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Publications - 6374

Energy distribution modeling of car body deformation using LPV representations and fuzzy reasoning

István A. Harmati András Rövid Peter Varlaki Lászlõ Szeidl

Publication Name: Wseas Transactions on Systems

Publication Date: 2008-12-01

Volume: 7

Issue: 11

Page Range: 1228-1237

Description:

The main aim of the paper is to introduce a novel method - based on fuzzy control and linear parameter varying (LPV) system representation transformable into HOSVD based Canonical form - for modeling deformation processes with respect to the distribution of the absorbed kinetic energy. Modeling such kind of processes requires many uncertain input parameters. Using the proposed concept we are able to handle them and keep the complexity of the models low by using higher order singular value decomposition (HOSVD) technique.

Open Access: Yes

DOI: DOI not available

Predictive hybrid scan-to-BIM method improves heritage building documentation completeness and accuracy

Kitti Karolyfi Nóra Géczy Rnin Salah

Publication Name: Scientific Reports

Publication Date: 2026-12-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

Incomplete survey data often undermines the reliability of Building Information Models (BIM), particularly for structures with restricted access and complex geometries. This study demonstrates a hybrid Scan-to-BIM workflow that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, supported by a predictive feasibility concept, to improve documentation accuracy and completeness. A two-phase strategy was validated on a chapel case study. Phase 1, combining TLS and ground-based photogrammetry, achieved only 54% coverage due to severe occlusions and limited scanner placement. These results led to the formulation of a Predictive Scan Feasibility Estimation Model (PSFEM), designed to generalize site-specific parameters such as scanner range, clearance angle, and building height into a decision-support tool for future surveys. Guided by the recognition of Phase 1 limitations, Phase 2 incorporated UAV photogrammetry and supplemental TLS, increasing coverage to 96%. Comparative analyses confirmed consistency in accuracy and improved geometric completeness. While the PSFEM was developed retrospectively based on the limitations identified in Phase 1, its analytical validation demonstrates the potential value of predictive planning for reducing redundant site visits and enhancing BIM reliability. The proposed framework provides a transferable basis for applying predictive hybrid workflows in both heritage and complex building documentation. This workflow offers a practical and scalable method for Scan-to-BIM documentation, applicable to heritage as well as other complex buildings, enabling high accuracy and completeness while effectively managing time and resources.

Open Access: Yes

DOI: 10.1038/s41598-026-38200-8

Robot environment representation based on Quadtree organization of Fuzzy Signatures

Áron Ballagi Csaba Hajdu Ahmet Mehmet Karadeniz Ĺaszĺo T. Ḱoczy

Publication Name: Saci 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2021-05-19

Volume: Unknown

Issue: Unknown

Page Range: 509-514

Description:

This paper presents a novel approach to mobile robot environment representation to hold information on detected obstacles. The method is inspired by fuzzy signature-based formalism and is based on classical quadtrees as a data indexing structure. Each detected feature point is evaluated by a fuzzy-ruleset defining the presumed significance of each detected object. Feature points and their fuzzy-mapping are indexed in a classical quadtree-based fashion. During the reconstruction of the environment representation, inference is done by the traversal on the constructed tree using accumulated fuzzy-ruleset. Our goal is to use this representation format for further robotic tasks such as obstacle avoidance in a distributed computational environment.

Open Access: Yes

DOI: 10.1109/SACI51354.2021.9465566

Analysis of a numerical method developed for estimation of the heat transfer coefficient obtained during quenching

Gustavo Sanchez Sarmiento Augusto Moita De Deus B. Smoljan Imre Felde Tamás Réti

Publication Name: Proceedings of the 17th Ifhtse Congress

Publication Date: 2008-01-01

Volume: 2

Issue: Unknown

Page Range: 816-819

Description:

A numerical method for prediction of the Heat Transfer Coefficient (HTC) obtained during quenching is described in this paper. An iterative regularization algorithm is used to solve the inverse problem under study. The unknown HTC function is approximated by polynomial functions of surface temperature. The numerical method developed is verified by using the temperature data measured with a JIS silver probe.

