High-Speed Solid-Rotor Induction Machine – Electromagnetic Calculation and Design

Within the latest decade high-speed motor technology has been increasingly commonly applied within the range of medium and large power. More particularly, applications like such involved with gas movement and compression seem to be the most important area in which high-speed machines are used. In manufacturing the induction motor rotor core of one single piece of steel it is possible to achieve an extremely rigid rotor construction for the high-speed motor. In a mechanical sense, the solid rotor may be the best possible rotor construction. Unfortunately, the electromagnetic properties of a solid rotor are poorer than the properties of the traditional laminated rotor of an induction motor. This thesis analyses methods for improving the electromagnetic properties of a solid-rotor induction machine. The slip of the solid rotor is reduced notably if the solid rotor is axially slitted. The slitting patterns of the solid rotor are examined. It is shown how the slitting parameters affect the produced torque. Methods for decreasing the harmonic eddy currents on the surface of the rotor are also examined. The motivation for this is to improve the efficiency of the motor to reach the efficiency standard of a laminated rotor induction motor. To carry out these research tasks the finite element analysis is used. An analytical calculation of solid rotors based on the multi-layer transfer-matrix method is developed especially for the calculation of axially slitted solid rotors equipped with wellconducting end rings. The calculation results are verified by using the finite element analysis and laboratory measurements. The prototype motors of 250 – 300 kW and 140 Hz were tested to verify the results. Utilization factor data are given for several other prototypes the largest of which delivers 1000 kW at 12000 min-1.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Laadun ja oikean tiedon varmistaminen pk-yrityksen tilaus-toimitusketjussa

Oikean tiedon siirtyminen oikeaan aikaan, sekä laadukkaan työn tekeminen yrityksen tilaus-toimitusketjun jokaisessa vaiheessa, ovat avaintekijöitä arvolupauksen ja laadun täyttämiseen asiakkaalle. Diplomityön tavoite on kehittää pk-yritykselle työkalut parempaan tiedon hallintaan ja laadukkaan työn tekemiseen toiminnanohjausjärjestelmässä. Tutkimusmenetelmänä diplomityössä käytettiin toimintatutkimusta, jossa diplomityön tekijä osallistui kohdeyrityksen päivittäiseen työn tekemiseen neljän kuukauden ajan. Tutkimuksen tiedon keräämisessä käytettiin myös puolistrukturoitua haastattelua, sekä kyselytutkimuksella. Tutkimusote työssä on kvalitatiivinen eli laadullinen tutkimusote. Työ koostuu teoriaosasta sekä soveltavasta osasta, jonka jälkeen työn tulokset esitetään tiivistetysti johtopäätöksissä ja yhteenvedossa. Toiminnanohjausjärjestelmät keräävät ja tallentavat tietoa, jota työntekijät ja yrityksen rajapinnoilla työskentelevät ihmiset siihen syöttävät. Onkin äärimmäisen tärkeää, että yrityksellä on kuvatut yhtenäiset toimintamallit prosesseille, joita he käyttävät tiedon tallentamisessa järjestelmiin. Tässä diplomityössä tutkitaan pk-yrityksen nykyiset toimintamallit tiedon tallentamisesta toiminnanohjausjärjestelmään, jonka jälkeen kehitetään yhtenäiset ohjeet toiminnanohjausjärjestelmään syötetystä myyntitilaussopimuksesta. Teoriaosuudessa esitetään laatu eri näkökulmista ja mitä laadunhallintajärjestelmät ovat ja kuinka niitä kehitetään. Teoriaosassa myös avataan tilausohjautuvan tuotannon periaatteet, sekä toiminnanohjausjärjestelmän merkitys liiketoiminnalle. Teoriaosuudella pohjustetaan soveltavaa osuutta, jossa ongelma-analyysin jälkeen kehitetään yritykseen oma laadunhallintajärjestelmä, sekä uudet työmallit tiedonvaihtoon ja sen tallentamiseen. Tuloksena on myös toiminnanohjausjärjestelmän käytön tehostuminen ohjelmistotoimittajan tekemänä. Ohjelmasta karsittiin turhat nimikkeistöt ja sen konfigurointia tehostettiin. Työn tuloksena saatiin työohjeet ydinprosessien suorittamiseen, sekä oma laadunhallintajärjestelmä tukemaan yrityksen ydin- ja tukiprosesseja, sekä tiedonhallintaa.

Developing smart services by Internet of Things in manufacturing business

Manufacturing companies have passed from selling uniquely tangible products to adopting a service-oriented approach to generate steady and continuous revenue streams. Nowadays, equipment and machine manufacturers possess technologies to track and analyze product-related data for obtaining relevant information from customers’ use towards the product after it is sold. The Internet of Things on Industrial environments will allow manufacturers to leverage lifecycle product traceability for innovating towards an information-driven services approach, commonly referred as “Smart Services”, for achieving improvements in support, maintenance and usage processes. The aim of this study is to conduct a literature review and empirical analysis to present a framework that describes a customer-oriented approach for developing information-driven services leveraged by the Internet of Things in manufacturing companies. The empirical study employed tools for the assessment of customer needs for analyzing the case company in terms of information requirements and digital needs. The literature review supported the empirical analysis with a deep research on product lifecycle traceability and digitalization of product-related services within manufacturing value chains. As well as the role of simulation-based technologies on supporting the “Smart Service” development process. The results obtained from the case company analysis show that the customers mainly demand information that allow them to monitor machine conditions, machine behavior on different geographical conditions, machine-implement interactions, and resource and energy consumption. Put simply, information outputs that allow them to increase machine productivity for maximizing yields, save time and optimize resources in the most sustainable way. Based on customer needs assessment, this study presents a framework to describe the initial phases of a “Smart Service” development process, considering the requirements of Smart Engineering methodologies.