Henrik Håkansson

Chalmers University of Technology

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Emil Gustavsson

Fraunhofer Chalmers Research Centre for Industrial Mathematics

MÖ

Magnus Önnheim

Fraunhofer Chalmers Research Centre for Industrial Mathematics

Mats Jirstrand

Chalmers University of Technology

AÅ

Adam Åkerman

Helsingborgs lasarett

Marleen Huysman

Vrije Universiteit Amsterdam

Vendela Sjöberg

Niklas Samuelsson

A. von Raesfeld-Meijer

M. C. Toribio

Chalmers University of Technology

Hussein Hamoodi

Cooperative Institutions

Chalmers University of Technology

Fraunhofer Chalmers Research Centre for Industrial Mathematics

Leiden University

Netherlands Institute for Radio Astronomy

SRON Netherlands Institute for Space Research

University of Gothenburg

National Observatory

Swedish Institute of Space Physics

Helsingborgs lasarett

Leiden Observatory

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Capacity assessment of a production system using simulation

Henrik Håkansson Niklas Samuelsson

Syftet med examensarbetet ar att ge Ostspecialisten en tydligare bild av vilken kapacitet foretaget besitter pa en av sina produktionslinor samt over eventuella kapacitetsokningar som kravs i framt ...

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Through the woods to find the forest

Henrik Håkansson

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Klassiﬁcering av neuropati baserat på svettmönster

Johan Broberg Hussein Hamoodi Henrik Håkansson Jonathan R. Kerr

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Daily management of a Service Organization – in change towards excellence.

Henrik Håkansson Adam Åkerman

Excellence

Change Management

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Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning

Henrik Håkansson

Autoencoder

Human multitasking

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Grön marknadsföring : En kvalitativ studie om hållbarhet och grön marknadsföring inom livsmedelsbranschen

Alexander Ekelund Henrik Håkansson Jonathan Ståhl

Titel: Gron marknadsforing – En kvalitativ studie om hallbarhet och gronmarknadsforing inom livsmedelsbranschen.Syfte: Syftet med uppsatsen ar att undersoka hur ledande foretag inomlivsmedelsbransc ...

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Lärares tankar kring elevinflytande på Hantverksprogrammets frisörinriktning

Henrik Håkansson

Syftet med uppsatsen ar att undersoka hur larare pa Hantverksprogrammets Frisorinriktning uppfattar och arbetar med elevinflytande for att skapa mening i undervisningen. Arbetet begransades och pre ...

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Organizational interactions and organizational teaching

Henrik Håkansson Marleen Huysman A. von Raesfeld-Meijer

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Supply Chain Inventory Control-Methods for Coordination and Evaluation

Henrik Håkansson Adam Åkerman

Background As a part of the Service Organization’s earlier initiated and at the moment on-going project; “A more efficient Service Organization”, daily management has been identified as playing a major part. The starting-point for this has been to gain insight into how the Service Organization is actually performing and how it would be possible to measure, follow-up and visualize this performance. To attain a higher performance, the focus has been on utilizing the philosophy based on Lean Production. By attaining better insight, arrangements and improvements to become more efficient can easily be done. Problem definition Key Performance Indicators (KPIs) are measurements that in a true and continuous way indicate how a process is performing and supports continual improvements. The question is what to measure, how to measure it and how to make use of and visualize it. Therefore this thesis presents one case study on how KPIs could be incorporated in daily work. Purpose The purpose of this master thesis is to apply the philosophy of Lean Production to a service environment and suggest ways of following up as well as visualizing the daily work conducted. Method This master thesis has been conducted with a systems approach and the data has been gathered qualitatively. The information has been gathered through literature studies and interviews as well as observations during our time spent at the Service Organization. Project conclusions The employees at the Service Organization are heading towards an exciting future. There are large opportunities for improvement, and follow-up of the daily work is perfectly possible. Improvements to create prerequisites for daily management by identifying customer needs, shifting from being reactive to proactive and standardizations are suggested in different contexts. Furthermore an improved feedback and measurement system containing external as well as internal feedback is proposed, along with a general model for establishing a culture of continuous improvements.

Performance indicator

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Utilization of convolutional neural networks for H I source finding

Astronomy and Astrophysics (2023)

Henrik Håkansson Anders Sjöberg M. C. Toribio Magnus Önnheim M. Olberg

Context. The future deployment of the Square Kilometer Array (SKA) will lead to a massive influx of astronomical data and the automatic detection and characterization of sources will therefore prove crucial in utilizing its full potential. Aims. We examine how existing astronomical knowledge and tools can be utilized in a machine learning-based pipeline to find 3D spectral line sources. Methods. We present a source-finding pipeline designed to detect 21-cm emission from galaxies that provides the second-best submission of SKA Science Data Challenge 2. The first pipeline step was galaxy segmentation, which consisted of a convolutional neural network (CNN) that took an H I cube as input and output a binary mask to separate galaxy and background voxels. The CNN was trained to output a target mask algorithmically constructed from the underlying source catalog of the simulation. For each source in the catalog, its listed properties were used to mask the voxels in its neighborhood that capture plausible signal distributions of the galaxy. To make the training more efficient, regions containing galaxies were oversampled compared to the background regions. In the subsequent source characterization step, the final source catalog was generated by the merging and dilation modules of the existing source-finding software S O F I A, and some complementary calculations, with the CNN-generated mask as input. To cope with the large size of H I cubes while also allowing for deployment on various computational resources, the pipeline was implemented with flexible and configurable memory usage. Results. We show that once the segmentation CNN has been trained, the performance can be fine-tuned by adjusting the parameters involved in producing the catalog from the mask. Using different sets of parameter values offers a trade-off between completeness and reliability.

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10.1051/0004-6361/202245139

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