@phdthesis{Grum2021,
  author    = {Grum, Marcus},
  title     = {Construction of a concept of neuronal modeling},
  year      = {2021},
  abstract  = {The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.},
  language  = {en}
}
@article{GautamZhangLandwehretal.2021,
  author    = {Gautam, Khem Raj and Zhang, Guoqiang and Landwehr, Niels and Adolphs, Julian},
  title     = {Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building},
  series = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  volume    = {187},
  journal   = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0168-1699},
  doi       = {10.1016/j.compag.2021.106259},
  pages     = {10},
  year      = {2021},
  abstract  = {In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87\% of time, on average, by 0.53 degrees C compared to the current deviations.},
  language  = {en}
}