Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

An Open Source, DDI-based Curation System for Social Science Data

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Multi-source ingestion of publisher articles - The SCOAP3 repository experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Sustaining your Open Source project through training: a Hydra case study

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Kramerius - Fedora based open source system for a digital library

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Usability comparison of open source content management systems

The purpose of this thesis is to study, investigate and compare usability of open source cms. The thesis examines and compares usability aspect of some open source cms. The research is divided into two complementary parts –theoretical part and analytical part. The theoretical part mainly describes open source web content management systems, usability and the evaluation methods. The analytical part is to compare and analyze the results found from the empirical research. Heuristic evaluation method was used to measure usability problems in the interfaces. The study is fairly limited in scope; six tasks were designed and implemented in each interface for discovering defects in the interfaces. Usability problems were rated according to their level of severity. Time it took by each task, level of problem’s severity and type of heuristics violated will be recorded, analyzed and compared. The results of this study indicate that the comparing systems provide usable interfaces, and WordPress is recognized as the most usable system.

The neural processes generating visual phenomenal consciousness: ERP and neuronavigated brain stimulation studies

One of the greatest conundrums to the contemporary science is the relation between consciousness and brain activity, and one of the specifi c questions is how neural activity can generate vivid subjective experiences. Studies focusing on visual consciousness have become essential in solving the empirical questions of consciousness. Th e main aim of this thesis is to clarify the relation between visual consciousness and the neural and electrophysiological processes of the brain. By applying electroencephalography and functional magnetic resonance image-guided transcranial magnetic stimulation (TMS), we investigated the links between conscious perception and attention, the temporal evolution of visual consciousness during stimulus processing, the causal roles of primary visual cortex (V1), visual area 2 (V2) and lateral occipital cortex (LO) in the generation of visual consciousness and also the methodological issues concerning the accuracy of targeting TMS to V1. Th e results showed that the fi rst eff ects of visual consciousness on electrophysiological responses (about 140 ms aft er the stimulus-onset) appeared earlier than the eff ects of selective attention, and also in the unattended condition, suggesting that visual consciousness and selective attention are two independent phenomena which have distinct underlying neural mechanisms. In addition, while it is well known that V1 is necessary for visual awareness, the results of the present thesis suggest that also the abutting visual area V2 is a prerequisite for conscious perception. In our studies, the activation in V2 was necessary for the conscious perception of change in contrast for a shorter period of time than in the case of more detailed conscious perception. We also found that TMS in LO suppressed the conscious perception of object shape when TMS was delivered in two distinct time windows, the latter corresponding with the timing of the ERPs related to the conscious perception of coherent object shape. Th e result supports the view that LO is crucial in conscious perception of object coherency and is likely to be directly involved in the generation of visual consciousness. Furthermore, we found that visual sensations, or phosphenes, elicited by the TMS of V1 were brighter than identically induced phosphenes arising from V2. Th ese fi ndings demonstrate that V1 contributes more to the generation of the sensation of brightness than does V2. Th e results also suggest that top-down activation from V2 to V1 is probably associated with phosphene generation. The results of the methodological study imply that when a commonly used landmark (2 cm above the inion) is used in targeting TMS to V1, the TMS-induced electric fi eld is likely to be highest in dorsal V2. When V1 was targeted according to the individual retinotopic data, the electric fi eld was highest in V1 only in half of the participants. Th is result suggests that if the objective is to study the role of V1 with TMS methodology, at least functional maps of V1 and V2 should be applied with computational model of the TMS-induced electric fi eld in V1 and V2. Finally, the results of this thesis imply that diff erent features of attention contribute diff erently to visual consciousness, and thus, the theoretical model which is built up of the relationship between visual consciousness and attention should acknowledge these diff erences. Future studies should also explore the possibility that visual consciousness consists of several processing stages, each of which have their distinct underlying neural mechanisms.

