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Entwurf und Implementierung einer kamerabasierten Anwendung für ein Mühlespiel für den Panda-Roboter
(2023)
Injury to the anterior and posterior cruciate ligaments is not an uncommon occurrence. A rapid movement can already cause such an injury. Reconstruction of the anterior and posterior cruciate ligaments (ACL/PCL) in the knee joint is a complex orthopedic challenge. In this context, the development of a graft preparation system is gaining importance. Such a system will help surgeons prepare grafts accurately and efficiently to optimize reconstruction processes. This can increase the accuracy of graft placement, reduce surgical times, and decrease recovery time and postoperative risk for patients.
This paper focuses on the development of a graft preparation system for ACL/PCL reconstruction in collaboration with Getsch+Hiller Medizintechnik. The challenges and requirements in developing such a system were analyzed to ensure functionality and effectiveness. Existing approaches and technologies were examined to identify potential improvements and innovations. Development included extensive research on ACL/PCL reconstruction using grafts, including various surgical techniques. A detailed analysis of the graft preparation process was performed to identify critical issues. The design of the graft preparation system is based on research data and is performed using CAD. The needs of the target group, customer requirements and findings from research were considered.
Introduction: The present study investigated the role of training intensity in the dose–response relationship between endurance training and cardiorespiratory fitness (CRF). The hypothesis was that beginners would benefit from an increase in training intensity after an initial training phase, even if the energy expenditure was not altered. For this purpose, 26 weeks of continuous moderate training (control group, CON) was compared to training with gradually increasing intensity (intervention group, INC) but constant energy expenditure.
Methods: Thirty-one healthy, untrained subjects (13 men, 18 women; 46±8 years; body mass index 25.4 ± 3.3 kg m−2; maximum oxygen uptake, VO2max −1 −1 34 ± 4 ml min kg ) trained for 10 weeks with moderate intensity [3 days/week for 50 min/session at 55% heart rate reserve (HRreserve)] before allocation to one of two groups. A minimization technique was used to ensure homogeneous groups. While group CON continued with moderate intensity for 16 weeks, the INC group trained at 70% HRreserve for 8weeks and thereafter participated in a 4 × 4 training program (high-intensity interval training, HIIT) for 8 weeks. Constant energy expenditure was ensured by indirect calorimetry and corresponding adjustment of the training volume. Treadmill tests were performed at baseline and after 10, 18, and 26 weeks.
Results: The INC group showed improved VO2max (3.4 ± 2.7 ml kg−1 min−1) to a significantly greater degree than the CON group (0.4 ± 2.9 ml kg−1 min−1) (P = 0.020). In addition, the INC group exhibited improved Vmax (1.7 ± 0.7 km h−1) to a significantly greater degree than the CON group (1.0 ± 0.5 km h−1) (P = 0.001). The reduction of resting HR was significantly larger in the INC group (7±4bpm) than in the CON group (2±6bpm) (P=0.001). The mean heart rate in the submaximal exercise test was reduced significantly in the CON group (5±6bpm; P=0.007) and in the INC group (8±7bpm; P=0.001), without a significant interaction between group and time point.
Data processed in context is more meaningful, easier to understand and has higher information content, hence it derives its semantic meaning from the surrounding context. Even in the field of acoustic signal processing. In this work, a Deep Learning based approach using Ensemble Neural Networks to integrate context into a learning system is presented. For this purpose, different use cases are considered and the method is demonstrated using acoustic signal processing of machine sound data for valves, pumps and slide rails. Mel-spectrograms are used to train convolutional neural networks in order to analyse acoustic data using image processing techniques.
Separation of ventilation and cardiac activity on recorded voltages before EIT image reconstruction
(2023)
For many practitioners, considering sustainability during a software development project is a challenge. The Sustainability Awareness Framework (SusAF) is a tool for thinking through short, medium-and long-term impacts of socio-technical systems on its surrounding environment. While SusAF has been used by several companies, is not widely adopted in industry yet. In this Vision Paper, we discuss the options for extending the reach of SusAF and what it would take to evolve SusAF into a (de-facto) standard
Digital transformation is now reaching into topics like End-of-life Care, Funeral Culture, and Coping with Grief. Those developments are inevitably accompanied by the growing challenge to design IT systems that are appropriate and helpful for the stakeholders involved. Our aim in this paper is to further introduce the rather new combined research field of Socioinformatics and Thanatology (the scientific study of death and dying) and to present it with the first results on which requirements to consider for the design of digital tools within ‘Thanatopractice’. By using Participatory Design and the Sustainability Awareness Framework (SusAF) in the context of three workshops on socio-technical systems (Online Pastoral Care, Virtual Graveyards, and AI Memory Avatars), we want to sensitize software practitioners to the multidimensional impacts of their products and services in a field, which the participants in the workshops often described as “highly sensitive”.
