Ilmavirtausten laskennallinen mallinnus

Diplomityössä tutkitaan kolmea erilaista virtausongelmaa CFD-mallinnuksella. Yhteistä näille ongelmille on virtaavana aineena oleva ilma. Lisäksi tapausten perinteinen mittaus on erittäin vaikeaa tai mahdotonta. Ensimmäinen tutkimusongelma on tarrapaperirainan kuivain, jonka tuotantomäärä halutaan nostaa kaksinkertaiseksi. Tämä vaatii kuivatustehon kaksinkertaistamista, koska rainan viipymäaika kuivausalueella puolittuu. Laskentayhtälöillä ja CFD-mallinnuksella tutkitaan puhallussuihkun nopeuden ja lämpötilan muutoksien vaikutusta rainan pinnan lämmön- ja massansiirtokertoimiin. Tuloksena saadaan varioitujen suureiden sekä massan- ja lämmönsiirtokertoimien välille riippuvuuskäyrät, joiden perusteella kuivain voidaan säätää parhaallamahdollisella tavalla. Toinen ongelma käsittelee suunnitteilla olevan kuparikonvertterin sekundaarihuuvan sieppausasteen optimointia. Ilman parannustoimenpiteitä käännetyn konvertterin päästöistä suurin osa karkaa ohi sekundaarihuuvan. Tilannetta tutkitaan konvertterissa syntyvän konvektiivisen nostevirtauksen eli päästöpluumin sekä erilaisten puhallussuihkuratkaisujen CFD-mallinnuksella. Tuloksena saadaan puhallussuihkuilla päästöpluumia poikkeuttava ilmaverho. Suurin osa nousevasta päästöpluumista indusoituu ilmaverhoon ja kulkeutuu poistokanavaan. Kolmas tutkittava kohde on suunnitteilla oleva kuparielektrolyysihalli, jossa ilmanvaihtoperiaatteena on luonnollinen ilmanvaihto ja mekaaninen happosumun keräysjärjestelmä. Ilmanvaihtosysteemin tehokkuus ja sisäilman virtaukset halutaan selvittää ennen hallin rakentamista. CFD-mallinnuksella ja laskentayhtälöillä tutkitaan lämpötila- ja virtauskentät sekä hallin läpi virtaava ilmamäärä ja ilmanvaihtoaste. Tulo- ja poistoilma-aukkojen mitoitukseen ja sijoitukseen liittyvät suunnitteluarvot varmennetaan sekä löydetään ilmanvaihdon ongelmakohdat. Ongelmakohtia tutkitaan ja niille esitetään parannusehdotukset.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

Information sharing in R&D collaboration - Context-dependency and means of governance

