Applying neural networks for improving the MEG inverse solution

Magnetoencephalography (MEG) and electroencephalography (EEG) are appealing non-invasive methods for recording brain activity with high temporal resolution. However, locating the brain source currents from recordings picked up by the sensors on the scalp introduces an ill-posed inverse problem. The MEG inverse problem one of the most difficult inverse problems in medical imaging. The current standard in approximating the MEG inverse problem is to use multiple distributed inverse solutions – namely dSPM, sLORETA and L2 MNE – to estimate the source current distribution in the brain. This thesis investigates if these inverse solutions can be "post-processed" by a neural network to provide improved accuracy on source locations. Recently, deep neural networks have been used to approximate other ill-posed inverse medical imaging problems with accuracy comparable to current state-of- the-art inverse reconstruction algorithms. Neural networks are powerful tools for approximating problems with limited prior knowledge or problems that require high levels of abstraction. In this thesis a special case of a deep convolutional network, the U-Net, is applied to approximate the MEG inverse problem using the standard inverse solutions (dSPM, sLORETA and L2 MNE) as inputs. The U-Net is capable of learning non-linear relationships between the inputs and producing predictions about the site of single-dipole activation with higher accuracy than the L2 minimum-norm based inverse solutions with the following resolution metrics: dipole localization error (DLE), spatial dispersion (SD) and overall amplitude (OA). The U-Net model is stable and performs better in aforesaid resolution metrics than the inverse solutions with multi-dipole data previously unseen by the U-Net

W20-pääkokoonpanon kehittäminen

Tämä opinnäytetyö on tehty Wärtsilän W20-toimitusyksikön pääkokoonpanoon.W20-pääkokoonpanon toimintaa on parannettava kasvavista tuotantomääristä johtuen. Opinnäytetyön aiheena on etsiä niitä ratkaisuja, joilla kokoonpanoa voidaan kehittää. Opinnäytetyössä perehdyttiin Lean-toimintamalliin ja tuotannonsuunnittelun teoriaan. Kokoonpanon alkutilanne määritettiin tutkimalla kokoonpanoprosessia, materiaalivirtoja sekä tekemällä arvovirtakuvaus kokoonpanolle. Kokoonpanon suorityskykyä laskettiin simulointimallin avulla. Alkutilanne saatiin selvitettyä riittävällä tasolla, jotta kehityskohteet tulivat esiin. Opinnäytetyössä esitetään ratkaisuja layoutiin, työvaiheistukseen ja materiaalin tuontiin. Opinnäytetyössä saatujen tulosten perusteella ei linjakokoonpanon layout tai toimintamallit olisi enää este tavoitteena olevan tuotantomäärän saavuttamiseen.This thesis was done for Wärtsilä W20 Delivery unit. The procedure of the W20 main assembly needed to be improved due to increasing volume of the production. The topic of the thesis was to find out solutions for the development of the as-sembly. The thesis was made considering lean production and the theory of the production planning. The current situation was specified by examining the process, material flow and by doing the value stream mapping for the assembly. The capacity of the main assembly was calculated with a simulation model. The initial situation was clarified sufficiently to sort out the targets of the devel-opment. The outcome was solutions for layout, phasing and material import. Based on the results of the thesis the target volume of the production is possible to achieve