Control of Heating Chamber on Packaging Machine A1 TFA

On the packaging machine Tetra Pak A1 a heating box is used to sterilize the packaging material. This is done by passing the material through a bath of hydrogen peroxide and then vaporizing the liquid in the heating box. The heating is done by three resistive elements that pass their energy by convection and radiation to the packaging material. Problems occur due to temperature variations on the web of the packaging material. During normal production the temperature variations are small and acceptable. At starts and stops, overshoots cause material damages and undershoot results in peroxide residues forcing disposal of packages. This master's thesis proposes how to optimize the control of the temperature. The control will be divided into three stages; start, running and stop. Different conditions during these stages will force the control logic into three different implementations

Availability Estimations for Utilities in the Process Industry

An important performance measure of a plant is the plant-availability. The higher availability the better, since a high availability implies a possibility for a large production volume and thereby an increased profit for the company. One way of increasing the plant-availability is by eliminating, or minimizing the effect of disturbances. The cause of a disturbance can be personnel, material or equipment, where material includes both raw materials and utilities. The aim of this work is to increase the plant-availability by decreasing the effects of plant-wide disturbances caused by utilities. The first step is to determine the set of utilities that can be present at an industrial site, what disturbances these utilities can suffer, and how frequent and safety-critical these disturbances are. A later step will be to determine the effects on the plant-availability, and ways to decrease or eliminate these effects

A General Method for Handling Disturbances on Utilities in the Process Industry

Utilities, such as steam or cooling water, have shown to play an important role within the process industry, since a malfunctioning utility is a plant-wide disturbance that can lead to large revenue losses due to reduced production quantities. This work focuses on identifying disturbances on utilities that give economical consequences. Measures of utility availability and area availability are introduced and used for estimating the ratio of disturbances on utilities. A generic method for handling disturbances on utilities is presented, which could be applied using site models of different level of detail. Some modeling approaches for modeling a site are described and the framework of the general method is demonstrated with a case study example at Perstorp AB, Sweden

Reducing revenue loss due to disturbances in utilities using buffer tanks - A case study at Perstorp

Utilities, such as steam and cooling water, are often shared by several production areas at an industrial site. In order to minimize the loss of revenue due to disturbances in the supply of utilities, the optimal supply of utilities to different areas has to be determined. It is not evident how utility resources should be divided, as both buffer tank levels, the connections between areas, and the profitability of different products must be considered. This paper presents a case study at Perstorp, the objectives of which were to identify the utilities causing the greatest revenue losses at the site, and suggest strategies for reducing this loss using an on/off modeling approach including buffer tanks between areas

Geometric deep learning and equivariant neural networks

We survey the mathematical foundations of geometric deep learning, focusing on group equivariant and gauge equivariant neural networks. We develop gauge equivariant convolutional neural networks on arbitrary manifolds M using principal bundles with structure group K and equivariant maps between sections of associated vector bundles. We also discuss group equivariant neural networks for homogeneous spaces M= G/ K , which are instead equivariant with respect to the global symmetry G on M . Group equivariant layers can be interpreted as intertwiners between induced representations of G, and we show their relation to gauge equivariant convolutional layers. We analyze several applications of this formalism, including semantic segmentation and object detection networks. We also discuss the case of spherical networks in great detail, corresponding to the case M= S2= SO (3) / SO (2) . Here we emphasize the use of Fourier analysis involving Wigner matrices, spherical harmonics and Clebsch–Gordan coefficients for G= SO (3) , illustrating the power of representation theory for deep learning

HEAL-SWIN: A Vision Transformer On The Sphere

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, resulting in a one-dimensional representation of the spherical data with minimal computational overhead. We demonstrate the superior performance of our model for semantic segmentation and depth regression tasks on both synthetic and real automotive datasets. Our code is available at https://github.com/JanEGerken/HEAL-SWIN.Comment: Main body: 10 pages, 7 figures. Appendices: 4 pages, 2 figure

Neurala Faltningsnät för Segmentering av Mammogram

Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and the development of tools used by radiologists during examination. Over the years many approaches to this problem have been presented. A surge in the popularity of new methods to image processing involving deep neural networks present new possibilities in this domain, and this thesis evaluates mammogram segmentation as an application of a specialized neural network architecture, U-net. Results are produced on publicly available datasets mini-MIAS and CBIS-DDSM. Using these two datasets together with mammograms from Hologic and FUJI, instances of U-net are trained and evaluated within and across the different datasets. A total of 10 experiments are conducted using 4 different models. Averaged over classes Pectoral, Breast and Background the best Dice scores are: 0.987 for Hologic, 0.978 for FUJI, 0.967 for mini-MIAS and 0.971 for CBIS-DDSM

Neurala Faltningsnät för Segmentering av Mammogram

Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and the development of tools used by radiologists during examination. Over the years many approaches to this problem have been presented. A surge in the popularity of new methods to image processing involving deep neural networks present new possibilities in this domain, and this thesis evaluates mammogram segmentation as an application of a specialized neural network architecture, U-net. Results are produced on publicly available datasets mini-MIAS and CBIS-DDSM. Using these two datasets together with mammograms from Hologic and FUJI, instances of U-net are trained and evaluated within and across the different datasets. A total of 10 experiments are conducted using 4 different models. Averaged over classes Pectoral, Breast and Background the best Dice scores are: 0.987 for Hologic, 0.978 for FUJI, 0.967 for mini-MIAS and 0.971 for CBIS-DDSM

Open Source Software Licenses Impact on Businesses

Open source licensing has a significant impact on the choice of business model for companies in the software industry. Permissive open source licenses provide greater flexibility in commercial use and distribution, while restrictive licenses limit revenue generation potential. Understanding the nuances of different open source licenses and complying with licensing requirements is crucial for companies seeking to navigate the complex world of open source licensing and maximize the benefits of open source software.Companies that can successfully navigate the complex world of open source licensing and business model choice can achieve competitive advantage and long-term success in the software industry.  The objective of this thesis is to examine the impact of open source licenses on businesses and delve into how they can shape the choice of utilizing open source software, as well as their implications for business operations. We conduct a literature study and complement it with an empirical study to provide a more complete understanding of the subject. The empirical study enables us to fill in the gaps in our research and compare and validate our findings. Our findings demonstrates the significant impact that licenses can have on a business, highlighting the importance of understanding them for those utilizing open source software. While our results show that the use of open source software does not necessarily limit monetization, there can be restrictions on how products can be monetized. We discovered that the multi-licensing model, which combines an open source license with a proprietary license, can be a viable option for navigating these restrictions

LOW COST ULTRA WIDEBANDRADAR FOR HUMAN PROTECTION

The majority of the UWB radars available on the market today are expensive and often closed forfurther development due to proprietary rights. Therefore it is difficult to fully understand and adaptthe functionality of an available UWB system to fit one’s needs. The consulting-firm Addiva purchasedan UWB radar to be used in a safety system. However, the radar had limitations and the functionalityof it was partly unknown. This master thesis was inspired from this issue to examine the possibilitiesof developing a low-cost UWB radar, with main focus on research of human detection. The systemshould be easy to understand and modify, as well as reporting reliable data from the scanning. Theresults indicate that such a system can be developed. However, further development to the UWB radarneeds to be made in order to have a complete system