TY  - JOUR
A1  - Mosavi, Amir
A1  - Najafi, Bahman
A1  - Faizollahzadeh Ardabili, Sina
A1  - Shamshirband, Shahaboddin
A1  - Rabczuk, Timon
T1  - An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis
JF  - Energies
N2  - Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.
KW  - Biodiesel
KW  - ANN modeling
KW  - biodiesel
KW  - Artificial Intelligence
KW  - diesel engines
KW  - energy, exergy
KW  - mathematical modeling
KW  - OA-Publikationsfonds2018
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180507-37467
UR  - http://www.mdpi.com/1996-1073/11/4/860
VL  - 2018
IS  - 11, 4
PB  - MDPI
CY  - Basel
ER  - 
TY  - THES
A1  - Moosbrugger, Jennifer
T1  - Design Intelligence - Human-Centered-Design for the development of industrial AI/ML agents
N2  - This study deals with design for AI/ML systems, more precisely in the industrial AI context based on case studies from the factory automation field. It therefore touches on core concepts from Human-Centered-Design (HCD), User Experience (UX) and Human Computer Interaction (HCI) on one hand, as well as concepts from Artificial Intelligence (AI), Machine Learning (ML) and the impact of technology on the other. The case studies the research is based on are within the industrial AI domain. However, the final outcomes, the findings, solutions, artifacts and so forth, should be transferable to a wider spectrum of domains. The study’s aim is to examine the role of designers in the age of AI and the factors which are relevant, based on the hypothesis that current AI/ML development lacks the human perspective, which means that there are pitfalls and challenges that design can help resolve. The initial literature review revealed that AI/ML are perceived as a new design material that calls for a new design paradigm. Additional research based on qualitative case study research was conducted to gain an overview of the relevant issues and challenges. From this, 17 themes emerged, which together with explorative expert interviews and a structured literature review, were further analyzed to produce the relevant HCD, UX and HCI themes. It became clear that designers need new processes, methods, and tools in the age of AI/ML in combination with not only design, but also data science and business expertise, which is why the proposed solution in this PhD features process modules for design, data science and business collaboration. There are seven process modules and their related activities and dependencies that serve as guidelines for practitioners who want to design intelligence. A unified framework for collecting use case exemplars was created, based on a workshop with different practitioners and researchers from the area of AI/ML to support and enrich the process modules with concrete projects examples.
KW  - Künstliche Intelligenz
KW  - Benutzererlebnis
KW  - Human-centered Design
KW  - Datenkompetenz
KW  - Prozessmodell
KW  - AI, computational thinking
KW  - Design, UX, Human-Centered-Design
KW  - process, tools, methods
KW  - collaboration
KW  - Artificial Intelligence
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230719-64098
ER  -