topic has a strong European focus. The social, economic, social and cultural diversity of European countries makes these studies important from a European and EU perspective, but from an international perspective, further research on the topic is needed.

Open Access: Yes

DOI: 10.24294/jipd.v8i10.7486

Predicting somatic cell count in milk samples using machine learning∗

István Szabó Bence Tarr János Tőzsér

Publication Name: Annales Mathematicae Et Informaticae

Publication Date: 2024-01-01

Volume: 60

Issue: Unknown

Page Range: 159-168

Description:

Milk quality is an important factor both for the farmers to be able to sell their products and for the milk industry to be able to plan its production based on quantity and quality. Milk quality has a direct link with cow health, more specifically with utter health. One of the most common utter diseases is mastitis. It always captures a lot of interest based on its frequency and cost as a dairy disease which eventually leads to an involuntary and premature culling of milking cows and decreased milk yield. The genetic evaluation of mastitis is very difficult as it is a low heritable trait and categorical in nature [2]. That is why it is necessary to find markers that could predict the occurrence of mastitis. One of the widely used such markers is the somatic cell count (SCC) [9] which is considered to be the most suitable indicator trait for mastitis resistance given its medium to high genetic correlation with mastitis and its greater heritability than mastitis. The SCC is also easy to record in the practice. The selection for lower SCC in milk has a positive effect on the incidence of mastitis. The selection against high SCC also does not deteriorate the immune system of cattle and decreases the risk of infection at the same time. The genetic evaluation [1] of this trait is mostly based on somatic cell score (SCS), a logarithmic transformation of SCC to achieve normality of distribution. In our study, we used the milk database of Holstein cows from 3 different farms. From each farm, we had altogether 8000 samples tested. The samples were analyzed using chemical methods every month for a year. 11 different types of data were recorded from each sample. Our aim was to find the best mixture of recorded data that would predict the value of linearized somatic cell count. After the logarithmic linearization the SCC results were divided into 3 main groups (based on the probability of mastitis). Thus our prediction problem turned into a classification problem. We used machine learning to train our algorithm. We experimented with different types of classification methods and found good results for the prediction of SCC in milk samples. We changed the input variables as not all the 9 measured input variables will be necessary for good prediction results. Our preliminary results show that using machine learning it is possible to build a model that can be used to predict mastitis in dairy cows based on variables generally analyzed during milk quality checking tests.

Open Access: Yes

DOI: 10.33039/ami.2024.02.004

Error handling techniques of genetic algorithms in parallel computing environment

Miklós F. Hatwágner András Horváth

Publication Name: Pollack Periodica

Publication Date: 2008-08-01

Volume: 3

Issue: 2

Page Range: 3-14

Description:

It is easy to create parallel genetic algorithm software with master-slave type paralelization on a cluster of workstations. In a real situation the probability of errors in communication or in some of the slave processes during a long calculation is significant. In this paper we deal with different error handling strategies in master-slave type paralelization of standard GA algorithms and show results of test calculations. Our simulations are close to real applications in the sense that we examine the best achieved objective function value at a fixed wall clock time with different error handling strategies depending on the probability of errors and number of processors. Using these results we make suggestions on the selection of a good error handling method in different optimization problems. © 2008 Akadémiai Kiadó.

Open Access: Yes

DOI: 10.1556/Pollack.3.2008.2.1

Minimizing Freshwater Usage in Batch Process Scheduling: S-Graph Approach

Máté Hegyháti Olivér Osz Dominic C.Y. Foo

Publication Name: Process Integration and Optimization for Sustainability

Publication Date: 2021-03-01

Volume: 5

Issue: 1

Page Range: 31-42

Description:

Water is one of the most important natural resources of life. While it is considerably cheap and vastly available currently, except for extreme locations, this is not guaranteed in the future. Being provident with water has multitude of advantages in both short and long term. Using less clean water not only brings immediate financial benefits, it simultaneously reduces wastewater production, related treatment costs, and the impact on the environment. Reducing the water footprint of a batch system is not a trivial task, as water sources and sinks need to be matched not only in quantity and quality, but in time as well. In this paper, the S-graph scheduling framework is extended to address simultaneous scheduling and water minimization in batch processing systems. The proposed approach tackles truly batch processes with a single contaminant, and allows only a single water source to be reused for each sink. The presented algorithm and S-graph extension have been implemented and tested on various case studies from the literature. The results of this paper provide an opportunity for further extensions to address a wider range of problems with multiple contaminants, semi-continuous behavior, cyclic operations, etc.

