On the Diagnosis of Cyber-Physical Production Systems
Authors: Oliver Niggemann, Volker Lohweg
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In another project using a plant from process industry, the anomaly detection with the learned Timed Automata has been evaluated on data of a real production plant and compared with the results of the models learned by neural networks and decision tree learning. The results are given in the confusion matrix (according to (Tan, Steinbach, and Kumar 2005)) in table 1. |
| Researcher Affiliation | Academia | Oliver Niggemann and Volker Lohweg in IT Institute for Industrial IT, University of Applied Sciences OWL, 32657 Lemgo, Germany email: {oliver.niggemann, volker.lohweg}@hs-owl.de |
| Pseudocode | No | The paper includes figures illustrating processes and learned models, but it does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described. |
| Open Datasets | No | The paper refers to datasets like 'data of a real production plant' and 'Historical data from a WPS' but does not provide specific access information (links, DOIs, or formal citations) for these datasets to be publicly available or open. |
| Dataset Splits | No | The paper states, 'The training data contains 10% of the OK-data, the remaining 90% are used as evaluation data,' indicating a training and evaluation/test split, but it does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'neural networks and decision trees' and 'principal component analysis (PCA)' for modeling, but it does not provide specific software names with version numbers (e.g., libraries, frameworks, or solvers) used in the experiments. |
| Experiment Setup | No | The paper discusses algorithms and their application but does not provide specific experimental setup details such as hyperparameter values, model initialization, or specific training schedules. |