Kahden hyperparametrin vaikutus konvoluutioneuroverkkojen oppimiskykyyn

The research topic of this work was to study the effect of two special hyperparameters to the learning performance of Convolutional Neural Networks (CNN) in the Cifar-10 image recognition problem. The first hyperparameter, that was chosen to be studied, was the depth of the CNN, which is known to effect to the amount of the feature extraction. The second hyperparameter, was the use of the special regularization technique called Dataset Augmentation (DA), which is known to increase the amount of training data available artificially so that there is got extracted and taught more features from the members of each classes to the networks. The hypothesis was that the increment of the depth and the training data would improve the learning accuracy, especially by improving the testing accuracy, which measures the ability of generalization of learned features for the model. The work was implemented by the high-level Deep Learning software called Keras, of which source code is freely available on Github. For the CNN needed in the work there was found ready Python implementation, which was modified slightly by adding a few code lines. The depth of the networks was incremented first without Dataset Augmentation, by adding the number of the convolutional layers one-by-one from four to eight. The same was repeated while the augmentation was set on. The results were partly equivalent, partly contradictory with the hypothesis. The increment of the depth increased both the training and testing accuracy, when the Dataset Augmentation was not used. Instead if it was used, while the training accuracy still improved, the testing accuracy dropped, even below the accuracy achieved by the original networks. The natural conclusion is that both increasing of the depth and the amount of data artificially effect the overfitting. Instead to apply both at same time, only either of them should be used

OSKU - an Application for Collecting User Feedback on Living Conditions in Buildings

Reducing energy consumption is currently a very topical area of research, but perhaps it is more a question of optimizing rather than reducing consumption. Preserving optimal working and living conditions within buildings should also be considered in combination with energy usage. The global rise in temperature caused by climate change may bring requirements for additional cooling of buildings, even in geographical areas that have not needed cooling in the past. In Finland, the winter months will require buildings to be heated to provide a comfortable indoor environment even in the future. A multitude of sensors exist for measuring quantities such as carbon dioxide, relative humidity, and temperature, but how well do the values measured by sensors match the subjective feelings of the people living or working inside buildings? In an attempt to discover how people feel in their working environment, we developed an application called OSKU for collecting user feedback concerning indoor air quality, and tested it in two real-life locations (a daycare center and a school), which were also fitted with sensors. In this paper we will describe the application, the use cases, and give an example of how we compared the feedback with the measured sensor values.acceptedVersionPeer reviewe

Tutkimus autokatsastusalan markkinoiden kehityksesta ja kilpailusta

siirretty Doriast

Survey of Component-Based Software Engineering within IoT Development

The Internet of Things (IoT) is a growing area in everyday life. New applications under the umbrella term IoT are being developed continuously. A typical IoT system consists of quite a large set of interchangeable components. In prototype development the Raspberry Pi and Arduino have become core components of wireless sensor network solutions. This research looks at how component-based software engineering (CBSE) and off-the-shelf components can be taken advantage of when defining IoT systems. We will attempt to identify the common properties of IoT systems and compare how well these properties relate to the CBSE component characteristics: composability, deployability, comprehensive documentation, independence, and standardization. The research methodology used in this study is a literature survey. The main results of the study show that CBSE is not widely used. There has been very little research on the software side of applications, as most studies have focused on describing the hardware side of implementations. The conclusion can be made that, in order for studies to be reproducible, the software side should be described in more detail.acceptedVersionPeer reviewe

“I love learning new things” : An institutional logics perspective on learning in professional journalism

In contemporary working life, journalists are often faced with the pressures of an increasingly precarious field where employment is less stable and more contractual than in previous years. Consequently, learning as a skill has grown in importance as journalists enter and leave the job market. Previous research has often portrayed professional journalists as unwilling to learn due to the persistence of the institution of journalism. Consequently, this study examines learning in professional journalism through interviews with 30 Finnish journalists. We adopt the institutional logics perspective to examine which institutional logics manifest in journalists' descriptions of learning and how. We identify a labor market logic that highlights how the need to learn continuously to satisfy employer needs is felt as pervasive. Additionally, our analysis suggests that journalists negotiate the technology logic’s push for learning digital skills with journalism’s professional logic. The analysis also highlights a negotiation of market and professional logics in the journalists' experiences of intensification in relation to learning. Intensification, specifically, may have consequences for journalists' skill levels and occupational well-being.publishedVersionPeer reviewe

Mutation m.15923A>G in the MT-TT gene causes mild myopathy - case report of an adult-onset phenotype

Background: Only five patients have previously been reported to harbor mutations in the MT-TT gene encoding mitochondrial tRNA threonine. The m.15923A > G mutation has been found in three severely affected children. One of these patients died within days after birth and two had a phenotype of myoclonic epilepsy with ragged red fibers (MERRF) in early childhood. We have now found the mutation in an adult patient with mild myopathy.Case presentation: The patient is a 64-year-old Finnish man, who developed bilateral ptosis, diplopia and exercise intolerance in his fifties. Family history was unremarkable. Muscle histology showed cytochrome c-oxidase (COX) negative and ragged red fibres. The m.15923A > G mutation heteroplasmy was 33% in the skeletal muscle and 2% in buccal epithelial cells. The mutation was undetectable in the blood. Single-fibre analysis was performed and COX-negative fibres had a substantially higher heteroplasmy of 92%, than the normal fibres in which it was 43%.Conclusions: We report the fourth patient with m. 15923A > G and with a remarkably milder phenotype than the previous three patients. Our findings and recent biochemical studies suggest that the mutation m.15923A > G is a definite disease-causing mutation. Our results also suggest that heteroplasmy of the m.15923A > G mutation correlates with the severity of the phenotype. This study expands the catalog of the phenotypes caused by mutations in mtDNA

Mineral content of grain yield of cereals and the effect of fertilization on it

This investigation examines the mineral content of cereal grain yields and the effect of NPK-fertilization on this content. The NPK-fertilization and the increased yield level thus obtained have not essentially affected the mineral contents of cereal grain. Among different kinds of cereals the mineral content of oats is in most cases the highest and that of rye and winter wheat the lowest. None of the different kinds of fertilizers gave clear differences in the mineral content of barley grain

Comparing the Use of Custom-built and Commercial Off-the-shelf Data Gathering Devices in IoT Systems

IoT refers to a wide range of devices that have Internet access, and collect and transmit information. Data have to be gathered from the most diverse environments. Custom data gathering devices are often necessary in situa- tions where the data being collected are unique or need to be collected in a certain way. This is because commercial off-the-shelf (COTS) products may not have the necessary capabilities or features to meet the specific requirements of the data collection. This study focuses on COTS and self- built data gathering devices by comparing two data gathering prototype systems: The first designed and implemented from scratch and the second built with COTS components. Both systems are compared in terms of various features such as cost-effectiveness, time saving, and the usefulness of the output. The end goal of the study is to provide a proposal for when it is appropriate to use COTS components and when it is necessary to design and build devices yourself.acceptedVersionPeer reviewe