TY  - CHAP
A1  - Harlacher, Markus
A1  - Altepost, Andrea
A1  - Elsen, Ingo
A1  - Ferrein, Alexander
A1  - Hansen-Ampah, Adjan
A1  - Merx, Wolfgang
A1  - Niehues, Sina
A1  - Schiffer, Stefan
A1  - Shahinfar, Fatemeh Nasim
ED  - Lausberg, Isabel
ED  - Vogelsang, Michael
T1  - Approach for the identification of requirements on the design of AI-supported work systems (in problem-based projects)
T2  - AI in Business and Economics
N2  - To successfully develop and introduce concrete artificial intelligence (AI) solutions in operational practice, a comprehensive process model is being tested in the WIRKsam joint project. It is based on a methodical approach that integrates human, technical and organisational aspects and involves employees in the process. The chapter focuses on the procedure for identifying requirements for a work system that is implementing AI in problem-driven projects and for selecting appropriate AI methods. This means that the use case has already been narrowed down at the beginning of the project and must be completely defined in the following. Initially, the existing preliminary work is presented. Based on this, an overview of all procedural steps and methods is given. All methods are presented in detail and good practice approaches are shown. Finally, a reflection of the developed procedure based on the application in nine companies is given.
KW  - Business understanding
KW  - Requirements
KW  - Process model
KW  - Participation
KW  - Im-plementation of AI-systems
Y1  - 2024
SN  - 9783110790320
U6  - https://doi.org/10.1515/9783110790320
SP  - 87
EP  - 99
PB  - De Gruyter
CY  - Berlin
ER  - 
TY  - CHAP
A1  - Scholl, Ingrid
A1  - Bartella, Alexander K.
A1  - Moluluo, Cem
A1  - Ertural, Berat
A1  - Laing, Frederic
A1  - Suder, Sebastian
T1  - MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality
T2  - Bildverarbeitung für die Medizin 2019 : Algorithmen – Systeme – Anwendungen
Y1  - 2019
SN  - 978-3-658-25326-4
U6  - https://doi.org/10.1007/978-3-658-25326-4_32
SP  - 152
EP  - 157
PB  - Springer Vieweg
CY  - Wiesbaden
ER  - 
TY  - CHAP
A1  - Ferrein, Alexander
A1  - Nikolovski, Gjorgji
A1  - Limpert, Nicolas
A1  - Reke, Michael
A1  - Schiffer, Stefan
A1  - Scholl, Ingrid
ED  - Küçük, Serdar
T1  - Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation
T2  - Multi-Robot Systems - New Advances
N2  - In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map.
Y1  - 2023
SN  - 978-1-83768-290-4
U6  - https://doi.org/10.5772/intechopen.113044
PB  - Intech Open
CY  - London
ER  -