Human-machine-interaction in the industry 4.0 era
By Dominic Gorecky, Mathias Schmitt, Matthias Loskyll, Detlef Zühlke. International Conference on Industrial Informatics, 2014
594 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/9104dbe11c234b4c0c86c6e4102d8e6ccdccccf0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Giuseppe Aceto
- Valerio Persico
- Antonio Pescapè
- Jiafu Wan
- Athanasios V. Vasilakos
- Joaquín B. Ordieres Meré
- Meiqing Wang
- Xiaochen Zheng
In Computer Vision and Pattern Recognition
- Athanasios V. Vasilakos
- Joaquín B. Ordieres Meré
- Meiqing Wang
- Valerio Persico
- Antonio Pescapè
- Xiaochen Zheng
- Giuseppe Aceto
- Jiafu Wan
In Industrial and Manufacturing Engineering
- Jiafu Wan
- Valerio Persico
- Shiyong Wang
- Beata Mrugalska
- Antonio Pescapè
- Di Li
- Giuseppe Aceto
- Magdalena K. Wyrwicka
Citations in the corpus
- A review of industrial wireless networks in the context of Industry 4.0 (2015, background): “0 is to connect and integrate traditional industries, particularly manufacturing, to realize flexibility, adaptability, and efficiency and increase effective communication between producers and consumers [34, 77].”
- Big data for cyber physical systems in industry 4.0: a survey (2019, unknown): “…system logs (Qun et al. 2015), camera images (Weyer et al. 2015), radio-frequency identi fi cation (RFID) records (Albrecht et al. 2015), GPS data (Gorecky et al. 2014), Enterprise resource planning (ERP) data (Wollschlaeger, Sauter, and Jasperneite 2017), and social media data (Jiang, Ding,…”
- Industry 4.0: Towards future industrial opportunities and challenges (2015, background): “As shown in Figure 3, smart mobile terminal applications in industry[10], can easily control and monitor intelligent processes in factories [11].”
- Towards Lean Production in Industry 4.0 (2017, background): “He is provided the support by the mobile, context-sensitive user interfaces and user-focused assistance systems [29].”
- Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, background): “Instead, people should be integrated into the cyber-physical structure in such a way that their individual skills and talents can be fully realized [6,7].”
- Literature review of Industry 4.0 and related technologies (2018, unknown): “Gorecky et al. (2014) studied machine interactions and Industry 4.0 era.”
- A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, background): “Therefore, there is a need for ergonomically designed user interfaces, allowing for maximum user productivity, user acceptance, and user satisfaction [157].”