Entangled histories/touching tales - eine transdisziplinäre Annäherung

Tagungsbericht: Veranstalter: Exzellenzcluster "Die Herausbildung normativer Ordnungen", Goethe-Universität Frankfurt am Main. Datum, Ort: 17.02.2010, Frankfurt am Mai

Automating Virtualization of Machinery for enabling efficient Virtual Engineering Methods

Virtual engineering as a new working method in product development should make it much easier to validate the development progress and facilitate team communication. Work steps are brought forward and start with the virtual components instead of real ones. To validate mechanical and electrical CAD as well as programming, automated virtualization systems should create the virtual twin of the machine at the push of a button. For this purpose, generic intelligence is added to enable complex interactive virtual models that can be used for training, monitoring and many other applications. Advanced applications are for example training and support applications, especially in combination with augmented reality and remote collaboration. We propose a system that combines virtual reality, virtual engineering and artificial intelligence methods for the product development process. Geometry analysis algorithms are used to process mechanical CAD data and thus, for example, to automatically parameterize kinematic simulations. In combination with electrical CAD data and the simulations of electric circuits as well as the original machine program allow simulating the behavior of the machine and the user interaction with it. This article will describe the virtualization method in detail and present various use-cases in special machine construction. It will also propose a novel method to use causal discovery in complex machine simulations

Rethinking cluster-conditioned diffusion models

We present a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments. We elucidate how individual components regarding image clustering impact image synthesis across three datasets. By combining recent advancements from image clustering and diffusion models, we show that, given the optimal cluster granularity with respect to image synthesis (visual groups), cluster-conditioning can achieve state-of-the-art FID (i.e. 1.67, 2.17 on CIFAR10 and CIFAR100 respectively), while attaining a strong training sample efficiency. Finally, we propose a novel method to derive an upper cluster bound that reduces the search space of the visual groups using solely feature-based clustering. Unlike existing approaches, we find no significant connection between clustering and cluster-conditional image generation. The code and cluster assignments will be released

Contrastive Language-Image Pretrained (CLIP) Models are Powerful Out-of-Distribution Detectors

We present a comprehensive experimental study on pretrained feature extractors for visual out-of-distribution (OOD) detection. We examine several setups, based on the availability of labels or image captions and using different combinations of in- and out-distributions. Intriguingly, we find that (i) contrastive language-image pretrained models achieve state-of-the-art unsupervised out-of-distribution performance using nearest neighbors feature similarity as the OOD detection score, (ii) supervised state-of-the-art OOD detection performance can be obtained without in-distribution fine-tuning, (iii) even top-performing billion-scale vision transformers trained with natural language supervision fail at detecting adversarially manipulated OOD images. Finally, we argue whether new benchmarks for visual anomaly detection are needed based on our experiments. Using the largest publicly available vision transformer, we achieve state-of-the-art performance across all $18$ reported OOD benchmarks, including an AUROC of 87.6\% (9.2\% gain, unsupervised) and 97.4\% (1.2\% gain, supervised) for the challenging task of CIFAR100 $\rightarrow$ CIFAR10 OOD detection. The code will be open-sourced

Exploring the Limits of Deep Image Clustering using Pretrained Models

We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clustering heads, based on the fact that nearest neighbors in the pretrained feature space are likely to share the same label. We propose a novel objective to learn associations between images by introducing a variant of pointwise mutual information together with instance weighting. We demonstrate that the proposed objective is able to attenuate the effect of false positive pairs while efficiently exploiting the structure in the pretrained feature space. As a result, we improve the clustering accuracy over $k$-means on $17$ different pretrained models by $6.1$\% and $12.2$\% on ImageNet and CIFAR100, respectively. Finally, using self-supervised pretrained vision transformers we push the clustering accuracy on ImageNet to $61.6$\%. The code will be open-sourced

Human-Machine-Interaction in Innovative Work Environment 4.0 – A Human-Centered Approach

The working environment is constantly changing and companies face the challenge of adapting to new and constantly changing customer requirements. Employees are faced with the challenge of identifying and learning new, helpful technologies and using them in order to achieve efficiency gains and increase productivity. This article addresses the three technologies Artificial Intelligence, Robotic Process Automation and Virtual Reality, which will play an important role in the future of work and will influence the Work Environment 4.0. Artificial Intelligence and Robotic Process Automation relieve employees of repetitive and manual tasks which thus accelerate and simplify business processes. Virtual Reality offers employees new opportunities to collaborate in virtual environments. Instead of performing routine tasks, employees will increasingly promote the use of such technologies in future and orchestrate their application. In addition, it is important for employees to continuously look for new use cases within their own organization and to collaborate with external partners. The article aims to describe the opportunities that arise from the application of the technologies and to explain their effects on the Work Environment 4.0 and the employee

Virtual Engineering: Hands‐on Integration of Product Lifecycle Management, Computer‐Aided Design, eXtended Reality, and Artificial Intelligence in Engineering Education

Engineering education at the Institute for Information Management in Engineering integrates product lifecycle manage- ment (PLM), computer-aided design (CAD), eXtended reality (XR), and artificial intelligence (AI) to enhance learning and prepare students for modern challenges. Our interdisciplinary approach, emphasizing digital twins and virtual twins, fosters immersive, hands-on experiences. This paper reviews our strategies, comparing them with global initiatives, highlighting the transformative impact of our curriculum on preparing future engineers for complex industrial environments

Using Collaborative Immersive Environments and Building Information Modeling Technology for Holistic Planning of Production Lines

Large and complex building projects need many different experts from different engineering disciplines for different matters. But these experts each use their own IT tools that produce a lot of heterogeneous data. This leads to a strong fragmentation of competencies, what causes problems for interdisciplinary collaboration, because the data might be inconsistent, redundant or there are no interfaces to combine the data. These problems in collaboration increase the risk of planning mistakes that might significantly impair the overall project success. So only one database should be used for all engineering tasks to improve the transdisciplinary collaboration. The Building Information Modelling (BIM) working methodology enables the digital collaboration of virtual production planning and architecture tasks for developing a building. By means of lean optimization in combination with early integration of future-oriented production facilities, process-relevant production data can be included in the planning phase before construction begins. This article presents a real time immersive 3D virtualization system using the digital twin of complex buildings with a modern production line as the single source of truth and creates a consistent integrated data model, that enables transdisciplinary collaboration of all involved engineering disciplines. In this way, a continuous comparison can be made between the real construction project and its digital twin in an interactive, intuitive and collaborative manner. The same model is also used by production planners to optimize the material flow and in general the value chain of a production line through a holistic planning, which brings many benefits for all stakeholders