In this paper, we derive set constraints for a reduced order model and augment them into a model predictive control (MPC) scheme to ensure safe operation of the large-scale ensemble system. For the control feedback, only the aggregated information of the whole system is required. For the constraint satisfaction, we consider an adaptive tube formulation to characterize the deviation between the reduced order model and the ensemble system. Employing the robust control invariant set, we ensure recursive feasibility and initial feasibility under an easily verifiable condition.
3D Computer Vision for the Industrial Metaverse - On the potentials of Neural Radiance Fields
(2023)
The industrial metaverse refers to the use of virtual reality (VR) and augmented reality (AR) technologies in the context of industry and manufacturing. It is envisioned as a shared, immersive digital space where people can interact with and manipulate virtual representations of physical objects and processes. The industrial metaverse has the potential to transform the way products are designed, manufactured, and maintained,
enabling new levels of collaboration, automation, and innovation.
It further includes virtual representations of humans, also known as avatars. These avatars can be used to enable remote collaboration and communication between people in the virtual space. In this way, the industrial metaverse can facilitate virtual meetings, trainings, and other interactive experiences that involve human participants.
Neural Radiance Fields (NeRFs) are a powerful tool for synthesizing photorealistic images of 3D objects, including virtual representations of humans known as avatars. In this talk, we will discuss the potential applications of NeRFs in generating high-fidelity objects and avatars for use in the industrial metaverse.
Influence of Reconstruction Algorithms on Harmonic Analysis in Electrical Impedance Tomography
(2023)
The absolute value of recruitment-to-inflation ratio does not correlate with the recruited volume
(2023)
Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches
(2023)
Existing literature (Erling & Hingeldorf, 2006; Earls, 2014) indicates that there is a lack of formal policies at the macro- or meso-level governing the use of English in German higher education. This has led to a situation in which higher education institutions (HEIs) are required to formulate and implement their own policies and guidelines regarding English-medium instruction (EMI). As a growing number of HEIs adopt EMI (Wächter & Maiworm, 2014; Macaro et al., 2018) without access to policy guidelines, there is an urgent need to scrutinize the policy formulation and implementation processes at the institutional level. Such investigation is crucial to understand the complexities that come with tailoring EMI to unique institutional contexts, objectives, and stakeholder needs. We believe that this will enable more effective and equitable implementations, while also providing insights that could inform future policy recommendations. In this article, we analyze the motivations for drafting a language policy at a medium-sized German university of applied sciences1 (UAS) and investigate the attitudes and opinions towards EMI of three stakeholder groups: faculty members, administrative staff, and the student body. We were especially interested in exploring the rationales for implementing Bilingual Degree Programs (BDPs), as a variant of EMI, and how each stakeholder group influenced the formulation and implementation of the policy. To get an initial overview, we read institutional policy documents outlining the proposed language policy. We then complemented the documentary analysis by conducting a survey investigating the attitudes and opinions of the stakeholder groups using a questionnaire format (n=207). Finally, to gain deeper insights and triangulate data from the questionnaire, we conducted semi-structured interviews (n=18). Analysis of the data indicates that the primary motivation for implementing BDPs is to attract greater numbers of international as well as domestic applicants to make up for an ongoing decline in student numbers. We also discovered that stakeholder groups hold different beliefs about BDPs, impacting their level of support for their implementation. We argue this is due to some groups within the institution being more influential in policy formulation, leading to feelings of disempowerment in individuals tasked with implementing BDPs, but not being consulted in the policy formulation process. Finally, it also seems that the institutional policy is driven by experience in implementation, resulting in policy enhancement over time. We assume this approach is a direct outcome of the lack of policy guidelines and consider the issues that arise from such an approach and share implications of the current practice.
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
Assessing the impact of a structural prior mask on EIT images with different thorax excursion models
(2023)
Multifrequency bioimpedance device based on the Analog Discovery 2: performance and characterization
(2023)
Microstructure of Selective Laser Melted 316L under Non-Equilibrium Solidification Conditions
(2023)
Feasibility of Parylene C for encapsulating piezoelectric actuators in active medical implants
(2023)
Parylene C is well-known as an encapsulation material for medical implants. Within the approach of miniaturization and automatization of a bone distractor, piezoelectric actuators were encapsulated with Parylene C. The stretchability of the polymer was investigated with respect to the encapsulation functionality of piezoelectric chips. We determined a linear yield strain of 1% of approximately 12-μm-thick Parylene C foil. Parylene C encapsulation withstands the mechanical stress of a minimum of 5×105 duty cycles by continuous actuation. The experiments demonstrate that elongation of the encapsulation on piezoelectric actuators and thus the elongation of Parylene C up to 0.8 mm are feasible.