http://hdl.handle.net/20.500.14044/33854 Tue, 21 Jul 2026 07:00:08 GMT 2026-07-21T07:00:08Z http://hdl.handle.net/20.500.14044/33728 Investigation of the Impact of Surface Roughness, on a Ship’s Drag (Hull Resistance) Ali, Zainab; Bognár, Gabriella Recently, there has been an increasing focus on maritime transport, as it offers many advantages in terms of storage and transport. As a result, shipping companies need to reduce the fuel consumption of their vessels. These companies have tried to define methods of operation and maintenance in order to reduce greenhouse gas emissions and also to reduce operating costs, thus increasing company profits. One important parameter that directly affects speed, power requirements, and fuel consumption is the hull resistance. Computational Fluid Dynamics (CFD) can be used to calculate the resistance of a rough surface using special wall functions that take into account the effect of roughness on the boundary layer near the hull. These results can be compared with those of a smooth surface. In addition to the effect of surface roughness on hull resistance to pressure, this method also allows the combination of roughness and non-linear effects such as the spatial distribution of contaminants, the movement of the ship in waves, and the effect of thrust on hull resistance. Accordingly, the aim of this research is to determine the effect of surface roughness on the ship resistance for different values of roughness height, boundary layer, and values of velocity, pressure, and kinetic energy fields for the KVLCC2 model hull by CFD using the RANS equations and the k-ω SST model. A numerical study was performed to determine how surface roughness affects the velocity field and kinetic energy. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.14044/33728 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/33726 Human-Machine Co-Working for Socially Sustainable Manufacturing in Industry 4.0 Mareček-Kolibiský, Martin; Janík, Samuel; Mĺkva, Miroslava; Szabó, Peter; Czifra, György Human-machine cooperation is an activity used to maximize job openness for all workers by removing barriers of languages, disability, age, gender barriers and maximizing employee well-being and motivation. The diverse technologies providing physical and cognitive assistance should facilitate attractiveness and facilitate employment, and thus social sustainability within the production section. The main goal of this paper is to analyze the current state of human-machine cooperation and identify the requirements for future human-machine cooperation for socially sustainable manufacturing in Industry 4.0. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.14044/33726 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/33725 Verification of Articulatory Phonetics Features with Quantitative Data Czap, László This paper aims to refine the base data set of visemes – the visual counterparts of phonemes – with quantitative data to provide accurate input for visual speech synthesis (a talking head that supports the training of speech production of deaf and hard-of-hearing children). Measurement-based features extend the existing data and refine our previously used dynamic model of articulation. This requires the definition of two major types of data simultaneously: the shape of the mouth, which can be examined relatively simply in an ordinary camera image, and the position of the tongue, the analysis of which requires the use of medical-level imaging devices and the processing of their signals. Articulatory phonetics can be divided up into three areas to describe consonants. These are voice, place, and manner respectively. This study aims to confirm the description of the place of articulation with measurement data. Data derived from the shape and position of the tongue is suitable for determining the place of articulation of sounds. In the case of vowels, we estimated the tongue position with the centroid of the tongue while in the case of consonants, we define the place of articulation with the measured distance of the tongue from the palate. To measure these, we used MRI and US images and determined tongue contours with an automated process. The results of this analysis statically define data for articulation keyframes for visual speech synthesis. We applied our results to improve the existing Hungarian transparent talking head with a more accurate model based on the clarification of the dynamic features. We also adapted the same model to the Chinese Shaanxi Xi’an dialect. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.14044/33725 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.14044/33724 The Enhancement of the Overall Group Technology Efficacy using Clustering Algorithm for Cell Formation Phung, Lan Xuan; Nguyen, Trung Kien; Truong, Son Hoanh Cellular manufacturing is a principal application of group technology in which machine cells and part families are generated based on their similarity in the production process to minimize overall movement cost and maximize machine utilization by using complex mathematical programming procedures or computer tools with a lot of computational effort and time to solve problems. In this study, the clustering analysis based on a similarity coefficient is developed to efficiently solve cell formation problems in both single and multiple process routings. A novel similarity coefficient is developed to integrate operation sequence especially adjacent operation, processing time, production volume, machine capacity, and multi-visits to minimize the number of actual inter-cell moves and voids in machine cells. An improved clustering algorithm is proposed for grouping machines into cells and simultaneously determining the machine sequence in cells to reduce intra-cell moves as well as selecting the best process routing for each part. The practical effectiveness of the proposed method is demonstrated through computational experiments involving eighteen test instances, varying in scale from small to large problems. When compared to other complex methods, the proposed approach not only enhances overall group technology efficacy but also significantly reduces computational time, making it a highly promising and practical solution for addressing cellular manufacturing challenges. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.14044/33724 2024-01-01T00:00:00Z