This paper is intended to give an overview of the experiments to evaluate the cognitive load of the officer on watch (OOW) during a collision avoidance maneuver in a full-mission simulator. The main goal is to investigate the possibilities of recording the biometric parameters of an OOW during a simulated collision avoidance maneuver. Potentially dangerous navigation errors known as human erroneous action (HEA) are induced by excessive cognitive load. Despite modern navigational aids on the ship’s bridge, investigators of maritime incidents typically link the reason for incidents at sea with human factors, including high cognitive load. During the experimental tasks on the bridge, the biometric parameters of the OOW are recorded. Statistical tools are used to visualize the data and evaluate the cognitive load of the OOW. Biometric peaks of the OOW typically occur either during the collision avoidance maneuver or when the OOW has been exposed to disturbing factors that increase reaction time and cause potentially dangerous navigation. Assessing the cognitive load of OOWs in the simulator is challenging for several reasons: e.g., the environmental conditions of the simulator, the type of task to be simulated, and even the type of sensor used. After careful study of the available literature, an original experimental design using non-invasive biometric sensors is proposed.

There has been an increasing trend of integrating photovoltaic power plants (PVs). One of the important challenges for distribution system operators is to evaluate the total installed power of a PV that a particular network can host (or PV hosting capacity) while keeping voltage and element constraints within required limits. The major drawback of the existing methods for calculating PV hosting capacity is that they use the same installed power of the PV systems for all simulated PVs, as these methods do not use external data sources about building roofs. As a consequence, this has a significant impact on the final accuracy of the results. This paper presents a probabilistic methodology for calculating the PV hosting capacity in low voltage (LV) networks. The main contribution of this paper is the improved modeling of PV generation using actual building roof data when calculating the PV hosting capacity, as every building is treated according to its actual solar potential. Monte Carlo simulations with incorporated stochastic consumption and PV generation models are utilized for load flow calculations of the actual LV network. The simulation results presented in this paper prove that the proposed methodology increases the accuracy of the final PV hosting capacity calculations.

Efficient workspace awareness is critical for improved interaction in cooperative and collaborative robotic applications. In addition to safety and control aspects, quality-related tasks such as the monitoring of manual activities and the final quality assessment of the results are also required. In this context, a visual quality and safety monitoring system is developed and evaluated. The system integrates close-up observation of manual activities and posture monitoring. A compact single-camera stereo vision system and a time-of-flight depth camera are used to minimize the interference of the sensors with the operator and the workplace. Data processing is based on a deep learning to detect classes related to quality and safety aspects. The operation of the system is evaluated while monitoring a human-robot manual assembly task. The results show that the system ensures a high level of safety, provides reliable visual feedback to the operator on errors in the assembly process, and inspects the finished assembly with a low critical error rate.