Impingement jet cooling of end windings in a high-speed electric machine

The rotational speed of high-speed electric machines is over 15 000 rpm. These machines are compact in size when compared to the power rate. As a consequence, the heat fluxes are at a high level and the adequacy of cooling becomes an important design criterion. In the high-speed machines, the air gap between the stator and rotor is a narrow flow channel. The cooling air is produced with a fan and the flow is then directed to the air gap. The flow in the gap does not provide sufficient cooling for the stator end windings, and therefore additional cooling is required. This study investigates the heat transfer and flow fields around the coil end windings when cooling jets are used. As a result, an innovative and new assembly is introduced for the cooling jets, with the benefits of a reduced amount of hot spots, a lower pressure drop, and hence a lower power need for the cooling fan. The gained information can also be applied to improve the cooling of electric machines through geometry modifications. The objective of the research is to determine the locations of the hot spots and to find out induced pressure losses with different jet alternatives. Several possibilities to arrange the extra cooling are considered. In the suggested approach cooling is provided by using a row of air jets. The air jets have three main tasks: to cool the coils effectively by direct impingement jets, to increase and cool down the flow that enters the coil end space through the air gap, and to ensure the correct distribution of the flow by forming an air curtain with additional jets. One important aim of this study is the arrangement of cooling jets in such manner that hot spots can be avoided to wide extent. This enables higher power density in high-speed motors. This cooling system can also be applied to the ordinary electric machines when efficient cooling is needed. The numerical calculations have been performed using a commercial Computational Fluid Dynamics software. Two geometries have been generated: cylindrical for the studied machine and Cartesian for the experimental model. The main parameters include the positions, arrangements and number of jets, the jet diameters, and the jet velocities. The investigated cases have been tested with two widely used turbulence models and using a computational grid of over 500 000 cells. The experimental tests have been made by using a simplified model for the end winding space with cooling jets. In the experiments, an emphasis has been given to flow visualisation. The computational analysis shows good agreement with the experimental results. Modelling of the cooling jet arrangement enables also a better understanding of the complex system of heat transfer at end winding space.

Studies on integrating kinematic design method with mechanical systems simulation techniques

Theultimate goal of any research in the mechanism/kinematic/design area may be called predictive design, ie the optimisation of mechanism proportions in the design stage without requiring extensive life and wear testing. This is an ambitious goal and can be realised through development and refinement of numerical (computational) technology in order to facilitate the design analysis and optimisation of complex mechanisms, mechanical components and systems. As a part of the systematic design methodology this thesis concentrates on kinematic synthesis (kinematic design and analysis) methods in the mechanism synthesis process. The main task of kinematic design is to find all possible solutions in the form of structural parameters to accomplish the desired requirements of motion. Main formulations of kinematic design can be broadly divided to exact synthesis and approximate synthesis formulations. The exact synthesis formulation is based in solving n linear or nonlinear equations in n variables and the solutions for the problem areget by adopting closed form classical or modern algebraic solution methods or using numerical solution methods based on the polynomial continuation or homotopy. The approximate synthesis formulations is based on minimising the approximation error by direct optimisation The main drawbacks of exact synthesis formulationare: (ia) limitations of number of design specifications and (iia) failure in handling design constraints- especially inequality constraints. The main drawbacks of approximate synthesis formulations are: (ib) it is difficult to choose a proper initial linkage and (iib) it is hard to find more than one solution. Recentformulations in solving the approximate synthesis problem adopts polynomial continuation providing several solutions, but it can not handle inequality const-raints. Based on the practical design needs the mixed exact-approximate position synthesis with two exact and an unlimited number of approximate positions has also been developed. The solutions space is presented as a ground pivot map but thepole between the exact positions cannot be selected as a ground pivot. In this thesis the exact synthesis problem of planar mechanism is solved by generating all possible solutions for the optimisation process ¿ including solutions in positive dimensional solution sets - within inequality constraints of structural parameters. Through the literature research it is first shown that the algebraic and numerical solution methods ¿ used in the research area of computational kinematics ¿ are capable of solving non-parametric algebraic systems of n equations inn variables and cannot handle the singularities associated with positive-dimensional solution sets. In this thesis the problem of positive-dimensional solutionsets is solved adopting the main principles from mathematical research area of algebraic geometry in solving parametric ( in the mathematical sense that all parameter values are considered ¿ including the degenerate cases ¿ for which the system is solvable ) algebraic systems of n equations and at least n+1 variables.Adopting the developed solution method in solving the dyadic equations in direct polynomial form in two- to three-precision-points it has been algebraically proved and numerically demonstrated that the map of the ground pivots is ambiguousand that the singularities associated with positive-dimensional solution sets can be solved. The positive-dimensional solution sets associated with the poles might contain physically meaningful solutions in the form of optimal defectfree mechanisms. Traditionally the mechanism optimisation of hydraulically driven boommechanisms is done at early state of the design process. This will result in optimal component design rather than optimal system level design. Modern mechanismoptimisation at system level demands integration of kinematic design methods with mechanical system simulation techniques. In this thesis a new kinematic design method for hydraulically driven boom mechanism is developed and integrated in mechanical system simulation techniques. The developed kinematic design method is based on the combinations of two-precision-point formulation and on optimisation ( with mathematical programming techniques or adopting optimisation methods based on probability and statistics ) of substructures using calculated criteria from the system level response of multidegree-of-freedom mechanisms. Eg. by adopting the mixed exact-approximate position synthesis in direct optimisation (using mathematical programming techniques) with two exact positions and an unlimitednumber of approximate positions the drawbacks of (ia)-(iib) has been cancelled.The design principles of the developed method are based on the design-tree -approach of the mechanical systems and the design method ¿ in principle ¿ is capable of capturing the interrelationship between kinematic and dynamic synthesis simultaneously when the developed kinematic design method is integrated with the mechanical system simulation techniques.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Development of advanced silicon radiation detectors for harsh radiation environment

