Applications and Markets of Linear Motors

A steady increase in practical industrial applications has secured a place for linear motors. They provide high dynamics and high positioning accuracy of the motor, high reliability and durability of all components of the system. Machines with linear motors have very big perspectives in modern industry. This thesis enables to understand what a linear motor is, where they are used and what situation there is on their market nowadays. It can help to understand reasonability of applying linear motors on manufacture and benefits of its application.

Design and Analysis of Forward Error Control Coding and Signaling for Guaranteeing QoS in Wireless Broadcast Systems

Broadcasting systems are networks where the transmission is received by several terminals. Generally broadcast receivers are passive devices in the network, meaning that they do not interact with the transmitter. Providing a certain Quality of Service (QoS) for the receivers in heterogeneous reception environment with no feedback is not an easy task. Forward error control coding can be used for protection against transmission errors to enhance the QoS for broadcast services. For good performance in terrestrial wireless networks, diversity should be utilized. The diversity is utilized by application of interleaving together with the forward error correction codes. In this dissertation the design and analysis of forward error control and control signalling for providing QoS in wireless broadcasting systems are studied. Control signaling is used in broadcasting networks to give the receiver necessary information on how to connect to the network itself and how to receive the services that are being transmitted. Usually control signalling is considered to be transmitted through a dedicated path in the systems. Therefore, the relationship of the signaling and service data paths should be considered early in the design phase. Modeling and simulations are used in the case studies of this dissertation to study this relationship. This dissertation begins with a survey on the broadcasting environment and mechanisms for providing QoS therein. Then case studies present analysis and design of such mechanisms in real systems. The mechanisms for providing QoS considering signaling and service data paths and their relationship at the DVB-H link layer are analyzed as the first case study. In particular the performance of different service data decoding mechanisms and optimal signaling transmission parameter selection are presented. The second case study investigates the design of signaling and service data paths for the more modern DVB-T2 physical layer. Furthermore, by comparing the performances of the signaling and service data paths by simulations, configuration guidelines for the DVB-T2 physical layer signaling are given. The presented guidelines can prove useful when configuring DVB-T2 transmission networks. Finally, recommendations for the design of data and signalling paths are given based on findings from the case studies. The requirements for the signaling design should be derived from the requirements for the main services. Generally, these requirements for signaling should be more demanding as the signaling is the enabler for service reception.

Centralized Motion Control of a Linear Tooth Belt Drive: Analysis of the Performance and Limitations

A centralized robust position control for an electrical driven tooth belt drive is designed in this doctoral thesis. Both a cascaded control structure and a PID based position controller are discussed. The performance and the limitations of the system are analyzed and design principles for the mechanical structure and the control design are given. These design principles are also suitable for most of the motion control applications, where mechanical resonance frequencies and control loop delays are present. One of the major challenges in the design of a controller for machinery applications is that the values of the parameters in the system model (parameter uncertainty) or the system model it self (non-parametric uncertainty) are seldom known accurately in advance. In this thesis a systematic analysis of the parameter uncertainty of the linear tooth beltdrive model is presented and the effect of the variation of a single parameter on the performance of the total system is shown. The total variation of the model parameters is taken into account in the control design phase using a Quantitative Feedback Theory (QFT). The thesis also introduces a new method to analyze reference feedforward controllers applying the QFT. The performance of the designed controllers is verified by experimentalmeasurements. The measurements confirm the control design principles that are given in this thesis.

Design and analysis of a transversal-flux switched-reluctance-linear-machine pole-pair

The Switched Reluctance technology is probably best suited for industrial low-speed or zerospeed applications where the power can be small but the torque or the force in linear movement cases might be relatively high. Because of its simple structure the SR-motor is an interesting alternative for low power applications where pneumatic or hydraulic linear drives are to be avoided. This study analyses the basic parts of an LSR-motor which are the two mover poles and one stator pole and which form the “basic pole pair” in linear-movement transversal-flux switchedreluctance motors. The static properties of the basic pole pair are modelled and the basic design rules are derived. The models developed are validated with experiments. A one-sided one-polepair transversal-flux switched-reluctance-linear-motor prototype is demonstrated and its static properties are measured. The modelling of the static properties is performed with FEM-calculations. Two-dimensional models are accurate enough to model the static key features for the basic dimensioning of LSRmotors. Three-dimensional models must be used in order to get the most accurate calculation results of the static traction force production. The developed dimensioning and modelling methods, which could be systematically validated by laboratory measurements, are the most significant contributions of this thesis.

