Tuottojen tunnistaminen ja kustannuskertymän ennustaminen pitkäaikaishankkeissa: tapaustutkimus

Tutkimuksen tavoitteena oli kehittää Larox Oyj:n alihankintaprojektien kustannuslaskentaa. Yrityksessä oli havaittu, että pitkäaikaishankkeiden kustan-nusten kertymistä pitää pystyä ennustamaan tarkemmin. Konstruktiivisen tutkimusotteen mukaisesti tutkimuksessa luotiin esiymmärrys kohdeyrityksen nykytilanteesta, perehdyttiin vaikuttavaan lainsäädäntöön ja aikaisempaan tutkimustietoon. Kuvaus nykytilanteesta luotiin kohdeyrityksen ja alihankkijan edustajien haastatteluiden avulla, tutustumalla yrityksen toimintaohjeisiin ja keräämällä tietoa projektien kustannusten kertymisestä. Kerätyn tiedon perusteella luotiin konstruktiot eli ratkaisuehdotukset toiminnan kehittämiseksi. Tutkimuksessa kehitettiin raportointimalli alihankintaprojektien edistymisen raportointiin. Mallin tavoitteena on yhtenäistää alihankkijoiden raportointikäy-täntöjä ja tuottaa sellaista tietoa, jota Larox tarvitsee tuottojen tunnistamista varten. Toinen konkreettinen ratkaisu on alihankintaprojektien kustannuskertymän ennustetyökalu, jonka avulla voidaan ennakoida hankkeen valmiusasteen kehitystä projektin aikana. Malli on rakennettu yhden konetyypin projektien ennustamiseen, mutta siitä voidaan helposti muokata ennustemallit muidenkin projektityyppien tarpeisiin. Tarkempien ennusteiden avulla voidaan kehittää johdon raportointia ja parantaa kassavirtojen ennustettavuutta.

Revenue Logic inSoftware Business Context

Tutkimuksen tavoitteena oli selvittää ohjelmistotoimialan avaintekijöitä, jotka vaikuttavat yrityksen ansaintalogiikkaan sekä lisätä tietoisuutta ansaintalogiikan muodostumisesta pienissä ja keskisuurissa ohjelmistoyrityksissä. Tutkimuksen teoreettisessa osassa keskityttiin tarkastelemaan ansaintalogiikan, strategian ja liiketoimintamallin käsitteiden suhteita sekä arvioitiin toimialan osatekijöiden, hinnoitteluperiaatteiden ja ansaintamallien vaikutusta ansainnan muodostumiseen ohjelmistotoimialalla. Ohjelmistotuote ja - palveluliiketoimintaa koskien oli merkityksellistä tutkia tuotteistamisasteen ja arvoketjujen vaikutusta ansaintalogiikan muodostumisessa sekä esitellä erilaisia, tyypillisiä ohjelmistotoimialalla käytettäviä hinnoittelumenetelmiä. Työn empiirisessä osassa tarkasteltiin 23 suomalaisen ohjelmistoalan yrityksen ansaintalogiikkaa. Tiedot kerättiin haastatteluin ja analysoitiin laadullisen tutkimuksen keinoin. Tutkimustulokset korostivat ansaintalogiikan 'epämääräisyyttä' terminä mutta osoittivat, että ydinliiketoimintaan keskittyminen, tuote-, palvelu-, tai projektiliiketoiminnan osaaminen, tuotteistusaste ja kanavavalinnat ovat avaintekijöitä ansaintalogiikanmuodostumisessa. Ansaintalogiikan muodostamiseen liittyy paljon yrityksen sisäisiä ja ulkoisia haasteita sekä muutospaineita, eikä ohjelmistotoimialalla ole todennettavissa yhtä yleismaailmallista, menestyksen takaavaa ansaintalogiikkaa.

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.

Optical Character Recognition for Symbian OS

This Master's thesis addresses the design and implementation of the optical character recognition (OCR) system for a mobile device working on the Symbian operating system. The developed OCR system, named OCRCapriccio, emphasizes the modularity, effective extensibility and reuse. The system consists of two parts which are the graphical user interface and the OCR engine that was implemented as a plug-in. In fact, the plug-in includes two implementations of the OCR engine for enabling two types of recognition: the bitmap comparison based recognition and statistical recognition. The implementation results have shown that the approach based on bitmap comparison is more suitable for the Symbian environment because of its nature. Although the current implementation of bitmap comparison is lacking in accuracy, further development should be done in its direction. The biggest challenges of this work were related to developing an OCR scheme that would be suitable for Symbian OS Smartphones that have limited computational power and restricted resources.