This thesis describes the development of advanced silicon radiation detectors and their characterization by simulations, used in the work for searching elementary particles in the European Organization for Nuclear Research, CERN. Silicon particle detectors will face extremely harsh radiation in the proposed upgrade of the Large Hadron Collider, the future high-energy physics experiment Super-LHC. The increase in the maximal fluence and the beam luminosity up to 1016 neq / cm2 and 1035 cm-2s-1 will require detectors with a dramatic improvement in radiation hardness, when such a fluence will be far beyond the operational limits of the present silicon detectors. The main goals of detector development concentrate on minimizing the radiation degradation. This study contributes mainly to the device engineering technology for developing more radiation hard particle detectors with better characteristics. Also the defect engineering technology is discussed. In the nearest region of the beam in Super-LHC, the only detector choice is 3D detectors, or alternatively replacing other types of detectors every two years. The interest in the 3D silicon detectors is continuously growing because of their many advantages as compared to conventional planar detectors: the devices can be fully depleted at low bias voltages, the speed of the charge collection is high, and the collection distances are about one order of magnitude less than those of planar technology strip and pixel detectors with electrodes limited to the detector surface. Also the 3D detectors exhibit high radiation tolerance, and thus the ability of the silicon detectors to operate after irradiation is increased. Two parameters, full depletion voltage and electric field distribution, is discussed in more detail in this study. The full depletion of the detector is important because the only depleted area in the detector is active for the particle tracking. Similarly, the high electric field in the detector makes the detector volume sensitive, while low-field areas are non-sensitive to particles. This study shows the simulation results of full depletion voltage and the electric field distribution for the various types of 3D detectors. First, the 3D detector with the n-type substrate and partial-penetrating p-type electrodes are researched. A detector of this type has a low electric field on the pixel side and it suffers from type inversion. Next, the substrate is changed to p-type and the detectors having electrodes with one doping type and the dual doping type are examined. The electric field profile in a dual-column 3D Si detector is more uniform than that in the single-type column 3D detector. The dual-column detectors are the best in radiation hardness because of their low depletion voltages and short drift distances.