Tiedon jakaminen ja kommunikointi ovat tärkeitä toimintoja verkostoituneiden yritysten välillä ja ne käsitetäänkin yhteistyösuhteen yhtenä menestystekijänä ja kulmakivenä. Tiedon jakamiseen liittyviä haasteita ovat mm. yrityksen liiketoiminnalle kriittisen tiedon vuotaminen ja liiketoiminnan vaatima tiedon reaaliaikaisuus ja riittävä määrä. Tuotekehitysyhteistyössä haasteellista on tiedon jäsentymättömyys ja sitä kautta lisääntyvä tiedon jakamisen tarve, minkä lisäksi jaettava tieto on usein monimutkaista ja yksityiskohtaista. Lisäksi tuotteiden elinkaaret lyhenevät, ja ulkoistaminen ja yhteistyö ovat yhä kasvavia trendejä liiketoiminnassa. Yhdessä nämä tekijät johtavat siihen, että tiedon jakaminen on haastavaa eritoten verkostoituneiden yritysten välillä. Tässä tutkimuksessa tiedon jakamisen haasteisiin pyrittiin vastaamaan ottamalla lähtökohdaksi tiedon jakamisen tilanneriippuvuuden ymmärtäminen. Työssä vastattiin kahteen pääkysymykseen: Mikä on tiedon jakamisen tilanneriippuvuus ja miten sitä voidaan hallita? Tilanneriippuvuudella tarkoitetaan työssä niitä tekijöitä, jotka vaikuttavat siihen, miten yritys jakaa tietoa tuotekehityskumppaneidensa kanssa. Tiedon jakamisella puolestaan tarkoitetaan yrityksestä toiselle siirrettävää tietoa, jota tarvitaan tuotekehitysprojektin aikana. Työn empiirinen aineisto on kerätty laadullisella tutkimusotteella case- eli tapaustutkimuksena yhdessä telekommunikaatioalan yrityksessä jasen eri liiketoimintayksiköissä. Tutkimusjoukko käsitti 19 tuotekehitys- ja toimittajanhallintatehtävissä toimivaa johtajaa tai päällikköä. Työ nojaa pääasiassa hankintojen johtamisen tutkimuskenttään ja tilanneriippuvuuden selvittämiseksi paneuduttiin erityisesti verkostojen tutkimukseen. Työssä kuvattiin tiedon jakaminen yhtenä verkoston toimintona ja yhteistyöhön liittyvättiedon jakamisen hyödyt, haasteet ja riskit identifioitiin. Tämän lisäksi työssä kehitettiin verkoston tutkimismalleja ja yhdistettiin eri tasoilla tapahtuvaa verkoston tutkimusta. Työssä esitettiin malli verkoston toimintojen tutkimiseksija todettiin, että verkostotutkimusta pitäisi tehdä verkosto, ketju, yrityssuhde- ja yritystasolla. Malliin on myös hyvä yhdistää tuote- ja tehtäväkohtaiset ominaispiirteet. Kirjallisuuskatsauksen perusteella huomattiin, että tiedon jakamista on aiemmin tarkasteltu lähinnä tuote- ja yrityssuhteiden tasolla. Väitöskirjassa esitettiin lisää merkittäviä tekijöitä, jotka vaikuttavat tiedon jakamiseen. Näitä olivat mm. tuotekehitystehtävän luonne, teknologia-alueen kypsyys ja toimittajan kyvykkyys. Tiedon jakamisen luonnetta tarkasteltaessa erotettiin operatiivinen, projektin hallintaan ja tuotekehitykseen liittyvä tieto sekä yleinen, toimittajan hallintaan liittyvä strateginen tieto. Tulosten mukaan erityisesti tuotekehityksen määrittelyvaihe ja tapaamiset kasvotusten korostuivat yhteistyössä. Empirian avulla tutkittiin myös niitä tekijöitä, joilla tiedon jakamista voidaan hallita tilanneriippuvuuteen perustuen, koska aiemmin tiedon jakamisen hallintakeinoja tai menestystekijöitä ei ole liitetty suoranaisesti eri olosuhteisiin. Nämä hallintakeinot jaettiin yhteistyötason- ja tuotekehitysprojektitason tekijöihin. Yksi työn keskeisistä tuloksista on se, että huolimatta tiedon jakamisen haasteista, monet niistä voidaan eliminoida tunnistamalla vallitsevat olosuhteet ja panostamalla tiedon jakamisen hallintakeinoihin. Työn manageriaalinen hyöty koskee erityisesti yrityksiä, jotka suunnittelevat ja tekevät tuotekehitysyhteistyötä yrityskumppaniensa kanssa. Työssä esitellään keinoja tämän haasteellisen tehtäväkentän hallintaan ja todetaan, että yritysten pitäisikin kiinnittää entistä enemmän huomiota tiedon jakamisen ja kommunikaation hallintaan jo tuotekehitysyhteistyötä suunniteltaessa.

Modelling additional costs caused by a new product feature

Työn tavoitteena oli mallintaa uuden tuoteominaisuuden aiheuttamat lisäkustannukset ja suunnitella päätöksenteon työkalu Timberjack Oy:n kuormatraktorivalmistuksen johtoryhmälle. Tarkoituksena oli luoda karkean tason malli, joka sopisi eri tyyppisten tuoteominaisuuksien kustannuksien selvittämiseen. Uuden tuoteominaisuuden vaikutusta yrityksen eri toimintoihin selvitettiin haastatteluin. Haastattelukierroksen tukena käytettiin kysymyslomaketta. Haastattelujen tavoitteena oli selvittää prosessit, toiminnot ja resurssit, jotka ovat välttämättömiä uuden tuoteominaisuuden tuotantoon saattamisessa ja tuotannossa. Malli suunniteltiin haastattelujen ja tietojärjestelmästä hankitun tiedon pohjalta. Mallin rungon muodostivat ne prosessit ja toiminnot, joihin uudella tuoteominaisuudella on vaikutusta. Huomioon otettiin sellaiset resurssit, joita uusi tuoteominaisuus kuluttaa joko välittömästi, tai välillisesti. Tarkasteluun sisällytettiin ainoastaan lisäkustannukset. Uuden tuoteominaisuuden toteuttamisesta riippumattomat, joka tapauksessa toteutuvat yleiskustannukset jätettiin huomioimatta. Malli on yleistys uuden tuoteominaisuuden aiheuttamista lisäkustannuksista, koska tarkoituksena on, että se sopii eri tyyppisten tuoteominaisuuksien aiheuttamien kustannusten selvittämiseen. Lisäksi malli soveltuu muiden pienehköjen tuotemuutosten kustannusten kartoittamiseen.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Design and Implementation of a Power Management Feature to a Configuration Management System

