2022
,
Veerappa, Manjunatha
,
Anneken, Mathias
,
Burkart, Nadia
,
Huber, Marco
Due to the lack of explanation towards their internal mechanism, state-of-the-art deep learning-based classifiers are often considered as black-box models. For instance, in the maritime domain, models that classify the types of ships based on their trajectories and other features perform well, but give no further explanation for their predictions. To gain the trust of human operators responsible for critical decisions, the reason behind the classification is crucial. In this paper, we introduce explainable artificial intelligence (XAI) approaches to the task of classification of ship types. This supports decision-making by providing explanations in terms of the features contributing the most towards the prediction, along with their corresponding time intervals. In the case of the LIME explainer, we adapt the time-slice mapping technique (LimeforTime), while for Shapley additive explanations (SHAP) and path integrated gradient (PIG), we represent the relevance of each input variable to generate a heatmap as an explanation. In order to validate the XAI results, the existing perturbation and sequence analyses for classifiers of univariate time series data is employed for testing and evaluating the XAI explanations on multivariate time series. Furthermore, we introduce a novel evaluation technique to assess the quality of explanations yielded by the chosen XAI method.
2016
,
Monostori, László
,
Kádár, Botond
,
Bauernhansl, Thomas
,
Kondoh, Shinsuke
,
Kumara, Soundar R.
,
Reinhart, Gunther
,
Sauer, Olaf
,
Schuh, Günther
,
Sihn, Wilfried
,
Ueda, Kanji
One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet. Cyber-physical production systems (CPPS), relying on the latest, and the foreseeable further developments of computer science, information and communication technologies on one hand, and of manufacturing science and technology, on the other, may lead to the 4th industrial revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots in general - and in particular to the CIRP community - which point towards CPPS. Expectations towards research in and implementation of CPS and CPPS are outlined and some case studies are introduced. Related new R&D challenges are highlighted.