TY - JOUR A1 - Mandal, Dipak Kumar A1 - Biswas, Nirmalendu A1 - Manna, Nirmal K A1 - Benim, Ali Cemal T1 - Impact of chimney divergence and sloped absorber on energy efficacy of a solar chimney power plant (SCPP) JF - Ain Shams Engineering Journal N2 - A numerical study is carried out meticulously to scrutinize the impact of different shapes of chimneys like circular (outer dia, dc), convergent (outer dia, 0.5dc), divergent (outer dia, 1.5dc), sudden contraction (outer dia, 0.5dc), and sudden expansion (outer dia, 1.5dc) on the performance of an SCPP. Furthermore, the parametric impact with different chimney divergence angles (CDA, ϕ), and ground absorber slope angle (GSA, γ) on the SCPP performance is also scrutinized. Optimum divergence angle (ϕ=+0.75◦) enhances the power generation up to ~ 47% (76 kW) with a horizontal ground absorber surface. An increase or decrease in CDA lessens the power generation. With a sloped ground absorber angle γ=0.6◦, the gain in power generation is 60% (82 kW). The study of combination of ground sloped absorber (γ=0.6◦) and divergent chimney (ϕ=+0.75◦) shows enhancement of the power generation upto 80% (92 kW) more than the classical Manzaranes plant. KW - Solar chimney power plant (SCPP) KW - Chimney divergence angle KW - Sloped absorber surface KW - Power generation KW - Efficiency KW - Regression analysis KW - DEAL KW - HSD Publikationsfonds KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42410 SN - 2090-4495 N1 - Datenverfügbarkeitserklärung: Daten, die die Ergebnisse dieser wissenschaftlichen Publikation stützen, sind auf begründete Anfrage bei dem*der korrespondierenden Autor*in erhältlich. PB - Elsevier ER - TY - JOUR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI²: an interactive tool for data quality assurance JF - AI and Ethics N2 - The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits. KW - Performance metrics KW - HSD Publikationsfonds KW - DEAL KW - DFG Publikationskosten KW - Machine learning KW - Quality assurance KW - Data integrity KW - Data quality Y1 - 2024 U6 - https://doi.org/10.1007/s43681-023-00390-6 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences Data availability declaration: The data that support the findings of this scientific publication are available from the corresponding author upon reasonable request. PB - Springer Nature ER - TY - JOUR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS: an interactive tool for data quality assurance JF - AI and Ethics N2 - With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems. KW - DEAL KW - HSD Publikationsfonds KW - Data visulization KW - Distance based KW - Data quality assurance KW - Equivalence class sets KW - DFG Publikationskosten Y1 - 2024 U6 - https://doi.org/10.1007/s43681-023-00393-3 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences Data availability declaration: All data that support the findings of this scientific publication are available within this paper and/or its supplementary information files/materials. Otherwise: The data that support the findings of this scientific publication are available from the corresponding author upon reasonable request. PB - Springer Nature ER - TY - RPRT A1 - Deckert, Carsten T1 - Good Vibrations from Electronic Circuits: The Technological Development of the Synthesizer T3 - Business Cases in Industrial Engineering - 4 Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-44394 SN - 2941-4075 ER - TY - JOUR A1 - Ruth, Nicolas A1 - Hantschel, Florian A1 - Loepthien, Tim A1 - Randall, Will M. A1 - Steffens, Jochen T1 - The relation between technology acceptance and music listening: An experience sampling study JF - Jahrbuch Musikpsychologie N2 - In the intersection of technology and music listening, understanding user experiences is paramount. This research employed the Experience Sampling Method via the smartphone app MuPsych to continuously capture real-time data on individuals' music listening behaviors and related emotional responses. Over a span of two weeks, participants from Germany were prompted to report on various factors as they engaged in music listening, resulting in a rich dataset. Results indicate that Spotify Premium was the most frequently used music application, with personal playlists being the preferred listening format. To unravel the intricacies of these responses and their determinants, linear mixed-effects model analysis was utilized. Among the critical findings, the Perceived Usefulness and Perceived Ease of Use, two of the central constructs of the Technology Acceptance Model, emerged as significant predictors for the valence and enjoyment of the music experienced by users during the onset of music listening sessions. This highlights the imperative role of user-friendly interfaces in enhancing positive emotional states even before fully engaging with the music, underscoring the need for designers and developers of music-related apps to prioritise usability and useful functions. KW - digital music platforms KW - emotional valence KW - user experience KW - Technology Acceptance Model KW - experience sampling method KW - music listening KW - DOAJ Y1 - 2024 U6 - https://doi.org/10.5964/jbdgm.181 SN - 2569-5665 VL - 32 SP - 1 EP - 10 PB - Leibniz Institute for Psychology (ZPID) ER -