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Elevators contribute significantly to the electricity consumption of residential buildings, office buildings and commercial enterprises. In this paper, the electricity consumption is investigated using an elevator system and its individual operating states as example. In addition to analyzing and allocating the energy demand, this work examines how the individual operating states can be determined solely on the basis of the power consumption of the elevator. The knowledge gained from this, such as the usage behavior, the travel profile, or load, is determined independently of the elevator control system. A subsequent installation on any system can be easily realized. In this work, the investigated elevator requires a substantial part of the total annual power consumption in standby (>90 %). This shows an enormous potential for energy savings. The individual elevator states, as well as the load, can be detected very well on the basis of the measured total power consumption. The work thus shows exemplary the potential of an intelligent measurement system for the state detection of elevator systems.
In several domains of product design - like medical device design -
risk related use scenarios have to be analyzed in an early stage of design process.
Virtual reviews with users make it possible to get early insights in use problems.
Also, situations that are difficult to imitate in reality can be modeled and simulated
in virtual reality without risking the health of user. Therefore, virtual usability
tests are a promising approach which allow testing different scenarios under
controlled conditions. We chose a sample scenario from medical device design
and compare and evaluate different technical systems which can be used for virtual
usability tests. Aim is to derive guidance for virtual usability tests including
systems that can be used for specific conditions and the qualitative and quantitative
data, which can be collected with these systems. A formative test is performed
to evaluate and compare different systems. Also, a summative test is performed
to evaluate the selected systems. Results show that virtual usability tests
make it possible to test scenarios with users in an early stage and thus to encounter
possible interaction problems. But there are also many additional and new
things to consider compared to normal usability tests, such as checking motion
sickness, maintaining presence and the extensive operation of the technology.
Finally, a proposed method for virtual usability testing is described which also
comprises our equipment recommendations for virtual usability tests.
Evolutionary strategy is increasingly used for optimization in various machine learning problems. It can scale very well, even to high dimensional problems, and its ability to globally self optimize in flexible ways provides new and exciting opportunities when combined with more recent machine learning methods. This paper describes a novel approach for the optimization of models with a data driven evolutionary strategy. The optimization can directly be applied as a preprocessing step and is therefore independent of the machine learning algorithm used. The experimental analysis of six different use cases show that, on average, better results are attained than without evolutionary strategy. Furthermore it is shown, that the best individual models are also achieved with the help of evolutionary strategy. The six different use cases were of different complexity which reinforces the idea that the approach is universal and not depending on specific use cases.
Precedent research in human resources management has put forward the challenges of recognizing the appropriate methods to unlock employees’ potential. However, it has been proven that leaders and managers can establish a culture where employees can unlock their potential with the proper understanding of neuroscience and emotional intelligence. This research has shown that cognitive skills, which are necessary for a good job performance, can be further developed due to neuroplasticity. Furthermore, understanding human needs and emotions can help people improve their work and life experience. This research also studies the relationship between employees’ well-being and job performance. Moreover, how emotional intelligence influences the company’s working atmosphere with its staff will be, with the support of literature research, thoroughly investigated and further analysed. Based on findings of various studies, attributes such as empathy, compassion, and kindness play a predominant role and indicate an underlying connection between motivation and job performance. All in all, questions such as how health impacts job performance and how neuroscience research and understanding human emotions shift the approach to motivating people will be further discussed. This research also questions what attributes a company leader should have to build trust with the employees and encourage them to work better. Therefore, this research takes a closer look into the exemplary leadership approach and explains why communication and purpose in the company matter when unlocking human potential. In addition, it highlights the effect of experiencing stress and fear at work and the remarkable impact of positive emotions such as kindness, gratitude, and compassion. Thus, with this understanding, this research acknowledges profoundly the positive influence of having a trustworthy and empathetic leader in the company. It fundamentally shows that working in a healthy and inclusive working culture, where psychological safety is ensured, one can, in fact, unlock the potential of employees to work better and feel good about themselves.