Modeling dynamic behavior in tilting pad gas journal bearings

Thisresearch deals with the dynamic modeling of gas lubricated tilting pad journal bearings provided with spring supported pads, including experimental verification of the computation. On the basis of a mathematical model of a film bearing, a computer program has been developed, which can be used for the simulation of a special type of tilting pad gas journal bearing supported by a rotary spring under different loading conditions time dependently (transient running conditions due to geometry variations in time externally imposed). On the basis of literature, different transformations have been used in the model to achieve simpler calculation. The numerical simulation is used to solve a non-stationary case of a gasfilm. The simulation results were compared with literature results in a stationary case (steady running conditions) and they were found to be equal. In addition to this, comparisons were made with a number of stationary and non-stationary bearing tests, which were performed at Lappeenranta University of Technology using bearings designed with the simulation program. A study was also made using numerical simulation and literature to establish the influence of the different bearing parameters on the stability of the bearing. Comparison work was done with literature on tilting pad gas bearings. This bearing type is rarely used. One literature reference has studied the same bearing type as that used in LUT. A new design of tilting pad gas bearing is introduced. It is based on a stainless steel body and electron beam welding of the bearing parts. It has good operation characteristics and is easier to tune and faster to manufacture than traditional constructions. It is also suitable for large serial production.

Organisaation toimintamallin muutoksen toteuttaminen

Työn tavoitteena oli selvittää mitä erityspiirteitä esiintyy siirryttäessä kompetenssipohjaiseen organisaatiorakenteeseen ja toimintaan sekä kuvata muutoksen toteutusprosessi. Tutkimus pitää sisällään kaksi keskeisintä ja tärkeää käsitettä: osaamisrakenteen muutos ja sen johtaminen. Empiirinen osa on toteutettu kyselylomakkeen avulla, joka lähetettiin valituille Case-yrityksen operatiivisesta johtamisesta vastuussa oleville esimiehille. Organisaation muutoksen toteuttaminen on erittäin vaativa toimenpide, joka vaikuttaa organisaation jokaiseen henkilöön ja heidän päivittäisiin työtehtäviinsä. Muutoksen toteuttaminen vaatii paljon aikaa, mutta ennen kaikkea kärsivällisyyttä. Mitä isommasta organisaatiosta on kysymys, sitä vaikeampaa toteuttaminen on. Muutokseen vaikuttavat oman henkilöstön lisäksi organisaation ulkoiset tekijät, kuten asiakkaat, markkinat sekä erilaiset osaamiseen ja teknologiaan liittyvät uudenlaiset osaamisvaatimukset. Tärkein tekijä muutoksen onnistumisen kannalta on tiedostaa koko organisaation tasolla, miksi muutokseen lähdetään ja mitkä ovat muutoksen tavoitteet. Tavoitellaanko muutoksella pientä hienosäätöä organisaation toiminnassa vai onko taustalla kokonaan uuden toimintamallin käyttöönotto uudenlaisia strategisia linjauksia myöten.

Microwave Slot Transmission Line Filled with Ferroelectric and Ferromagnetic Layers

Now when the technology is fast developing it is very important to investigate new hybrid structures. One way is to use ferrite ferroelectric layered structures. Theoretical and experimental investigation of such structures was made. These structures have advantages of both layers and it is possible to tune the behavior of this structure by external electric and magnetic field. But these structures have some disadvantages connected with presence of thick ferroelectric layer. One way to overcome this problem is to use slotline. So this is another new way to create hybrid ferrite ferroelectric structures, but it is needed to create new theory and find experimental proof that the behavior of these structures can be tuned with external magnetic and electric fields.

