Small scale combined heat and power units using external combustion

Combined heat and power plants are of increasing interest due to the rising concern over global warming as they can lower emissions, by having very higher efficiencies than traditional power plants. Implemented on a small scale units and with external combustion they allow for great flexibility in implementation and fuel, and can allow for remote locations to serve their own heat and power needs. This thesis investigates in the first part the available technologies for such plants on a small scale and compares them on criteria which are important for efficiency, economy, implementation and operation in a remote area. Organic- and steam Rankine cycles are evaluated as well as gas turbines, Stirling engines and thermoelectric generators. The first part concludes with the choice of gas turbines as the best technology for small scale power and heat generation based on defined criteria. In the second different concepts for the basic layout and components of such a plant is evaluated. A commercially available gas turbine is chosen as a base and a solution where the gas turbine feeds its outlet air into the combustion chamber is selected. Two alternative layouts are simulated using the open source DNA, and compared along with a base case. While both layouts compared have advantages and drawbacks, only one has an efficiency comparable to larger plants, and should therefore the preferred concept under most circumstances

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models

Learning from problem-based projects in cross-diciplinary student teams.

This paper explores how Engineering students and Work and Welfare students reflect upon their own engagement in a one-week cross-disciplinary project. To develop a better understanding of what unfolds during these activities we collected data through anonymous surveys two consecutive years. Data from these 141 respondents were analysed using a learning history approach and are presented as narratives. Results show major disruptions and conflicts driving the student projects, exposing inviting confrontations, social identity threats, managing diversity, and friction of ideas. Whereas this in many cases led to new and better project solutions, these real-world experiences raise awareness of the need for tools and methods for training students. The aim of the paper is to learn from students’ experiences through narrative distance, and fill a gap in the literature between problem-based learning (PBL) and the learning history method. Discussing different experiences of cross-disciplinary teamwork through the explanations of these theories, we also lay out potential questions for future research on the topic.publishedVersio

Mating activity and parturition of the smooth snake Coronella austriaca in Norway

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Video Analytics in Elite Soccer: A Distributed Computing Perspective

Ubiquitous sensors and Internet of Things (IoT)technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post-game. New methods, including machine learning, image, and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA’s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing, and its importance in video analytics and propose a future research perspective.acceptedVersio

Real time magneto-optical imaging of vortices in superconductors

We demonstrate here real-time imaging of individual vortices in a NbSe2 single crystal using polarized light microscopy. A new high-sensitivity magneto-optical (MO) imaging system enables observation of the static vortex lattice as well as single vortex motion at low flux densities.Comment: 3 pages, 1 figur