Open Access: Yes

DOI: 10.1007/s41660-020-00142-7

Classification of Time Series Using Singular Values and Wavelet Subband Analysis with ANN and SVM Classifiers

Balázs Benyó Péter Somogyi Béla Paláncz

Publication Name: Journal of Advanced Computational Intelligence and Intelligent Informatics

Publication Date: 2006-07-01

Volume: 10

Issue: 4

Page Range: 498-503

Description:

Oscillation of cerebral blood flow (CBF) in physiological or pathophysiological brain states is common, therefore it is promising to identify cerebral circulation disorders based on CBF signal classification. To characterize temporal blood flow patterns, we applied two feature extractions, spectral matrix and wavelet subband analysis. To distinguish between different physiological states, two different classifications have been developed – the radial basis function-based neural network and a support vector classifier with a Gaussian kernel. Feature extraction and classification are evaluated and their efficiency compared. Calculation was done using Mathematica 5.1 and its Wavelet Application.

Open Access: Yes

DOI: 10.20965/jaciii.2006.p0498

Interpolative decisions in the fuzzy signature based image classification for liver CT

Ferenc Lilik Melinda Kovacs Szonja Krisztina Szujo Laszlo T. Koczy Szilvia Nagy

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2021-07-11

Volume: 2021-July

Issue: Unknown

Page Range: Unknown

Description:

In computer aided diagnostics image processing and classification plays an essential role. Image processing experts have been developing solutions for different types of problems, that can be related to image processing, however, due to the sensitivity of the data and the high cost of medical experts, these experimental methods usually have very limited use in real medical practice. The databases that are available are very limited, thus the elsewhere usual and extremely effective convolutional neural network or other automated learning methods are not so easy to adjust for medical image processing. To overcome this difficulty, this paper presents an expert knowledge based method which describes the decision structures by fuzzy signatures. Values of various properties of Computer Tomography images as e.g. density or homogeneity are being considered in these signatures that are different in all case of liver diseases. Because of the low number of samples available, fuzzy sets that describes the leafs of the signatures leads to sparse systems, hence interpolation is needed. However further investigations of other interpolation methods are planned, Stabilized Koczy-Hirota interpolation seems to be appropriate.

Open Access: Yes

DOI: 10.1109/FUZZ45933.2021.9494401

Change in Stiffness of Reinforced Concrete Tunnel Walls and Its Effect Under Fire Load

Edina Koch Éva Lublóy Zoltán Major

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 342-350

Description:

This article uses the knowledge gained from tunnel fires to address the structural analysis of tunnel walls during fire exposure. The designing at normal temperature and its theoretical background are discussed in the literature. As these books did not yet deal with the issue of fire protection designing, we tried to supplement the existing theoretical knowledge with the knowledge provided by the relevant standards for reinforced concrete tunnel walls. In addition, we have tried to add our own individual ideas to the theory where we felt that there were gaps. The theoretical summary has been compiled in such a way that it can be easily transferred and applied to everyday practice. In this article, we discuss in detail the calculation of the internal forces in tunnel walls during fire exposure. Due to space constraints, the issue of designing at normal temperatures is only touched upon in this article, limiting it to the knowledge available in the literature. Since finite element modelling has become a commonly used technique in tunnel design since the 1970s, we use its potential to investigate the effects of earth pressure and surface loads on the tunnel walls during fire and their changes, using specific software for geotechnical design. In accordance with the limitations of the scope, the determination of the equivalent thickness and the modulus of elasticity of the tunnel wall is also presented in order to determine the internal forces during the fire action.

Open Access: Yes

DOI: 10.3233/ATDE240565