Enhancing the development of military capabilities by a systems approach

The political environment of security and defence has changed radically in the Western industrialised world since the Cold War. As a response to these changes, since the beginning of the twenty-first century, most Western countries have adopted a ‘capabilities-based approach’ to developing and operating their armed forces. More responsive and versatile military capabilities must be developed to meet the contemporary challenges. The systems approach is seen as a beneficial means of overcoming traps in resolving complex real -world issues by conventional thinking. The main objectives of this dissertation are to explore and assess the means to enhance the development of military capabilities both in concept development and experimentation (CD&E) and in national defence materiel collaboration issues. This research provides a unique perspective, a systems approach, to the development areas of concern in resolving complex real-world issues. This dissertation seeks to increase the understanding of the military capability concept both as a whole and with in its life cycle. The dissertation follows the generic functionalist systems methodology by Jackson. The methodology applies a comprehensive set of constitutive rules to examine the research objectives. This dissertation makes contribution to current studies about military capability. It presents two interdepen dent conceptual capability models: the comprehensive capability meta-model (CCMM) and the holistic capability life cycle model (HCLCM). These models holistically and systematically complement the existing, but still evolving, understanding of military capability and its life cycle. In addition, this dissertation contributes to the scientific discussion of defence procurement in its broad meaning by introducing the holistic model about the national defence materiel collaboration between the defence forces, defence industry and academia. The model connects the key collaborative mechanisms, which currently work in isolation from each other, and take into consideration the unique needs of each partner. This dissertation contributes empirical evidence regarding the benefits of enterprise architectures (EA) to CD&E. The EA approach may add value to traditional concept development by increasing the clarity, consistency and completeness of the concept. The most important use considered for EA in CD&E is that it enables further utilisation of the concept created in the case project.

Taajuusmuuttaja aktiivisesti säädetyn magneettilaakerin virtalähteenä

Työssä tutkitaan mahdollisuutta hyödyntää sähkömoottorien nopeudensäätöön suunniteltuja kaupallisia taajuusmuuttajia osana aktiivisesti säädetyn magneettilaakeroinnin säätöjärjestelmää. Magneettilaakerijärjestelmän ohjaamiseksi tarvitaan vahvistin, jonka tehtävänä on muuntaa paikkasäätöjärjestelmältä tuleva ohjearvo virraksi, jännitteeksi tai magneettivuoksi voiman tuottamiseksi laakerikäämityksellä. Nykyaikaiset modernit taajuusmuuttajat mahdollistavat säätöalgoritmien suorittamisen sekä liitynnän muuhun automaatiojärjestelmään kenttäväylien kautta. Tämän järjestelmäintegraation myötä olisi mahdollista rakentaa modulaarinen säätöjärjestelmä hyödyntäen luotettavaksi todettuja teollisuusautomaatiotuotteita vain muutamalla itse magneettilaakerijärjestelmään liittyvällä tuotteella. Haluttaessa hyödyntää kolmivaiheinen taajuusmuuttaja mahdollisimman tehokkaasti magneettilaakerin teholähteenä tulee laakerikäämityksien kytkeytymistä taajuusmuuttajaan tarkastella tarkemmin. Kirjallisuustutkimuksessa keskitytään taajuusmuuttajan tehokytkimien muodostaman vaihtosuuntaajan eri rakennevaihtoehtojen sekä virtasäädön dynaamisten ominaisuuksien tarkasteluun. Soveltuvien rakennevaihtoehtojen sekä virtasäädön suorituskyky todennetaan simuloinnein ja lopuksi todellisella koelaitteistolla. Magneettilaakeroinnilla varustetun sähkökoneen roottorin onnistunut leijuttaminen viiden vapausasteen suhteen paikkasäädettynä sekä mittauksien tulokset osoittavat, ettei taajuusmuuttajien järjestelmäarkkitehtuurista löydy merkittäviä esteitä muuttajien hyödyntämiseksi magneettilaakerisovelluksissa.

