Related Issues (20)
- [3pt,3pt] Integrate statistics GNU Radio block into flowgraph and visualize data
- [2pt,2pt] Integrate integration GNU Radio block into flowgraph and visualize data
- Implement functionality to load and run trained model in TensorFlow format in inference tool
- Implement interface between dashboard and inference tool
- Implement imPlot dashbaord / visualizations for NILM quantities
- Produce a complete set of training data with all required appliances
- [5pt,6pt] Set-up of the local infrastructure including acquisition of required hardware HOT 2
- [7pt,2pt] Generic interface to load the training data (either database- or file-system-based) HOT 1
- [13pt,12pt] Implementation of a model-independent training loop including monitoring and validation of the training progress HOT 1
- [20pt,42pt] Implementation, training and model validation using the approach from Sidhant Gupta HOT 3
- [10pt,6pt] Implementation of a machine learning system using ONNX Runtime and consisting of an ‘off-line tool’ and an ‘online micro- service’
- [10pt,12pt] Near real-time visualisation of the micro-service’s metrics using web-based ImPlot HOT 3
- [25pt] Development of post-processing blocks using GNU-Radio (GR) to extract additional features required by the AI
- Copy imPlot Dashboard from call #2
- Change layout and include visualization for state vector (from NILM model)
- Implement a OpenCMW Worker to send and receive state vectors
- [5pt,5pt] Integration of OpenCMW Sinks in GR-Flowgraph HOT 1
- Implement function to concatenate P,Q,S, Phi and spectra (U, I, S) from GR to one data point for model inference
- [5pt,5pt] Implement function to integrate current power per appliance per (day, week, month) HOT 2
- Implementation of a function to serialize processed data on disk
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