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Found 6412 publications

Cognitive Aspects of 2D Content Integration and Management in 3D Virtual Reality Spaces

Adam B. Csapo Tarek Setti

Publication Name: Infocommunications Journal

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 37-45

Description:

The advent of 2D graphical user interfaces in the 1980s shifted user interactions from line-based terminals to icon-based interfaces. As smartphones emerged in the 2010s, portable 2D graphical interfaces became a reality, liberating users from being confined to a single location when accessing digital services. These transformations have profoundly altered our understanding of digital information systems, with impacts that cannot be easily quantified. Current advancements in virtual and augmented reality (VR/AR), the Internet of Things (IoT), and artificial intelligence (AI) are on the verge of ushering in the next significant leap in cognitive expansion, introducing portable and highly contextual spatial interfaces, also sometimes referred to as Digital Realities (DRs). As a result, users now anticipate the ability to engage with an increasing array and variety of digital content in ways that are more contextualized and tailored to their needs, taking into account factors such as time, location, personalized goals and user-specific histories. In this paper, we aim to give an overview of cognitive aspects relevant to content integration and management specifically in DR environments, and to propose solutions and / or best practices to address them. Our discussion is centered around a paradigm called the Doing-When-Seeing (DWS) paradigm, which we propose for the design of Digital Reality interfaces. We demonstrate the applicability of this paradigm to the design of interfaces for creating content, organizing content, and semantically representing and retrieving content within 3D Digital Reality environments.

Open Access: Yes

DOI: 10.36244/ICJ.2023.6.5

Comparative study of women's labour market discrimination according by sector

Szabó Szentgróti Gábor Kőműves Zsolt Szabó Szilvia Hollósy Vadász Gábor

Publication Name: Civil Szemle

Publication Date: 2023-01-01

Volume: 20

Issue: 3

Page Range: 157-172

Description:

In our research, we examined discrimination against women on the labour market by sector. Our research question is whether women perceive discrimination on the labour market differently depending on the sector in which they work. In this context, the following hypotheses have been tested: According to H(1): those working in different sectors experience different discriminatory factors in their job search. According to H(2), people working in different sectors have different perceptions of the pay gap between men and women. According to H(3), workers in different sectors differ in whether they have ever felt any discrimination in their workplace based on their gender. Snowball sampling was used in the research, therefore, our research results cannot be generalized. In the course of the research, a self-developed questionnaire was completed by the study subjects. The online survey was conducted in the spring of 2022, with a total of 530 female respondents. ANOVA, LSD post-hoc, and Chi-square tests were used to analyze the results, and these statistical methods confirm all our hypotheses. The answer to our research question is: women, depending on the sector in which they work, perceive discrimination in the workplace differently. Based on the justification of the first hypothesis, those working in different sectors experience different discriminatory factors. The second hypothesis demonstrates that women working in different sectors have a differentiated view of the pay gap between men and women. The third hypothesis confirmed that women working in different sectors have different opinions on whether they have been previously discriminated against in the workplace due to their gender.

Open Access: Yes

DOI: DOI not available

Clothoid-based Trajectory following Approach for Self-driving vehicles

Claudiu Radu Pozna Erno Horváth

Publication Name: Sami 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics Proceedings

Publication Date: 2021-01-21

Volume: Unknown

Issue: Unknown

Page Range: 251-254

Description:

Lately self-driving navigation and control have obtained significant attention in many fields, such as mobile robotics or autonomous driving. Although sensing, perception, planning and following subtasks associated with autonomous vehicles persist with open challenges. In this paper the autonomous following subtask is targeted. The paper proposes trajectory following approach which is designed for self-driving vehicles.