VIRVE-tukiaseman peittoalueen mallintaminen

Tekniikan kehityksen myötä reaalimaailman ilmiöitä voidaan mallintaa yhä tarkemmin. Tässä työssä tutkitaan VIRVE-radioverkon tukiaseman peittoalueen mallintamista. Työn tavoitteena oli mallintaa VIRVE-tukiaseman peittoalue mahdollisimman tarkasti. Tavoitteena oli myös löytää VIRVE-tukiasemaan peittoalueen kannalta nykyistä paremmat antenniratkaisut. Tutkimuksen teoreettisessa osuudessa selvitettiin tukiaseman peittoalueen mallintamiseen liittyviä tekijöitä, kuten radioaaltojen ominaisuuksia ja etenemismalleja sekä antennien teoriaa. Eri etenemismalleilla laskettuja tukiasemien peittoennusteita verrattiin empiirisesti toteutettuihin tukiasemien peittoalueiden mittaustuloksiin. Työn tuloksena löydettiin radioaaltojen etenemismalli, jolla mallinnettavien tukiasemien peittoalue-ennusteet vastasivat hyvin empiirisesti mitattuja tuloksia. Lisäksi löydettiin nykyistä paremmat antenniratkaisut VIRVE-tukiasemiin.

Machine vision methods for process measurements in pulping

The papermaking industry has been continuously developing intelligent solutions to characterize the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee the desired quality of the end product. Based on the much improved imaging techniques and image-based analysis methods, it has become possible to look inside the manufacturing pipeline and propose more effective alternatives to human expertise. This study is focused on the development of image analyses methods for the pulping process of papermaking. Pulping starts with wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of the process it is important to analyze the properties of the raw material to guarantee the product quality. In order to evaluate properties of fibers, the main component of the pulp suspension, a framework for fiber characterization based on microscopic images is proposed in this thesis as the first contribution. The framework allows computation of fiber length and curl index correlating well with the ground truth values. The bubble detection method, the second contribution, was developed in order to estimate the gas volume at the delignification stage of the pulping process based on high-resolution in-line imaging. The gas volume was estimated accurately and the solution enabled just-in-time process termination whereas the accurate estimation of bubble size categories still remained challenging. As the third contribution of the study, optical flow computation was studied and the methods were successfully applied to pulp flow velocity estimation based on double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets, including the semisynthetic ground truth generation, feature selection, and performance comparison of the state-of-the-art classification techniques, was proposed as the fourth contribution. The framework was successfully tested on the semisynthetic and real-world pulp sheet images. These four contributions assist in developing an integrated factory-level vision-based process control.

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.

Mathematics inspired by Darwin. Adaptive dynamics of dispersal and cooperation

In 1859, Charles Darwin published his theory of evolution by natural selection, the process occurring based on fitness benefits and fitness costs at the individual level. Traditionally, evolution has been investigated by biologists, but it has induced mathematical approaches, too. For example, adaptive dynamics has proven to be a very applicable framework to the purpose. Its core concept is the invasion fitness, the sign of which tells whether a mutant phenotype can invade the prevalent phenotype. In this thesis, four real-world applications on evolutionary questions are provided. Inspiration for the first two studies arose from a cold-adapted species, American pika. First, it is studied how the global climate change may affect the evolution of dispersal and viability of pika metapopulations. Based on the results gained here, it is shown that the evolution of dispersal can result in extinction and indeed, evolution of dispersalshould be incorporated into the viability analysis of species living in fragmented habitats. The second study is focused on the evolution of densitydependent dispersal in metapopulations with small habitat patches. It resulted a very surprising unintuitive evolutionary phenomenon, how a non-monotone density-dependent dispersal may evolve. Cooperation is surprisingly common in many levels of life, despite of its obvious vulnerability to selfish cheating. This motivated two applications. First, it is shown that density-dependent cooperative investment can evolve to have a qualitatively different, monotone or non-monotone, form depending on modelling details. The last study investigates the evolution of investing into two public-goods resources. The results suggest one general path by which labour division can arise via evolutionary branching. In addition to applications, two novel methodological derivations of fitness measures in structured metapopulations are given.

Differentiation of Metabolically Distinct Areas within Head and Neck Region using Dynamic 18F-FDG Positron Emission Tomography Imaging

Positron Emission Tomography (PET) using ¹⁸F-FDG is playing a vital role in the diagnosis and treatment planning of cancer. However, the most widely used radiotracer, ¹⁸F-FDG, is not specific for tumours and can also accumulate in inflammatory lesions as well as normal physiologically active tissues making diagnosis and treatment planning complicated for the physicians. Malignant, inflammatory and normal tissues are known to have different pathways for glucose metabolism which could possibly be evident from different characteristics of the time activity curves from a dynamic PET acquisition protocol. Therefore, we aimed to develop new image analysis methods, for PET scans of the head and neck region, which could differentiate between inflammation, tumour and normal tissues using this functional information within these radiotracer uptake areas. We developed different dynamic features from the time activity curves of voxels in these areas and compared them with the widely used static parameter, SUV, using Gaussian Mixture Model algorithm as well as K-means algorithm in order to assess their effectiveness in discriminating metabolically different areas. Moreover, we also correlated dynamic features with other clinical metrics obtained independently of PET imaging. The results show that some of the developed features can prove to be useful in differentiating tumour tissues from inflammatory regions and some dynamic features also provide positive correlations with clinical metrics. If these proposed methods are further explored then they can prove to be useful in reducing false positive tumour detections and developing real world applications for tumour diagnosis and contouring.