Effects of Surface Properties and Wood Modification into Direct Printing on Wood

Building industry is a high volume branch which could provide prominent markets for wood based interior decoration solutions. Competition in interior decoration markets requires versatility in appearance. Versatility in wood appearance and added value could be achieved by printing grain patterns of different species or images directly onto wood. The problem when planning wood printing’s implementing into durable applications is basically how to transfer a high quality image or print sustainably onto wood, which is porous, heterogeneous, dimensionally unstable, non-white and rough. Wood preservation or treating, and modification can provide durability against degradation but also effect to the surface properties of wood which will effect on printability. Optimal adhesion is essential into print quality, as too high ink absorbance can cause spreading and too low ink absorbance cause pale prints. Different printing techniques have different requirements on materials and production. The direct printing on wood means, that intermedias are not used. Printing techniques with flexible printing plates or in fact non-impact techniques provide the best basis for wood printing. Inkjet printing of wood with different mechanical or chemical surface treatments, and wood plastic composite material gave good results that encourage further studies of the subject. Sanding the wood surface anti-parallel to the grain gave the best overall printing quality. Spreading parallel to the grain could not be avoided totally, except in cases where wood was treated hydrophobic so adhesion of the ink was not sufficient. Grain pattern of the underlying wood stays clearly visible in the printed images. Further studies should be made to fine tune the methods that already gave good results. Also effects of moisture content of wood, different inks, and long-term exposure to UV-radiation should be tested.

Robottisolun servotekniikan kehittäminen

Työn kehityksen kohteena toimii CIRB 500i –robottisolu, jonka sisällä työskentelee korkeaan tuotantokapasiteettiin kehitetty näköjärjestelmällä ohjattu sähköservomanipulaattori. Kokonaisuutena ajatellen työ tekee servotekniikan osalta poikkileikkauksen CIRB 500i –robottisolun matkasta prototyypistä tuotteeksi erilaisine kehitysvaiheineen. Työn alkutaipaleella perehdytään pitkälle pelkkään teoriaan nojautuen mitä on servotekniikka, miten se toimii ja kuinka se viritetään. Tämän jälkeen edellä opitut teoriat konkretisoidaan CIRB 500i –robottisoluun ja siinä käytettävään servotekniikkaan. Robottisolun komponenttien ja laitteistojen tullessa tutuiksi mietitään ja kehitetään kuinka robottisolusta saataisiin entistä kehittyneempi ja käytännöllisempi teknillisestä näkökulmasta katsoen kuitenkaan valmistamisen kustannustehokkuutta unohtamatta. Loppupuolella analysoidaan miten edellä tehdyt kehitystoimenpiteet ovat vaikuttaneet robottisolun toimintaan ja valmistamiseen. Lopuksi sovelletaan teoriaosuuden virityskeinoja servojen virittämiseen käytännössä ja analysoidaan saavutettuja kehittämistyön tuloksia. Lisäksi tehdään katsaus CIRB 500i –robottisolun tulevaisuuden näkymiin.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.

Populaarimusiikin mustempi puoli

Kirjallisuusarvostelu

”Du sköna sång, vårt bästa arv” - kanonisering av en sångskatt ur ett hermeneutiskt perspektiv

Songs have the power to get through to people. When lyrics are combined with a tune, the result is an entity where the first few notes of a melody can evoke emotions of recognition and belonging. A song treasury consists of such songs that are part of a canonized song tradition. The process where certain songs become part of an established song treasury is long, and many other aspects than the tune itself influence the forming of a song treasury. By examining the characteristics of a song tradition, the history of an ethnic group can be illuminated. In this study, music, pedagogy, and the sociocultural context are merged into a whole where a common song tradition, the song treasury, is in focus. The main aim of this study is to deepen the understanding of a song treasury, its development and contents. This understanding is accomplished by analyzing the musical and lyrical characteristics of 60 songs, which have been sung in schools, homes, and communities, thereby becoming popular among the Swedish-speaking Finns during the 20th century. The songs have been chosen by combining three song lists, of which two lists are closely related to school curricula. The third song list is a result of a survey on favourite songs, according to the situation around year 2000. The songs are examined in their notated versions, a number of song books and text books (n = 29) forming the empirical material. In this study, a hermeneutical approach is applied, content analysis being the method. The analysis is based on three perspectives: the sociocultural perspective, the music-pedagogical perspective, and the musico-analytical perspective. Within each perspective, two aspects are studied. This results in a hexagonal model which forms the structure of the study as a whole. The first two perspectives form the background; a historical context where nation, education, home country, and homestead are regarded as highly important. A common song repertoire is considered to be an effective means of building collective identity within ethnic groups, the common language and the cultural heritage being used as rhetorical arguments. During the early 1900s, choir festivals become an educational platform where conceptions of a common belonging are developed and strengthened through religious, patriotic, and poetical expressions. National school curricula in singing and music have similar characteristics, cultural heritage and values education being in focus. The song lyrics often describe nature and emotions, and they also appear to be personal and situated in a given time and place. Patriotic expressions and songs about music are also fairly common. The songs generally express positive attitudes, which are intensified by major tonality, rich and varied melodies with stable rhythms, and a strong tonal base. The analyzed details of the studied aspects are merged into a thick description, which results in an interpretation pattern with three dimensions: a song treasury can be considered an expression of collective identity, cultural heritage, and values education.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Urvilja : när livet är människans hem

