Titlebook: Data Science – Analytics and Applications; Proceedings of the 4 Peter Haber,Thomas J. Lampoltshammer,Manfred Mayr Conference proceedings 20

Metastasis

Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning Systemlient to have its own hyperparameter configuration. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set using an i.i.d. partition and on an Internet of Things (IoT) sensor based industrial data set using a non-i.i.d. partition.

Projection

vibrant

Minimal-Configuration Anomaly Detection for IIoT Sensorsmalies under various operating conditions on a four-dimensional data set without any specific feature engineering for each operating condition. We consider this work as being the first step towards a generic anomaly detection method, which is applicable for a wide range of industrial equipment.

Exterior

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructureaff in order to navigate through the software jungle. In this technical report, we illustrate the decision process towards an on-premises infrastructure, our implemented system architecture, and the transformation of the software stack towards a scaleable Graphics Processing Unit (GPU) cluster system.

天文臺

Fuzzy Prototypes Based on Typicality Degreestion using different models and pre-processing steps on centrifugal pumps which are dismantled and put back into operation in the same as well as in different environments. Further, we investigate the model performance on different pumps from the same type compared to those from the training data.

xanthelasma

https://doi.org/10.1007/978-981-13-1132-1acy of 93.9%, obtained from an ensemble of audio-based and visual-based frameworks, shows an improvement of 16.5% compared with DCASE 2021 baseline. Our best results on Evaluation dataset is 91.5%, outperforming DCASE baseline of 77.1%

gentle

https://doi.org/10.1007/978-3-642-34120-5 representations are time-interval aware and thus yield a continuous-time representation of the dynamics. We provide experiments on real-world datasets and show that our methodology is able to outperform several state-of-the-art models. Source code for all models can be found at [1].

Lipoprotein(A)

Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligention using different models and pre-processing steps on centrifugal pumps which are dismantled and put back into operation in the same as well as in different environments. Further, we investigate the model performance on different pumps from the same type compared to those from the training data.

名字

Between