Towards input/output-free modelling of linear infinite-dimensional systems in continuous time

This dissertation describes a networking approach to infinite-dimensional systems theory, where there is a minimal distinction between inputs and outputs. We introduce and study two closely related classes of systems, namely the state/signal systems and the port-Hamiltonian systems, and describe how they relate to each other. Some basic theory for these two classes of systems and the interconnections of such systems is provided. The main emphasis lies on passive and conservative systems, and the theoretical concepts are illustrated using the example of a lossless transfer line. Much remains to be done in this field and we point to some directions for future studies as well.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

The prognostic and predictive value of selected biomarkers in colorectal cancer

Tissue-based biomarkers are studied to receive information about the pathologic processes and cancer outcome, and to enable development of patient-tailored treatments. The aim of this study was to investigate the potential prognostic and/or predictive value of selected biomarkers in colorectal cancer (CRC). Group IIA secretory phospholipase A2 (IIA PLA2) expression was assessed in 114 samples presenting different phases of human colorectal carcinogenesis. Securin, Ki-67, CD44 variant 6 (CD44v6), aldehyde dehydrogenase 1 (ALDH1) and β-catenin were studied in a material including 227 rectal carcinoma patients treated with short-course preoperative radiotherapy (RT), long-course preoperative (chemo)RT (CRT) or surgery only. Epidermal growth factor receptor (EGFR) gene copy number (GCN), its heterogeneity in CRC tissue, and association with response to EGFR-targeted antibodies cetuximab and panitumumab were analyzed in a cohort of 76 metastatic CRC. IIA PLA2 expression was decreased in invasive carcinomas compared to adenomas, but did not relate to patient survival. High securin expression after long-course (C)RT and high ALDH1 expression in node-negative rectal cancer were independent adverse prognostic factors, ALDH1 specifically in patients treated with adjuvant chemotherapy. The lack of membranous CD44v6 in the rectal cancer invasive front associated with infiltrative growth pattern and the risk of disease recurrence. Heterogeneous EGFR GCN increase predicted benefit from EGFR-targeted antibodies, also in the chemorefractory patient population. In summary, high securin and ALDH1 protein expression independently relate to poor outcome in subgroups of rectal cancer patients, potentially because of resistance to conventional chemotherapeutics. Heterogeneous increase in EGFR GCN was validated to be a promising predictive factor in the treatment of metastatic CRC.

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

Linear prediction in optical spectroscopy

A linear prediction procedure is one of the approved numerical methods of signal processing. In the field of optical spectroscopy it is used mainly for extrapolation known parts of an optical signal in order to obtain a longer one or deduce missing signal samples. The first is needed particularly when narrowing spectral lines for the purpose of spectral information extraction. In the present paper the coherent anti-Stokes Raman scattering (CARS) spectra were under investigation. The spectra were significantly distorted by the presence of nonlinear nonresonant background. In addition, line shapes were far from Gaussian/Lorentz profiles. To overcome these disadvantages the maximum entropy method (MEM) for phase spectrum retrieval was used. The obtained broad MEM spectra were further underwent the linear prediction analysis in order to be narrowed.

Comparison of Different Concentrated Solar Power Collector Designs and Development of a Linear Fresnel Solar Collector Model

Concentrated solar power (CSP) is a renewable energy technology, which could contribute to overcoming global problems related to pollution emissions and increasing energy demand. CSP utilizes solar irradiation, which is a variable source of energy. In order to utilize CSP technology in energy production and reliably operate a solar field including thermal energy storage system, dynamic simulation tools are needed in order to study the dynamics of the solar field, to optimize production and develop control systems. The object of this Master’s Thesis is to compare different concentrated solar power technologies and configure a dynamic solar field model of one selected CSP field design in the dynamic simulation program Apros, owned by VTT and Fortum. The configured model is based on German Novatec Solar’s linear Fresnel reflector design. Solar collector components including dimensions and performance calculation were developed, as well as a simple solar field control system. The preliminary simulation results of two simulation cases under clear sky conditions were good; the desired and stable superheated steam conditions were maintained in both cases, while, as expected, the amount of steam produced was reduced in the case having lower irradiation conditions. As a result of the model development process, it can be concluded, that the configured model is working successfully and that Apros is a very capable and flexible tool for configuring new solar field models and control systems and simulating solar field dynamic behaviour.

Stability and predictive ability of beta

The purpose of this paper is to examine the stability and predictive abilities of the beta coefficients of individual equities in the Finnish stock market. As beta is widely used in several areas of finance, including risk management, asset pricing and performance evaluation among others, it is important to understand its characteristics and find out whether its estimates can be trusted and utilized.