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Pulmonary response prediction through personalized basis functions in a virtual patient model
(2024)
Effiziente Erosion von superabrasiven Werkzeugen : Schwerpunkt auf Schleif- und Abrichtwerkzeugen
(2024)
In this paper, the influence of current sensors of a NILM system is investigated. The current sensors of a classical inductive current transformer and a Rogowski coil are compared. To evaluate the actual influence on the NILM, measurements are performed with two measuring systems with different current sensors. With these measuring systems, 20 different consumers with 50 switch-on and switch-off cycles are measured in parallel. Besides, the influence of the sampling rate on the results of the NILM classification is evaluated. The classification is carried out with features normalized to the performance and without phase information, so only the signal waveform is used to differentiate the devices.
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.
Modelability of processes is a recognized and important characteristic of any modeling language. Nevertheless, it is not always purposeful or easy to create process models for every kind of workflow. This article discusses the opportunities and limitations of modeling agile development projects with SCRUM as an example. For this purpose, a BPMN and an S-BPM model for SCRUM are presented. The discussion along recognized rules for good process models shows that both notations provide possible and accurate insights into the process of SCRUM on the one hand. On the other hand, the models raise questions of necessity, added value, and relevance in practice. Practitioners can use the developed models to technically implement agile project management, while researchers benefit from a discourse on opportunities and limitations of modeling agility.
On Consistency Viability and Admissibility in Constrained Ensemble and Hierarchical Control Systems
(2023)
Several control architectures, such as decentralized, distributed, and hierarchical control, have been elaborated over the past decades for controlling systems composed of a set of subsystems. However, computational complexity and constraint satisfaction are still challenging tasks. We present an approach to control an ensemble of similar heterogeneous systems with input and state constraints via an identical control input. This control input is globally admissible and computed based on an aggregated system that reflects the overall behavior of the ensemble. To limit the computational complexity of the control task, the aggregated system is designed such that its dimension is independent of the number of subsystems. To guarantee viability, i.e., state constraint satisfaction for all times, appropriate consistency conditions are derived based on invariant set theory. The presented approach is illustrated with a numerical example.
Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI 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.
Qualitative Analyseverfahren
(2023)
Design and fabrication of a novel 4D-printed customized hand orthosis to treat cerebral palsy
(2024)
Michael Burawoy: „For Public Sociology“ als Referenzdokument der Debatte um öffentliche Soziologie
(2023)
Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions; some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450–900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments.