Tarkkoihin matkustajatietoihin perustuva hissiohjausjarjestelma

Työssä esitetään geneettisten algoritmien käyttöön perustuva hissiohjausjärjestelmä, jossa ohjauspaatosten tekemisessä hyödynnetään tarkkoja matkustajatietoja. Tämä hissiohjausjärjestelmä soveltuu käytettäväksi muun muassa kohde-allokointiin perus-tuvassa hissijärjestelmässä, jossa matkustajat antavat hissikutsun yhteydessä kohde-kerrostietonsa. Esitetty ohjausjärjestelmä soveltuu käytettäväksi ulkokutsun välittömään tai jatkuvaan allokointiin perustuvassa hissijärjestelmässä. Työn kirjallisessa osuudessa esitetään parannuksia aiemmin esitettyihin hissiohjausjärjestelmiin ja käydään läpi erilaisia kohde-allokointiin perustuvia hissijärjestelmiä. Työssä kuvataan uusi matkustaja-ohjaustapa, joka vähentää matkustajan tekemän hissikutsun välittömään palveluun liittyviä hissiohjausongelmia. Tarkkoja matkustajatietoja hyödyntämällä hissijärjestelmä kykenee sekä tarjoamaan matkustajille yksilöllistä palvelua että kuljettamaan matkustajia tehokkaasti.

Sellupesurin rakenteellinen kehittäminen

Tämä diplomityö on tehty Andritz Oy:lle Washers & Filters tuoteryhmään. Työ on osa pienten sellupesureiden tuotekehitysprojektia. Tavoitteena on vertailla olemassa olevaa tuotekehitysaineistoa ja tuoda esiin suunnitteluprosessi, jolla DD – sellupesurin osien rakenteita voidaan järjestelmällisesti kehittää. Diplomityössä tutkittuja osia ovat tiiviste–elementti, päätypalkki ja rumpu. Tiiviste–elementtejä vertailtiin olemassa olevan tuotekehitysaineiston osalta, sekä tutkittiin geneettisiin algoritmeihin pohjautuvan topologian optimoinnin soveltuvuutta tiiviste-elementin suunnitteluun. Päätypalkin ja rummun optimaaliset geometriat selvitettiin geneettisiä algoritmejä hyödyntävällä topologisella optimoinnilla. Optimaalisten topologioiden perusteella suunniteltiin valmistettavissa olevat rakenteet joiden ainevahvuudet määrättiin alustavasti vakion variointiin perustuvalla optimoinnilla. Tällä menettelyllä saatiin päätypalkista ja rummusta aikaiseksi aikaisempaa kevyemmät rakenteet. Topologian optimointi huomattiin soveltuvan rakenteisiin, joiden kuormitus- ja kiinnitystiedot ovat yksiselitteisesti määrätyt.

Structural and operational optimisation : applications in energy systems

In this work mathematical programming models for structural and operational optimisation of energy systems are developed and applied to a selection of energy technology problems. The studied cases are taken from industrial processes and from large regional energy distribution systems. The models are based on Mixed Integer Linear Programming (MILP), Mixed Integer Non-Linear Programming (MINLP) and on a hybrid approach of a combination of Non-Linear Programming (NLP) and Genetic Algorithms (GA). The optimisation of the structure and operation of energy systems in urban regions is treated in the work. Firstly, distributed energy systems (DES) with different energy conversion units and annual variations of consumer heating and electricity demands are considered. Secondly, district cooling systems (DCS) with cooling demands for a large number of consumers are studied, with respect to a long term planning perspective regarding to given predictions of the consumer cooling demand development in a region. The work comprises also the development of applications for heat recovery systems (HRS), where paper machine dryer section HRS is taken as an illustrative example. The heat sources in these systems are moist air streams. Models are developed for different types of equipment price functions. The approach is based on partitioning of the overall temperature range of the system into a number of temperature intervals in order to take into account the strong nonlinearities due to condensation in the heat recovery exchangers. The influence of parameter variations on the solutions of heat recovery systems is analysed firstly by varying cost factors and secondly by varying process parameters. Point-optimal solutions by a fixed parameter approach are compared to robust solutions with given parameter variation ranges. In the work enhanced utilisation of excess heat in heat recovery systems with impingement drying, electricity generation with low grade excess heat and the use of absorption heat transformers to elevate a stream temperature above the excess heat temperature are also studied.