Suorituskyvyn mittaaminen rakennusteollisuudessa

Tutkimuksen tavoitteena oli rakentaa rakennusteollisuuteen sopiva suorituskyvyn analysointijärjestelmä. Järjestelmä on koko yrityksen kattava ja tasapainoinen. Tällöin voidaan paremmin saavuttaa yritystoiminnan perimmäinen tarkoitus eli kannattava liiketoiminta sekä taata yrityksen taloudelliset toimintaedellytykset, niin pitkällä kuin lyhyelläkin aikavälillä. Työ oli toimintatutkimus, mikä on reaalimaailmaa koskeva empiirinen tutkimus, joka konstruoi uutta todellisuutta. Toimintatutkimukselle on tyypillistä, että se suuntautuu käytäntöihin, pyrkii muutokseen ja tutkittavat osallistuvat tutkimusprosessiin. Aluksi käytiin läpi suorituskyvyn mittaamiseen liittyvää teoriaa. Sen jälkeen aloitettiin itse järjestelmän suunnittelu pohtimalla yrityksen visiota, strategiaa ja menestystekijöitä huomioiden rakennusteollisuuden erityispiirteet. Näiden pohjalta valittiin mitattavat osa-alueet sekä niihin liittyvät mittarit. Suorituskyvyn analysointijärjestelmän toteutuksen lisäksi havaittiin, että järjestelmän rakentaminen itsessään oli yritykselle erinomainen oppimisprosessi, joka avasi uusia näkökulmia sekä yrityksen toiminnan että henkilöstön kehittämiseen.

Business intelligence viewpoint on managerial decision-making : Ill-structured problems and decision support systems

Vaikka liiketoimintatiedon hallintaa sekä johdon päätöksentekoa on tutkittu laajasti, näiden kahden käsitteen yhteisvaikutuksesta on olemassa hyvin rajallinen määrä tutkimustietoa. Tulevaisuudessa aiheen tärkeys korostuu, sillä olemassa olevan datan määrä kasvaa jatkuvasti. Yritykset tarvitsevat jatkossa yhä enemmän kyvykkyyksiä sekä resursseja, jotta sekä strukturoitua että strukturoimatonta tietoa voidaan hyödyntää lähteestä riippumatta. Nykyiset Business Intelligence -ratkaisut mahdollistavat tehokkaan liiketoimintatiedon hallinnan osana johdon päätöksentekoa. Aiemman kirjallisuuden pohjalta, tutkimuksen empiirinen osuus tunnistaa liiketoimintatiedon hyödyntämiseen liittyviä tekijöitä, jotka joko tukevat tai rajoittavat johdon päätöksentekoprosessia. Tutkimuksen teoreettinen osuus johdattaa lukijan tutkimusaiheeseen kirjallisuuskatsauksen avulla. Keskeisimmät tutkimukseen liittyvät käsitteet, kuten Business Intelligence ja johdon päätöksenteko, esitetään relevantin kirjallisuuden avulla – tämän lisäksi myös dataan liittyvät käsitteet analysoidaan tarkasti. Tutkimuksen empiirinen osuus rakentuu tutkimusteorian pohjalta. Tutkimuksen empiirisessä osuudessa paneudutaan tutkimusteemoihin käytännön esimerkein: kolmen tapaustutkimuksen avulla tutkitaan sekä kuvataan toisistaan irrallisia tapauksia. Jokainen tapaus kuvataan sekä analysoidaan teoriaan perustuvien väitteiden avulla – nämä väitteet ovat perusedellytyksiä menestyksekkäälle liiketoimintatiedon hyödyntämiseen perustuvalle päätöksenteolle. Tapaustutkimusten avulla alkuperäistä tutkimusongelmaa voidaan analysoida tarkasti huomioiden jo olemassa oleva tutkimustieto. Analyysin tulosten avulla myös yksittäisiä rajoitteita sekä mahdollistavia tekijöitä voidaan analysoida. Tulokset osoittavat, että rajoitteilla on vahvasti negatiivinen vaikutus päätöksentekoprosessin onnistumiseen. Toisaalta yritysjohto on tietoinen liiketoimintatiedon hallintaan liittyvistä positiivisista seurauksista, vaikka kaikkia mahdollisuuksia ei olisikaan hyödynnetty. Tutkimuksen merkittävin tulos esittelee viitekehyksen, jonka puitteissa johdon päätöksentekoprosesseja voidaan arvioida sekä analysoida. Despite the fact that the literature on Business Intelligence and managerial decision-making is extensive, relatively little effort has been made to research the relationship between them. This particular field of study has become important since the amount of data in the world is growing every second. Companies require capabilities and resources in order to utilize structured data and unstructured data from internal and external data sources. However, the present Business Intelligence technologies enable managers to utilize data effectively in decision-making. Based on the prior literature, the empirical part of the thesis identifies the enablers and constraints in computer-aided managerial decision-making process. In this thesis, the theoretical part provides a preliminary understanding about the research area through a literature review. The key concepts such as Business Intelligence and managerial decision-making are explored by reviewing the relevant literature. Additionally, different data sources as well as data forms are analyzed in further detail. All key concepts are taken into account when the empirical part is carried out. The empirical part obtains an understanding of the real world situation when it comes to the themes that were covered in the theoretical part. Three selected case companies are analyzed through those statements, which are considered as critical prerequisites for successful computer-aided managerial decision-making. The case study analysis, which is a part of the empirical part, enables the researcher to examine the relationship between Business Intelligence and managerial decision-making. Based on the findings of the case study analysis, the researcher identifies the enablers and constraints through the case study interviews. The findings indicate that the constraints have a highly negative influence on the decision-making process. In addition, the managers are aware of the positive implications that Business Intelligence has for decision-making, but all possibilities are not yet utilized. As a main result of this study, a data-driven framework for managerial decision-making is introduced. This framework can be used when the managerial decision-making processes are evaluated and analyzed.