Open Access: Yes

DOI: 10.1109/SAMI50585.2021.9378664

On wavelet based enhancing possibilities of fuzzy classification methods

Ferenc Lilik Brigita Sziova Levente Solecki Laszlo T. Koczy Szilvia Nagy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2020-01-01

Volume: 945

Issue: Unknown

Page Range: 56-73

Description:

If the antecedents of a fuzzy classification method are derived from pictures or measured data, it might have too many dimensions to handle. A classification scheme based on such data has to apply a careful selection or processing of the measured results: either a sampling, re-sampling is necessary or the usage of functions, transformations that reduce the long, high dimensional observed data vector or matrix into a single point or to a low number of points. Wavelet analysis can be useful in such cases in two ways. As the number of resulting points of the wavelet analysis is approximately half at each filters, a consecutive application of wavelet transform can compress the measurement data, thus reducing the dimensionality of the signal, i.e., the antecedent. An SHDSL telecommunication line evaluation is used to demonstrate this type of applicability, wavelets help in this case to overcome the problem of a one dimensional signal sampling. In the case of using statistical functions, like mean, variance, gradient, edge density, Shannon or Rényi entropies for the extraction of the information from a picture or a measured data set, and they don not produce enough information for performing the classification well enough, one or two consecutive steps of wavelet analysis and applying the same functions for the thus resulting data can extend the number of antecedents, and can distill such parameters that were invisible for these functions in the original data set. We give two examples, two fuzzy classification schemes to show the improvement caused by wavelet analysis: a measured surface of a combustion engine cylinder and a colonoscopy picture. In the case of the first example the wear degree is to be determine, in the case of the second one, the roundish polyp content of the picture. In the first case the applied statistical functions are Rényi entropy differences, the structural entropies, in the second case mean, standard deviation, Canny filtered edge density, gradients and the entropies. In all the examples stabilized KH rule interpolation was used to treat sparse rulebases. The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [1].

Open Access: Yes

DOI: 10.1007/978-3-030-18058-4_5

Detection of Brake Disc Deformation With Adaptive Wavelet Neural Networks

István Lakatos Péter Kovács Tamás Dózsa Péter Ori Gergo Ungvari Zoltan Szabo Alexandros Soumelidis

Publication Name: IEEE Transactions on Instrumentation and Measurement

Publication Date: 2026-01-01

Volume: 75

Issue: Unknown

Page Range: Unknown

Description:

In this work, we consider the problem of detecting the phenomenon of brake disc runout, which is usually caused by deformations of the brake disc in passenger vehicles. We introduce a novel measurement system that collects and processes information on the vibration profile of the vehicle, as well as the pressure of the brake fluid. Our novel processing method for the collected signals relies on model-driven adaptive wavelet neural networks. We demonstrate that the proposed signal processing methodology outperforms previous approaches to detect aberrations of the brake disc. In addition, the proposed neural network models are defined by interpretable parameters and have reduced complexity when compared to traditional and similar approaches. These characteristics make the proposed construction suitable for automotive applications.

Open Access: Yes

DOI: 10.1109/TIM.2026.3674239

Drop performance of dangerous goods packages in the aspect of parcel delivery standards

Ákos Mojzes Kata Vöröskoi Thomas Trost

Publication Name: 21st Iapri World Conference on Packaging 2018 Packaging Driving A Sustainable Future

Publication Date: 2019-01-01

Volume: Unknown

Issue: Unknown

Page Range: 569-577

Description:

Requirements for dangerous goods packaging are well known, whatever version are used. The testing circumstances are strictly defined for each transportation method (road, rail, air, sea). But nowadays it is becoming a practice that courier express operators transport dangerous goods as single package. This parcel delivery method means a higher risk for all kind of logistics participants. By this service the packages are delivered fast, but handled more roughly than in comparison to LTL (less than truckload) or FTL (full truck load). Naturally, the parcel delivery sector uses its own suitability testing methods, which are also well defined. These procedures are coming from various standards such as ASTM, ISTA or corporate (FedEx) standards. This paper compares the most common parcel delivery testing conditions concerning the drop test requirements of DGR (Dangerous Goods Regulation) using packaging such as paper bag, corrugated fibreboard box, steel drum and plastic jerrycan, respectively. Then the test results were analyzed to present the differences.