Technological and business challenges of smart grids

Growing recognition of the electricity grid modernization to enable new electricity generation and consumption schemes has found articulation in the vision of the Smart Grid platform. The essence of this vision is an autonomous network with two-way electricity power flows and extensive real-time information between the generation nodes, various electricity-dependent appliances and all points in-between. Three major components of the Smart Grids are distributed intelligence, communication technologies, and automated control systems. The aim of this thesis is to recognize the challenges that Smart Grids are facing, while extinguishing the main driving factors for their introduction. The scope of the thesis also covers possible place of electricity Aggregator Company in the current and future electricity markets. Basic functions of an aggregator and possible revenue sources along with demand response feasibility calculations are reviewed within this thesis.

Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Future revenue models: Transformation from textbooks to digital solutions in the educational publishing industry, Case Sanoma Learning

This study was conducted in order to learn how companies’ revenue models will be transformed due to the digitalisation of its products and processes. Because there is still only a limited number of researches focusing solely on revenue models, and particularly on the revenue model change caused by the changes at the business environment, the topic was initially approached through the business model concept, which organises the different value creating operations and resources at a company in order to create profitable revenue streams. This was used as the base for constructing the theoretical framework for this study, used to collect and analyse the information. The empirical section is based on a qualitative study approach and multiple-case analysis of companies operating in learning materials publishing industry. Their operations are compared with companies operating in other industries, which have undergone comparable transformation, in order to recognise either similarities or contrasts between the cases. The sources of evidence are a literature review to find the essential dimensions researched earlier, and interviews 29 of managers and executives at 17 organisations representing six industries. Based onto the earlier literature and the empirical findings of this study, the change of the revenue model is linked with the change of the other dimen-sions of the business model. When one dimension will be altered, as well the other should be adjusted accordingly. At the case companies the transformation is observed as the utilisation of several revenue models simultaneously and the revenue creation processes becoming more complex.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Additional revenue opportunities in pulp mills and their impacts on the kraft process

Although the concept of multi-products biorefinery provides an opportunity to meet the future demands for biofuels, biomaterials or chemicals, it is not assured that its implementation would improve the profitability of kraft pulp mills. The attractiveness will depend on several factors such as mill age and location, government incentives, economy of scale, end user requirements, and how much value can be added to the new products. In addition, the effective integration of alternative technologies is not straightforward and has to be carefully studied. In this work, detailed balances were performed to evaluate possible impacts that lignin removal, hemicelluloses recovery prior to pulping, torrefaction and pyrolysis of wood residues cause on the conventional mill operation. The development of mill balances was based on theoretical fundamentals, practical experience, literature review, personal communication with technology suppliers and analysis of mill process data. Hemicelluloses recovery through pre-hydrolysis of chips leads to impacts in several stages of the kraft process. Effects can be observed on the pulping process, wood consumption, black liquor properties and, inevitably, on the pulp quality. When lignin is removed from black liquor, it will affect mostly the chemical recovery operation and steam generation rate. Since mineral acid is used to precipitate the lignin, impacts on the mill chemical balance are also expected. A great advantage of processing the wood residues for additional income results from the fact that the pulping process, pulp quality and sales are not harmfully affected. For pulp mills interested in implementing the concept of multi-products biorefinery, this work has indicated possible impacts to be considered in a technical feasibility study.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Recognition of nucleic acids by metal ion complexes

Metal-ion-mediated base-pairing of nucleic acids has attracted considerable attention during the past decade, since it offers means to expand the genetic code by artificial base-pairs, to create predesigned molecular architecture by metal-ion-mediated inter- or intra-strand cross-links, or to convert double stranded DNA to a nano-scale wire. Such applications largely depend on the presence of a modified nucleobase in both strands engaged in the duplex formation. Hybridization of metal-ion-binding oligonucleotide analogs with natural nucleic acid sequences has received much less attention in spite of obvious applications. While the natural oligonucleotides hybridize with high selectivity, their affinity for complementary sequences is inadequate for a number of applications. In the case of DNA, for example, more than 10 consecutive Watson-Crick base pairs are required for a stable duplex at room temperature, making targeting of sequences shorter than this challenging. For example, many types of cancer exhibit distinctive profiles of oncogenic miRNA, the diagnostics of which is, however, difficult owing to the presence of only short single stranded loop structures. Metallo-oligonucleotides, with their superior affinity towards their natural complements, would offer a way to overcome the low stability of short duplexes. In this study a number of metal-ion-binding surrogate nucleosides were prepared and their interaction with nucleoside 5´-monophosphates (NMPs) has been investigated by 1H NMR spectroscopy. To find metal ion complexes that could discriminate between natural nucleobases upon double helix formation, glycol nucleic acid (GNA) sequences carrying a PdII ion with vacant coordination sites at a predetermined position were synthesized and their affinity to complementary as well as mismatched counterparts quantified by UV-melting measurements.

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.