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Pattern Recognition with Neuromorphic Sensor System
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2018 (English)Report (Other academic)
Abstract [en]

Biological sensor systems are remarkably robust and power efficient systems that solve complex pattern recognition problems. Neuromorphic engineering concerns the design of very-large-scale-integration (VLSI) systems with power-efficient analog circuits that mimic biological sensors and neural systems. In this work, we examine how dynamical models of spiking neural networks (SNNs) and the low-power neuromorphic processor Dynap-se, developed at iniLabs in Zurich, can be used in a pattern-recognition application. We implement and investigate a training protocol for signal classification with an SNN-model incorporating the same neuron- and synapse models as those implemented in Dynap-se. We use the model to classify sampled vibration signals generated in healthy- and faulty states of a wind turbine. We investigate two different methods for conversion of an analog signal to spikes, a software delta-modulator and a neuromorphic sensor system known as the Dynamic Audio Sensor (DAS) from iniLabs. The SNN-based classifier is tested on 10 pairs of healthy- and faulty signals not included in the training set. We achieve 90% classification test accuracy using the delta-modulator. The SNN-based classifier is trained on a rather small dataset. Larger training and test sets are needed to increase the performance and reliability of the results. For the delta-modulator stimuli, the model needs to be further evaluated using for instance cross-validation. For the DAS-stimuli, the classifier is not functioning well in its current state. Possibly, this can be improved by modifying the network architecture. A prototype training protocol for Monte Carlo-based synaptic configuration of Dynap-se is developed using a hardware-in-the-loop approach. The protocol enables optimization of a given cost function, and thus has potential to be further developed for the optimization of neural networks implemented in Dynap-se.

Place, publisher, year, edition, pages
Luleå, 2018. , p. 29
Keywords [en]
Pattern recognition, Neuromorphic engineering, Condition monitoring, DYNAP
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-67370OAI: oai:DiVA.org:ltu-67370DiVA, id: diva2:1176836
Note

Handledare: Fredrik Sandin

Available from: 2018-01-23 Created: 2018-01-23 Last updated: 2018-04-25Bibliographically approved

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