Motivointi pelillistämisen avulla

Työ käsittelee ihmisten motivointia hyödyntäen pelillistämistä. Työhön kuuluu myös mobiilisovelluksen toteutus, jolla pyritään vaikuttamaan julkisen liikenteen käytön kavattamiseen. Pelillistämisen käyttäminen sovelluksissa on kasvanut huimasti varsinkin mobiililaitteiden yleistymisen myötä. Pelillistämisellä pyritään vaikuttamaan ihmisten käyttäytymiseen pelielementtien avulla. Eli pelielementit, kuten pisteet ja saavutukset, tuodaan reaalimaailmaan ja käyttäjä palkitaan suorituksesta välittömästi. Työssä käydään läpi kaksi pelillistämistutkimusesimerkkiä, EcoIsland ja Orientation Passport, ja niiden havainnot. Pelillistämisen tehokkuuden tutkiminen on vaikeata, mutta yleisesti sitä pidetään toimivana. Kuitenkaan mikään taianomainen työkalu se ei ole.

SobekCM’s Community Ecosystems & Socio-Technical Practices

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

Service oriented archive based on Fedora Commons

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

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.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Dynamic Analysis Model of Spherical Roller Bearings with Defects

Rolling element bearings are essential components of rotating machinery. The spherical roller bearing (SRB) is one variant seeing increasing use, because it is self-aligning and can support high loads. It is becoming increasingly important to understand how the SRB responds dynamically under a variety of conditions. This doctoral dissertation introduces a computationally efficient, three-degree-of-freedom, SRB model that was developed to predict the transient dynamic behaviors of a rotor-SRB system. In the model, bearing forces and deflections were calculated as a function of contact deformation and bearing geometry parameters according to nonlinear Hertzian contact theory. The results reveal how some of the more important parameters; such as diametral clearance, the number of rollers, and osculation number; influence ultimate bearing performance. Distributed defects, such as the waviness of the inner and outer ring, and localized defects, such as inner and outer ring defects, are taken into consideration in the proposed model. Simulation results were verified with results obtained by applying the formula for the spherical roller bearing radial deflection and the commercial bearing analysis software. Following model verification, a numerical simulation was carried out successfully for a full rotor-bearing system to demonstrate the application of this newly developed SRB model in a typical real world analysis. Accuracy of the model was verified by comparing measured to predicted behaviors for equivalent systems.