Applications of NF AI
Artificial intelligence - more than just image recognition.
Artificial intelligence is often equated with image recognition. However, you can expect much more from NF AI. NeuroForge's experts deal with interesting and state-of-the-art solutions for problems in a wide variety of application areas on a daily basis. Often, concepts from one area are transferable to other areas, expanding knowledge and expertise in a wide variety of use cases. Below you will find some of these use cases outlined.
Data type: Image
The most popular area of artificial intelligence is very diverse. NeuroForge established a technologically diverse position in this field at an early stage and is already achieving great success here with the application of state-of-the-art methods.
Classification of images into learned classes. Exemplary here is the NOK/OK classification of components in quality assurance.
Image Segmentation and Regions
Partitioning of images into different, learned regions. As an example, the determination of the quality of surfaces.
Detection of alignment and deformation of complex objects. Finds use in pose correction.
Generate plausible training data to train robust artificial intelligences even with small data sets.
Data type: Video
Videos are a sequence of images and can usually be analyzed using the same technologies. In addition to the individual images, videos also contain additional information about the temporal context. A clear gain in information over simple image analysis.
Video processing in production is subject to strict real-time requirements so that errors can be detected and corrected at an early stage in ongoing production.
Adaptive learning methods enable targeted predictions based on learned patterns and behaviors. Optimizations can thus be integrated early and effectively.
Data type: Audio
The recording of audio data offers the manufacturing industry a fast and reliable way to check and monitor the condition of machines and components. Similar to the processing of video data, the temporal relationships of audio tracks can also be included in the analysis.
Artificial intelligence allows, for example, the formalization and automation of laborious manual steps such as the extraction of useful signals from vibration data.
Self-learning methods allow systems to automatically detect deviations. Anomaly detection in oscillations, such as recorded power consumption, form the core of predictive maintenance systems.
NOK/OK classification is also possible on recorded audio signals. As an example, the procedure of structure-borne sound analysis can be considered.
Particularly in Industry 4.0 and the accompanying digitalization, massive amounts of data accumulate in tabular, structured form. The information from company databases or Excel tables can be explored with the help of artificial intelligence.
Explorative approaches allow previously unrecognized connections to be revealed and unused potential to be discovered.
Through learned predictive models, artificial intelligences can provide suggestions to optimize workflows.
Important data can be automatically distinguished from unimportant data. In this way, Big Data can be reduced to the relevant information.
With process data such as temperature, pressure or humidity, possible sources of error can be identified at an early stage and proactively eliminated.