Green IT is a term that covers various tasks and concepts that are related to reducing the environmental impact of IT. At enterprise level, Green IT has significant potential to generate sustainable cost savings: the total amount of devices is growing and electricity prices are rising. The lifecycle of a computer can be made more environmentally sustainable using Green IT, e.g. by using energy efficient components and by implementing device power management. The challenge using power management at enterprise level is how to measure and follow-up the impact of power management policies? During the thesis a power management feature was developed to a configuration management system. The feature can be used to automatically power down and power on PCs using a pre-defined schedule and to estimate the total power usage of devices. Measurements indicate that using the feature the device power consumption can be monitored quite precisely and the power consumption can be reduced, which generates electricity cost savings and reduces the environmental impact of IT.

Trustworthy context dependency in ubiquitous systems

The modern society is getting increasingly dependent on software applications. These run on processors, use memory and account for controlling functionalities that are often taken for granted. Typically, applications adjust the functionality in response to a certain context that is provided or derived from the informal environment with various qualities. To rigorously model the dependence of an application on a context, the details of the context are abstracted and the environment is assumed stable and fixed. However, in a context-aware ubiquitous computing environment populated by autonomous agents, a context and its quality parameters may change at any time. This raises the need to derive the current context and its qualities at runtime. It also implies that a context is never certain and may be subjective, issues captured by the context’s quality parameter of experience-based trustworthiness. Given this, the research question of this thesis is: In what logical topology and by what means may context provided by autonomous agents be derived and formally modelled to serve the context-awareness requirements of an application? This research question also stipulates that the context derivation needs to incorporate the quality of the context. In this thesis, we focus on the quality of context parameter of trustworthiness based on experiences having a level of certainty and referral experiences, thus making trustworthiness reputation based. Hence, in this thesis we seek a basis on which to reason and analyse the inherently inaccurate context derived by autonomous agents populating a ubiquitous computing environment in order to formally model context-awareness. More specifically, the contribution of this thesis is threefold: (i) we propose a logical topology of context derivation and a method of calculating its trustworthiness, (ii) we provide a general model for storing experiences and (iii) we formalise the dependence between the logical topology of context derivation and its experience-based trustworthiness. These contributions enable abstraction of a context and its quality parameters to a Boolean decision at runtime that may be formally reasoned with. We employ the Action Systems framework for modelling this. The thesis is a compendium of the author’s scientific papers, which are republished in Part II. Part I introduces the field of research by providing the mending elements for the thesis to be a coherent introduction for addressing the research question. In Part I we also review a significant body of related literature in order to better illustrate our contributions to the research field.

A Soft Contact Collision Method For Real-Time Simulation of Triangularized Geometries in Multibody Dynamics

When modeling machines in their natural working environment collisions become a very important feature in terms of simulation accuracy. By expanding the simulation to include the operation environment, the need for a general collision model that is able to handle a wide variety of cases has become central in the development of simulation environments. With the addition of the operating environment the challenges for the collision modeling method also change. More simultaneous contacts with more objects occur in more complicated situations. This means that the real-time requirement becomes more difficult to meet. Common problems in current collision modeling methods include for example dependency on the geometry shape or mesh density, calculation need increasing exponentially in respect to the number of contacts, the lack of a proper friction model and failures due to certain configurations like closed kinematic loops. All these problems mean that the current modeling methods will fail in certain situations. A method that would not fail in any situation is not very realistic but improvements can be made over the current methods.

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.