This caring science study explores ‘Will’ as an ontological concept. The aim is to deepen the understanding of the essence of Will, and to highlight the manifestations of Will and how Will becomes evident in clinical caring. Will is ontological and universal. Will is connected with the essence of the human being, and manifests in the human being as will. The approach is inspired by Gadamer’s philosophical hermeneutics. The study’s horizon of understanding consists of Eriksson’s caritative theory and the caring science-tradition. The study’s research questions are as follows: What is the essence of Will? What are its manifestations? How does Will become evident in clinical caring? The hermeneutic interpretative movement is initiated by the material, which consists of the philosopher Arthur Schopenhauer’s texts, letters from experts and dictionaries. Meaning-bearing substance fragments in the material are intertwined with the original horizon of understanding through hermeneutical reading, hermeneutical interpretation and concept analysis in an oscillating interpretive movement. An abstraction occurs when the new substance is illuminated by the caring science ontology. The oscillating interpretive movement results in a reinterpreted horizon of understanding, which in turn provides the findings of the study. The reinterpreted horizon of understanding is presented in the form of a theoretical model and abductive theses. The essence of Will is represented in the theoretical model as the lifeaffirming and the loving force. Life and love are Will’s origin and destination. Will’s manifestations (its diversity) hold conditions and chance occurrences that obstruct Will. Hence the will of the human being does not necessarily appear in a way that is in tune with ontological Will. Will represents the lifeblood of ethos, and in this lifeblood love flows. Will acts by virtue of itself, and gives ethos its force. Will manifests in a way that ethos can affirm. When Will is affected by caring its force is active in the service of life and love. Being a caregiver entails acting as a world-eye, which means recognizing Will in diversity. For caregivers, being a world-eye means observing fragments of Will as it manifests in its original form in the real reality, and acting as the mirror of life. The human being who is able to perceive the fundamental values of life and to live according to these has understood the laws of life and entered upon the human calling. The human being then lives according to the fundamental order and has found a home in life.

Modulation of TGF-β signaling by Hypoxia

A tumor is a fast-growing malignant tissue. This creates areas inside the tumor that are distant from local blood vessels to be able to get enough oxygen. This hypoxic condition activates a transcription factor called hypoxia inducible factor (HIF). HIF responses help a cell to adapt to decreased oxygen by activating glycolytic and angiogenesis pathways and by regulating apoptotic responses. Hypoxia drives the upregulation of a growth factor called transforming growth factor beta (TGF-beta). Similar to a hypoxia response, TGF is an important regulator of cell fate. TGF-β and HIF pathways regulate partially overlapping target genes. This regulation can also be cooperative. The TGF-beta signal is initiated by activation of plasma membrane receptors that then activate effector proteins called small mothers against decapentaplegic (Smad) homologs. In healthy tissue, TGF-β keeps cell proliferation and growth under control. During cancer progression, TGF-beta has shown a dual role, whereby it inhibits initial tumor formation but, conversely, in an existent tumor, TGF-beta drives malignant progression. Along with HIF and TGF-beta also protein dephosphorylation is an important regulatory mechanism of cell fate. Protein dephosphorylation is catalyzed by protein phosphatases such as Protein phosphatase 2A (PP2A). PP2A is a ubiquitous phosphatase that can exist in various active forms. PP2A can specifically regulate TGF-beta signaling either by enhancing or inhibiting the receptor activity. This work demonstrates that during hypoxia, PP2A is able to fine-tune TGF-beta signal by specifically targeting Smad3 effector in a Smad7-dependent manner. Inactivation of Smad3 in hypoxia leads to malignant conversion of TGF-beta signaling.

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.