Evolutionary computing in search-based software engineering

This master’s thesis aims to study and represent from literature how evolutionary algorithms are used to solve different search and optimisation problems in the area of software engineering. Evolutionary algorithms are methods, which imitate the natural evolution process. An artificial evolution process evaluates fitness of each individual, which are solution candidates. The next population of candidate solutions is formed by using the good properties of the current population by applying different mutation and crossover operations. Different kinds of evolutionary algorithm applications related to software engineering were searched in the literature. Applications were classified and represented. Also the necessary basics about evolutionary algorithms were presented. It was concluded, that majority of evolutionary algorithm applications related to software engineering were about software design or testing. For example, there were applications about classifying software production data, project scheduling, static task scheduling related to parallel computing, allocating modules to subsystems, N-version programming, test data generation and generating an integration test order. Many applications were experimental testing rather than ready for real production use. There were also some Computer Aided Software Engineering tools based on evolutionary algorithms.

Internet-lähteistä haettujen uutisten ryhmittely

Tämän diplomityön tarkoituksena on tutkia, mitä vaaditaan uutisten samanlaisuuden automaattiseen tunnistamiseen. Uutiset ovat tekstipohjaisia uutisia, jotka on haettu eri uutislähteistä. Uutisista on tarkoitus tunnistaa ensinnäkin ne uutiset, jotka tarkoittavat samaa asiaa, sekä ne uutiset, jotka eivät ole aivan sama asia, mutta liittyvät kuitenkin toisiinsa. Tässä diplomityössä tutkitaan, millä algoritmeilla tämä tunnistus onnistuu tehokkaimmin sekä suomalaisessa, että englanninkielisessä tekstissä. Diplomityössä vertaillaan valmiita algoritmeja. Tavoitteena on valita sellainen algoritmiyhdistelmä, että 90 % vertailluista uutisista tunnistuu oikein. Tutkimuksessa käytetään 2 eri ryhmittelyalgoritmia, sekä 3 eri stemmaus-algoritmia. Näitä algoritmeja vertaillaan sekä uutisten tunnistustehokkuuden, että niiden suorituskyvyn suhteen. Parhaimmaksi stemmaus-algoritmiksi osoittautui sekä suomen-, että englanninkielisten uutisten vertailussa Porterin algoritmi. Ryhmittely-algoritmeista tehokkaammaksi osoittautui yksinkertaisempi erilaisiin tunnuslukuihin perustuva algoritmi.

Mobile terminal adaptation in portal for better usability

Elektroniset finanssipalvelut, erityisesti Internetin kautta käytettynä, on kasvava alue. Elektronisten finanssipalveluiden tarjoajan tulee pystyä tarjoamaan laaja käytettävyys kaikkien kanavien kautta. Laajan käytettävyyden avulla asiakas voi valita haluamansa kanavan haluamanaan aikana. Palveluntarjoajalla tulee olla joustava arkkitehtuuri pystyäkseen tukemaan asiakkaiden muuttuvia vaatimuksia. Joustavalla arkkitehtuurilla päätelaitteeseen mukautuminen on mahdollista ja näin palveluntarjoaja pystyy tarjoamaan tuen monille eri päätelaitteille ja teknologioille helposti ja nopeasti. Diplomityö keskittyy tutkimaan mahdollisuutta monen kanavan tukeen ja päätelaitteeseen mukautumista Nordean tulevassa finanssiportaaliratkaisussa. Tämä pitäisi olla mahdollista uuden arkkitehtuurin kanssa, jonka TietoEnator on toteuttanut yhteistyössä Nordean kanssa. Sivujen rakenteen uudelleenjärjestelyillä saatiin hyviä tuloksia. Nykyisestä arkkitehtuurissa löydettiin myös puutteita ja jäljelle jäi avoimia kysymyksiä, jotka kirjattiin ylös. On selvästi nähtävissä, että tehokas päätelaitteeseen mukautuminen ja tuki monelle kanavalle tuo hyötyjä sekä pankille että asiakkaalle.

Type II aromatic polyketide biosynthetic tailoring enzymes: diversity and adaptation in Streptomyces secondary metabolism.

