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Course of studies
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.
Unter dem Motto „Technologie bewegt Pflege“ werden Beiträge aus Wissenschaft, Praxis und Industrie präsentiert. In einem abwechslungsreichen Programm werden aus verschiedenen Blickwinkeln die unterschiedlichen Schwerpunkte im Themenfeld Pflege und Technik diskutiert. Die Beiträge auf der 5. Clusterkonferenz, ausgerichtet vom Pflegepraxiszentrum Freiburg, sind im vorliegenden Abstractband ausgeführt.
Despite the unabated growth of algorithmic decision-making in organizations, there is a growing consensus that numerous situations will continue to require humans in the loop. However, the blending of a formal machine and bounded human rationality also amplifies the risk of what is known as local rationality. Therefore, it is crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically. In this paper, we propose a simulation-based model to address the current lack of research on quantifying algorithmic interventions in a broader organizational context. Our approach allows the combining of causal modeling and data science algorithms to represent decision settings involving a mix of machine and human rationality to measure performance. As a testbed, we consider the case of a fictitious company trying to improve its forecasting process with the help of a machine learning approach. The example demonstrates that a myopic assessment obscures problems that only a broader framing reveals. It highlights the value of a systems view since the effects of the interplay between human and algorithmic decisions can be largely unintuitive. Such a simulation-based approach can be an effective tool in efforts to delineate roles for humans and algorithms in hybrid contexts.
The YOLO series of object detection algorithms, including YOLOv4 and YOLOv5, have shown superior performance in various medical diagnostic tasks, surpassing human ability in some cases. However, their black-box nature has limited their adoption in medical applications that require trust and explainability of model decisions. To address this issue, visual explanations for AI models, known as visual XAI, have been proposed in the form of heatmaps that highlight regions in the input that contributed most to a particular decision. Gradient-based approaches, such as Grad-CAM, and non-gradient-based approaches, such as Eigen-CAM, are applicable to YOLO models and do not require new layer implementation. This paper evaluates the performance of Grad-CAM and Eigen-CAM on the VinDrCXR Chest X-ray Abnormalities Detection dataset and discusses the limitations of these methods for explaining model decisions to data scientists.
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.
Acid Sphingomyelinase Promotes Endothelial Stress Response in Systemic Inflammation and Sepsis
(2016)
Adding evidence of the effects of treatments into relevant Wikipedia pages: a randomised trial
(2020)
Additives and polymer composition influence the interaction of microplastics with xenobiotics
(2021)
Age-related changes in trunk muscle activity and spinal and lower limb kinematics during gait
(2018)
Akademisch qualifizierte Pflegefachkräfte sind prädestiniert dafür, erweiterte Rollen, Aufgaben und Spezialisierungen im Versorgungsgeschehen zu übernehmen. Ziel einer solchen Neuausrichtung in der Pflege ist es, Patienten*innen, Bewohner*innen in Pflegeeinrichtungen und Bürger*innen eine medizinisch-pflegerische Versorgung auf qualitativ hohem Niveau zukommen zu lassen. Dazu gehört auch die Sicherung und Verbesserung der Versorgungsqualität. Die erweiterte und fortgeschrittene Pflegepraxis durch akademisch qualifizierte Pflegefachkräfte in spezifischen Versorgungsbereichen bewährt sich seit vielen Jahren z.B. in den angelsächsischen oder skandinavischen Ländern und wird auch in Deutschland bereits an bestimmten Standorten umgesetzt. Dennoch sind zukünftige Handlungsfelder, Aufgaben- und Verantwortungsbereiche von akademisch ausgebildeten Pflegefachkräften unzureichend erprobt und strukturell beschrieben.
Kriterien zukünftiger Aufgaben- und Verantwortungsbereiche akademisch ausgebildeter Pflegefachpersonen in der ambulanten, stationären Langzeitpflege und im Quartier sowie die Ableitung vertraglicher, leistungs- und ordnungsrechtlicher Rahmenbedingungen und Empfehlungen für deren Einsatz sind herausgearbeitet wurden. Konkrete Handlungsempfehlungen für den Einsatz von akademisch ausgebildeten Pflegefachkräften und für die dazu erforderlichen Rahmenbedingungen sind beschrieben und formuliert worden, um so das Verständnis einer erweiterten Pflegepraxis zu fördern.
Analysing attention convolutional neural network for surgical tool localisation: a feasibility study
(2022)
Assessing the impact of a structural prior mask on EIT images with different thorax excursion models
(2023)
Assessment of Neck Muscle Shear Modulus Normalization in Women with and without Chronic Neck Pain
(2022)
While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.
Biosynthesis of iron oxide magnetic nanoparticles using clinically isolated Pseudomonas aeruginosa
(2021)
The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more emphasis is needed on trust management, which presents significant challenges in ensuring security and trust in industrial devices and applications. To address these issues, industrial blockchain has the potential to make use of trustless and transparent technologies for devices, applications, and systems. By using a distributed ledger, blockchains can track devices and their data exchanges, improving relationships between trading partners, and proving the supply chain. In this paper, we propose a model for cross-domain authentication between the blockchain-based infrastructure and industrial centralized networks outside the blockchain to ensure secure communication in industrial environments. Our model enables cross authentication for different sub-networks with different protocols or authentication methods while maintaining the transparency provided by the blockchain. The core concept is to build a bridge of trust that enables secure communication between different domains in the IIoT ecosystem. Our proposed model enables devices and applications in different domains to establish secure and trusted communication channels through the use of blockchain technology, providing an efficient and secure way to exchange data within the IIoT ecosystem. Our study presents a decentralized cross-domain authentication mechanism for field devices, which includes enhancements to the standard authentication system. To validate the feasibility of our approach, we developed a prototype and assessed its performance in a real-world industrial scenario. By improving the security and efficiency in industrial settings, this mechanism has the potential to inspire this important area.