Open Access: Yes

DOI: DOI not available

Body Conformation Scoring of Cattle, using Machine Learning

István Szabó Bence Tarr János Tőzsér

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2025-01-01

Volume: 22

Issue: 3

Page Range: 27-38

Description:

Precision agriculture brings new artificial intelligence techniques closer to everyday farming. Agriculture historical data is easily available, so using this data to teach a machine-learning model, offers various opportunities to enhance farming efficiency. In our study, we develop a machine learning model to estimate some linear traits of Limousin sires (sore for muscularity, length of the rump, muscularity of breast and muscularity of the width of rump), based on a phenotypic score, using artificial intelligence, in Hungary. Phenotypic scores are usually given by the experts in field. Before scoring, many measurements are made on the animals, which takes time and places a high stress on the cattle. A hands-on prediction application can make the whole process faster, and more comparable, regardless of the expert who created the scoring. We found that after collecting sufficient data from previous observations it is possible to train specifically selected artificial intelligence (AI) algorithms to predict linear traits in Limousin breeding bulls. Machine learning (ML) was used to predict the score values for muscularity, length of the rump, muscularity of the breast and muscularity of the width of the rump for this study. We found no similar experiments for the usage of AI algorithms to predict these variables. The coefficient of determination (R2) of the algorithm, in this study, provided the following range values: (R2=0.77 to 0.86).

Open Access: Yes

DOI: 10.12700/APH.22.3.2025.3.2

Determination of Measurement Parameters for Vibration Analysis by Bus

Péter Szalai Péter Horváth F. Hajdu Rajmund Kuti M. Brinissat Antal Apagyi Cs Papp

Publication Name: Strojnicky Casopis Journal of Mechanical Engineering

Publication Date: 2022-11-01

Volume: 72

Issue: 2

Page Range: 189-200

Description:

From the point of view of field examinations, especially in the case of measurements done by instruments, it is crucially important to determine measurement conditions. Accurate identification of parameters enables accurate and certified measurement results to be achieved. In our work, we represent the determination of measurement conditions for harmful vibrations occurring during urban road transport of an Ikarus 55 type long-haul bus. This is a favourable basis for further vibration analysis regarding other buses.

Open Access: Yes

DOI: 10.2478/scjme-2022-0028

Serratia fajok jellemzése, valamint Serratia marcescens kvalitatív kimutatása nyers és pasztőrözött tejből polimeráz láncreakción alapuló vizsgálati módszerrel

E. Korcz Z. Kerényi László Varga

Publication Name: Elelmiszervizsgalati Kozlemenyek

Publication Date: 2021-01-01

Volume: 67

Issue: 2

Page Range: 3441-3452

Description:

No description provided

Open Access: Yes

DOI: 10.52091/EVIK-2021/2-4-HUN

Measuring and simulating magnetic characteristics using Epstein frame

Miklós Kuczmann Zoltan Nemeth

Publication Name: Pollack Periodica

Publication Date: 2018-08-01

Volume: 13

Issue: 2

Page Range: 15-26

Description:

The paper discusses the standard of the Epstein frame that has been used to measure magnetic characteristics of the core made of material M250-35A supplied by different frequencies between 1-400 Hz. The measuring program has been built in LabVIEW including a control, filter and data save section as well. COMSOL Multiphysics 4.3b has been chosen as simulation environment, in which the Jiles-Atherton hysteresis model has been implemented.

Open Access: Yes

DOI: 10.1556/606.2018.13.2.2

Investigation of the performance of direct forecasting strategy using machine learning in State-of-Charge prediction of Li-ion batteries exposed to dynamic loads

Adrienn Dineva Bence Csomós Szabolcs Kocsis Szurke Istvan Vajda

Publication Name: Journal of Energy Storage

Publication Date: 2021-04-01

Volume: 36

Issue: Unknown

Page Range: Unknown

Description:

On account of intense technological advances regarding Electric Vehicles, the state evaluation and prediction issues of Li-ion cells have become increasingly important for ensuring the competitiveness in terms of feasible performance and range. Albeit the wide investigation of various standard modelling and estimation techniques, only limited researches focus on their precision and applicability under heavy transient working conditions. This paper is concerned with Li-ion battery terminal voltage and State-of-Charge (SoC) prediction for two types of dynamic loads. Attention is focused on the investigation of the applicability of direct multi-step forecasting strategy in combination with Machine Learning. Beside that, a feature bank is composed of discharge profiles obtained at different C-rates. The set of discharge curves is proposed to complement the feature extraction, i.e. the additional historical data is considered for model building. Special care is devoted for the design of appropriate training data. Hence, a battery cell model is built for simulating intensive dynamic load scenarios in addition to the experimental setup. The cell model is validated by using measurement data. Results have demonstrated, that in case of WLTP-type discharge load of 0.3C-rate the forecasting performance is highly efficient on measurement data. Under dynamic loads of 1C-rate, or when small historical data is available, the application of feature bank improves the performance. We have obtained comprehensive results proving that the application of direct multi-step forecasting strategy using XGBoost represents a viable alternative to capture real-time the cell dynamics and predict the terminal voltage and SoC of Li-ion batteries exposed to dynamic loads.

Open Access: Yes

DOI: 10.1016/j.est.2021.102351