Open Access: Yes

DOI: DOI not available

Some considerations on data mining from questionnaires by constructing fuzzy signatures based on factor analysis

Dalia Susniene Ojaras Purvinis Laszlo T. Koczy

Publication Name: Journal of Intelligent and Fuzzy Systems

Publication Date: 2019-01-01

Volume: 36

Issue: 4

Page Range: 3739-3749

Description:

To interpret and to process the answers to questionnaires with large amount of questions may be not easy task. They are multidimensional data, sometimes with high dimensionality (in the hundreds). Therefore, it is necessary that some data reduction approach should be employed. On the other hand, answers to specific questions in questionnaires are imprecise, and the type and degree of imprecision is determined by the kind of the questions. The authors of the paper consider the imprecise answers to management type questions using a numerical scale as fuzzy degrees, and based on the semantic connections among the individual questions, a hierarchical structure is assumed. The paper suggests the use of factor analysis in order to determine this hierarchical structure, and thus the construction of fuzzy signatures from the tree graph representing the connections among the questions and answers, and the values normalized into membership degrees are assigned to the leaves of this tree. An interesting issue is how to determine the aggregations at the intermediate nodes. This may happen based on management science domain expert knowledge, and validated by the obtained results. Kohonen maps are used to demonstrate the clusters emerging among the overall fuzzy degrees representing the Fuzzy Signatures. The evaluation brings some results that partly confirm soft science based assumptions about employee behavior in the literature, and partly bring some interesting novel recognitions that may be brought in feedback to the original management science related problem, where the new method is illustrated.

Open Access: Yes

DOI: 10.3233/JIFS-18548

The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach

Laszlo Vasa Ashutosh Kolte Jewel Kumar Roy

Publication Name: Resources Policy

Publication Date: 2023-01-01

Volume: 80

Issue: Unknown

Page Range: Unknown

Description:

Global stock markets are incredibly unpredictable. Resource prices have a significant market impact on varying securities. With the use of cutting-edge technology like artificial intelligence, analysts and researchers are employing various machine learning techniques and econometrics methodologies to anticipate stock price trends in order to better comprehend stock market volatility. Volatility is the degree of variation in a time sequence of market rates. Stock market equity returns depend on the business output where the investor has trust in high and low equity. This research explores the interaction between industrialized and developing economies' market volatility relationships between 2000 and 2020 as well as the aforementioned impacts taking place on developing financial prudence worldwide. The aim of the study is to integrate an appropriate GARCH framework to estimate the uncertainty dependent on market conditions in the European Union, the Pacific, South America, Latin America, East Asia, West Asia and South Asia stock return indices. The Generalized Auto-Regressive Conditional Heteroscedasticity method is used for analyzing the effect of updates from the USA that influences the returns of S&P 500 globally as well as European Union, Pacific, South American, Latin American, East Asian, West Asian and South Asian indices returns. For capital markets of the world, there is a significant gap in equity return uncertainty. Such results have major effects on investors looking to diversify their portfolios. For international and domestic institutional shareholders, this paper is significant. The impact of international institutional investors' investments and effects of the growth of the equity market return may be omitted as the analysis is restricted exclusively to the European Union, the Pacific, South America, Latin America, East Asia, West Asia, and South Asia.

Open Access: Yes

DOI: 10.1016/j.resourpol.2022.103216

Overall Equipment Effectiveness (OEE) Life Cycle at the Automotive Semi-Automatic Assembly Lines

János Jósvai Péter Dobra

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2022-01-01

Volume: 19

Issue: 9

Page Range: 141-155

Description:

In the automotive industry, manufacturing companies are constantly improving and monitoring their processes with different Key Performance Indicators (KPIs) in order to achieve higher profits. One of the KPIs is the Overall Equipment Effectiveness (OEE), which represents the efficiency of the different machines and assembly lines. High OEE percentage means good performance and quality. Using Manufacturing Execution System (MES) data the OEE contributors such as availability, performance and quality are calculated and followed at the manufacturing area day by day. This paper concentrates on the entire OEE life cycle at the automotive semi-automatic assembly lines. Firstly, a literature review demonstrates scientific relevance. Secondly, the phases of OEE life cycle are revealed and presented regarding a passenger car seat structure production life cycle. Third section points at the connection between OEE percentage and maintenance, labour and quality costs at the assembly lines. In addition to the theoretical approach, real, practical data are also demonstrated based on experiences from the last fifteen years.

Open Access: Yes

DOI: 10.12700/aph.19.9.2022.9.8