Titlebook: Machine Learning for Cyber Physical Systems; Selected papers from Jürgen Beyerer,Alexander Maier,Oliver Niggemann Conference proceedings‘‘‘

古老

Improvement of the prediction quality of electrical load profiles with artificial neural networks,ofiles play an important role. On this basis, it is possible to plan and implement the use of controllable energy generation and storage systems as well as energy procurement with the required lead-time, taking into account the technical and contractual boundary conditions..The recorded electrical l

seruting

Incisor

Deployment architecture for the local delivery of ML-Models to the industrial shop floor,als for increasing productivity and machine utilization. However, the systematic engineering approach to integrate and manage these machine-learned components is still not standardized and no reference architecture exist. In this paper we will present the building block of such an architecture which

產(chǎn)生

ironic

Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis,cts this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although

冬眠

Proposal for requirements on industrial AI solutions,ances, plant owners strive for stability and robustness of the production process. To overcome this tension field, we propose a set of 16 requirements for the development of industrial AI solutions to foster i) the adaptation process, ii) support the solution engineering and iii) ease the embedding

NIP

Information modeling and knowledge extraction for machine learning applications in industrial produion of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and obser

Lumbar-Spine

ciliary-body

Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment mprove production throughput with a minimum of investment. Identifying these opportunities often requires the observation of the current production process by experts. This paper is the continuation of the previous work ’Automated, Nomenclature Based Data Point Selection for Industrial Event Log Gen

冬眠

Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks,eural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cybe