Family ownership – a source of sustainable success in listed companies

Extant research on exchange-listed firms has acknowledged that the concentration of ownership and the identity of owners make a difference. In addition, studies indicate that firms with a dominant owner outperform firms with dispersed ownership. During the last few years, scholars have identified one group of owners, in particular, whose ownership stake in publicly listed firm is positively related to performance: the business family. While acknowledging that family firms represent a unique organizational form, scholars have identified various concepts and theories in order to understand how the family influences organizational processes and firm performance. Despite multitude of research, scholars have not been able to present clear results on how firm performance is actually impacted by the family. In other words, studies comparing the performance of listed family and other types of firms have remained descriptive in nature since they lack empirical data and confirmation from the family business representatives. What seems to be missing is a convincing theory that links the involvement and behavioral consequences. Accordingly, scholars have not yet come to a mutual understanding of what precisely constitutes a family business. The variety of different definitions and theories has made comparability of different results difficult for instance. These two issues have hampered the development of a rigorous theory of family business. The overall objective of this study is to describe and understand how the family as a dominant owner can enhance firm performance, and can act a source of sustainable success in listed companies. In more detail, in order to develop understanding of the unique factors that can act as competitive advantages for listed family firms, this study is based on a qualitative approach and aims at theory development, not theory verification. The data in this study consist of 16 thematic interviews with CEOs, members of the board, supervisory board chairs, and founders of Finnish listed-family firms. The study consists of two parts. The first part introduces the research topic, research paradigm, methods, and publications, and also discusses the overall outcomes and contributions of the publications. The second part consists of four publications that address the research questions from different viewpoints. The analyses of this study indicate that family ownership in listed companies represents a structure that differs from the traditional views of agency and stewardship, as well as from resource-based and stakeholder views. As opposed to these theories and shareholder capitalism which consider humans as individualistic, opportunistic, and self-serving, and assume that the behaviors of an investor are based on the incentives and motivations to maximize private profits, the family owners form a collective social unit that is motivated to act together toward their mutual purpose or benefit. In addition, socio-emotional and psychological elements of ownership define the family members as owners, rather than the legal and financial dimensions of ownership. That is, collective psychological ownership of family over the business (F-CPO) can be seen as a construct that comprehensively captures the fusion between the family and the business. Moreover, it captures the realized, rather than merely potential, family influence on and interaction with the business, and thereby brings more theoretical clarity of the nature of the fusion between the family and the business, and offers a solution to the problem of family business definition. This doctoral dissertation provides academics, policy-makers, family business practitioners, and the society at large with many implications considering family and business relationships.

Benefits of gas driven ground source heat pump systems

This thesis studies energy efficiencies and technical properties of gas driven ground source heat pumps and pump systems. The research focuses on two technologies: gas engine driven compressor heat pump and thermally driven gas absorption heat pump. System consist of a gas driven compressor or absorption ground source heat pump and a gas condensing boiler, which covers peak load. The reference system is a standard electrically powered compressor heat pump with electric heating elements for peak load. The systems are compared through primary energy ratios. Coefficient of performances of different heat pump technologies are also compared. At heat pump level, gas driven heat pumps are having lower coefficient of performances as compared with corresponding electric driven heat pump. However, gas heat pumps are competitive when primary energy ratios, where electricity production losses are counted in, are compared. Technically, gas heat pumps can potentially achieve a slightly higher temperatures with greater total energy efficiency as compared to the electric driven heat pump. The primary energy ratios of gas heat pump systems in relation to EHP-system improves when the share of peak load increases. Electric heat pump system's overall energy efficiency is heavily dependent on the electricity production efficiency. Economy as well as CO2-emissions were not examined in this thesis, which however, would be good topics for further study.

Open source solutions for multiplatform graphics programming

New emerging technologies in the recent decade have brought new options to cross platform computer graphics development. This master thesis took a look for cross platform 3D graphics development possibilities. All platform dependent and non real time solutions were excluded. WebGL and two different OpenGL based solutions were assessed via demo application by using most recent development tools. In the results pros and cons of the each solutions were noted.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.