Open Access

This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 %), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 %), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.

Aircraft ad hoc networks simplify airplane-to-airplane or airplane-to-service station communication. It evolved from MANET and VANET ad-hoc networks. MANET connects mobile networks and VANET for cars. Adhoc networks are popular because they can be built without routers or access points when no network exists. Flights are self-organizing nodes in AANET. This dynamic network requires only two nodes and no specific infrastructure. This method is important for GPS navigation, aircraft–ship communications, and navy signaling. These Ad Hoc networks let aircraft interact with the grounds and service stations to decrease traffic between aircraft. It can also connect with a network radar aircraft to avoid collisions. Planes ad-hoc networks connect planes spontaneously. It is versatile and simple. The aircraft adapts to the situation to make connections; thus, routers or networks are unnecessary. Planes’ fast mobility accelerates network evolution. No infrastructure is needed for aircraft to transmit and relay data. AANET optimizes performance despite restricted bandwidth, power, and processing. This paper describes the AANET network and the recommended solutions to improve airplane performance. It also covers existing methodologies, benefits and cons, and various scholars’ work on the AANET.

We report on measurements of the anisotropy of velocities and attenuation of GHz-frequency acoustic phonons in a cubic MgO crystal at room temperature. They are used to quantify the strong anisotropy of the Grüneisen parameter and calculate the attenuation anisotropy for Rayleigh surface acoustic waves. These observations constitute important building blocks for better understanding of ultrafast laser-based magneto-acoustic and nonlinear acoustic experiments at ultrahigh frequencies.

Dieser Beitrag beleuchtet die wichtige Rolle, die Strategieentwicklung im Allgemeinen und im Speziellen für Industriebetriebe spielt. Zunächst wird die Historie der Strategieentwicklung aufgezeigt und die Herausforderungen, denen insbesondere Industriebetriebe gegenüberstehen, erläutert. Im Anschluss wird erörtert, wie eine Strategieentwicklung aufgebaut ist und methodisch sauber durchgeführt wird. Abschließend fasst der Beitrag die wichtigsten Schritte in Form einer strukturierten Grafik zusammen und erläutert die Bedeutung der Strategieentwicklung für Industriebetriebe.