Members of the bacterial genus Streptomyces are well known for their ability to produce an exceptionally wide selection of diverse secondary metabolites. These include natural bioactive chemical compounds which have potential applications in medicine, agriculture and other fields of commerce. The outstanding biosynthetic capacity derives from the characteristic genetic flexibility of Streptomyces secondary metabolism pathways: i) Clustering of the biosynthetic genes in chromosome regions redundant for vital primary functions, and ii) the presence of numerous genetic elements within these regions which facilitate DNA rearrangement and transfer between non-progeny species. Decades of intensive genetic research on the organization and function of the biosynthetic routes has led to a variety of molecular biology applications, which can be used to expand the diversity of compounds synthesized. These include techniques which, for example, allow modification and artificial construction of novel pathways, and enable gene-level detection of silent secondary metabolite clusters. Over the years the research has expanded to cover molecular-level analysis of the enzymes responsible for the individual catalytic reactions. In vitro studies of the enzymes provide a detailed insight into their catalytic functions, mechanisms, substrate specificities, interactions and stereochemical determinants. These are factors that are essential for the thorough understanding and rational design of novel biosynthetic routes. The current study is a part of a more extensive research project (Antibiotic Biosynthetic Enzymes; www.sci.utu.fi/projects/biokemia/abe), which focuses on the post-PKS tailoring enzymes involved in various type II aromatic polyketide biosynthetic pathways in Streptomyces bacteria. The initiative here was to investigate specific catalytic steps in anthracycline and angucycline biosynthesis through in vitro biochemical enzyme characterization and structural enzymology. The objectives were to elucidate detailed mechanisms and enzyme-level interactions which cannot be resolved by in vivo genetic studies alone. The first part of the experimental work concerns the homologous polyketide cyclases SnoaL and AknH. These catalyze the closure of the last carbon ring of the tetracyclic carbon frame common to all anthracycline-type compounds. The second part of the study primarily deals with tailoring enzymes PgaE (and its homolog CabE) and PgaM, which are responsible for a cascade of sequential modification reactions in angucycline biosynthesis. The results complemented earlier in vivo findings and confirmed the enzyme functions in vitro. Importantly, we were able to identify the amino acid -level determinants that influence AknH and SnoaL stereoselectivity and to determine the complex biosynthetic steps of the angucycline oxygenation cascade of PgaE and PgaM. In addition, the findings revealed interesting cases of enzyme-level adaptation, as some of the catalytic mechanisms did not coincide with those described for characterised homologs or enzymes of known function. Specifically, SnoaL and AknH were shown to employ a novel acid-base mechanism for aldol condenzation, whereas the hydroxylation reaction catalysed by PgaM involved unexpected oxygen chemistry. Owing to a gene-level fusion of two ancestral reading frames, PgaM was also shown to adopt an unusual quaternary sturucture, a non-covalent fusion complex of two alternative forms of the protein. Furthermore, the work highlighted some common themes encountered in polyketide biosynthetic pathways such as enzyme substrate specificity and intermediate reactivity. These are discussed in the final chapters of the work.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Gene Regulation in The Human Immune System. Gene Expression Signatures of Th2 Cell Differentiation, Type 1 Diabetes, and Intrauterine Immune Adaptation

The human immune system is constantly interacting with the surrounding stimuli and microorganisms. However, when directed against self or harmless antigens, these vital defense mechanisms can cause great damage. In addition, the understanding the underlying mechanism of several human diseases caused by aberrant immune cell functions, for instance type 1 diabetes and allergies, remains far from being complete. In this Ph.D. study these questions were addressed using genome-wide transcriptomic analyses. Asthma and allergies are characterized by a hyperactive response of the T helper 2 (Th2) immune cells. In this study, the target genes of the STAT6 transcription factor in naïve human T cells were identified with RNAi for the first time. STAT6 was shown to act as a central activator of the genes expression upon IL-4 signaling, with both direct and indirect effects on Th2 cell transcriptome. The core transcription factor network induced by IL-4 was identified from a kinetic analysis of the transcriptome. Type 1 diabetes is an autoimmune disease influenced by both the genetic susceptibility of an individual and the disease-triggering environmental factors. To improve understanding of the autoimmune processes driving pathogenesis in the prediabetic phase in humans, a unique series of prospective whole-blood RNA samples collected from HLA-susceptible children in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study was studied. Changes in different timewindows of the pathogenesis process were identified, and especially the type 1 interferon response was activated early and throughout the preclinical T1D. The hygiene hypothesis states that allergic diseases, and lately also autoimmune diseases, could be prevented by infections and other microbial contacts acquired in early childhood, or even prenatally. To study the effects of the standard of hygiene on the development of neonatal immune system, cord blood samples from children born in Finland (high standard of living), Estonia (rapid economic growth) and Russian Karelia (low standard of living) were compared. Children born in Russian Karelia deviated from Finnish and Estonian children in many aspects of the neonatal immune system, which was developmentally more mature in Karelia, resembling that of older infants. The results of this thesis offer significant new information on the regulatory networks associated with immune-mediated diseases in human. The results will facilitate understanding and further research on the role of the identified target genes and mechanisms driving the allergic inflammation and type 1 diabetes, hopefully leading to a new era of drug development.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.