Managing regulatory risks when outsourcing network-related services in the electricity distribution sector

Deregulation of the electricity sector liberated the electricity sale and production for competitive forces while in the network business, electricity transmission and distribution, natural monopoly positions were recognised. Deregulation was accompanied by efficiencyoriented thinking on the whole electricity supply industry. For electricity distribution this meant a transition from a public service towards profit-driven business guided by economic regulation. Regulation is the primary means to enforce societal and other goals in the regulated monopoly sector. The design of economic regulation is concerned with two main attributes; end-customer price and quality of electricity distribution services. Regulation limits the costs of the regulated company but also defines the desired quality of monopoly services. The characteristics of the regulatory framework and the incentives it provides are therefore decisive for the electricity distribution sector. Regulation is not a static factor; changes in the regulatory practices cause discontinuity points, which in turn generate risks. A variety of social and environmental concerns together with technological advancements have emphasised the relevance of quality regulation, which is expected to lead to the large-scale replacement of overhead lines with underground cables. The electricity network construction activity is therefore currently witnessing revolutionary changes in its competitive landscape. In a business characterised by high statutory involvement and a high level of sunk costs, recognising and understanding the regulatory risks becomes a key success factor. As a response, electricity distribution companies have turned into outsourcing to attain efficiency and quality goals. This doctoral thesis addresses the impacts of regulatory risks on electricity network construction, which is a commonly outsourced activity in the electricity distribution network sector. The chosen research approach is characterised as an action analytical research on account of the fact that regulatory risks are greatly dependent on the individual nature of the regulatory regime applied in the electricity distribution sector. The main contribution of this doctoral thesis is to develop a concept for recognising and managing the business risks stemming from economic regulation. The degree of outsourcing in the sector is expected to increase in years to come. The results of the research provide new knowledge to manage the regulatory risks when outsourcing services.

Magnetic Resonance Experiments with Atomic Hydrogen

In this thesis three experiments with atomic hydrogen (H) at low temperatures T<1 K are presented. Experiments were carried out with two- (2D) and three-dimensional (3D) H gas, and with H atoms trapped in solid H2 matrix. The main focus of this work is on interatomic interactions, which have certain specific features in these three systems considered. A common feature is the very high density of atomic hydrogen, the systems are close to quantum degeneracy. Short range interactions in collisions between atoms are important in gaseous H. The system of H in H₂ differ dramatically because atoms remain fixed in the H₂ lattice and properties are governed by long-range interactions with the solid matrix and with H atoms. The main tools in our studies were the methods of magnetic resonance, with electron spin resonance (ESR) at 128 GHz being used as the principal detection method. For the first time in experiments with H in high magnetic fields and at low temperatures we combined ESR and NMR to perform electron-nuclear double resonance (ENDOR) as well as coherent two-photon spectroscopy. This allowed to distinguish between different types of interactions in the magnetic resonance spectra. Experiments with 2D H gas utilized the thermal compression method in homogeneous magnetic field, developed in our laboratory. In this work methods were developed for direct studies of 3D H at high density, and for creating high density samples of H in H₂. We measured magnetic resonance line shifts due to collisions in the 2D and 3D H gases. First we observed that the cold collision shift in 2D H gas composed of atoms in a single hyperfine state is much smaller than predicted by the mean-field theory. This motivated us to carry out similar experiments with 3D H. In 3D H the cold collision shift was found to be an order of magnitude smaller for atoms in a single hyperfine state than that for a mixture of atoms in two different hyperfine states. The collisional shifts were found to be in fair agreement with the theory, which takes into account symmetrization of the wave functions of the colliding atoms. The origin of the small shift in the 2D H composed of single hyperfine state atoms is not yet understood. The measurement of the shift in 3D H provides experimental determination for the difference of the scattering lengths of ground state atoms. The experiment with H atoms captured in H2 matrix at temperatures below 1 K originated from our work with H gas. We found out that samples of H in H2 were formed during recombination of gas phase H, enabling sample preparation at temperatures below 0.5 K. Alternatively, we created the samples by electron impact dissociation of H₂ molecules in situ in the solid. By the latter method we reached highest densities of H atoms reported so far, 3.5(5)x10¹⁹ cm^-3. The H atoms were found to be stable for weeks at temperatures below 0.5 K. The observation of dipolar interaction effects provides a verification for the density measurement. Our results point to two different sites for H atoms in H2 lattice. The steady-state nuclear polarizations of the atoms were found to be non-thermal. The possibility for further increase of the impurity H density is considered. At higher densities and lower temperatures it might be possible to observe phenomena related to quantum degeneracy in solid.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Epäsuoran kysyntätiedon hyödyntäminen verkkokaupan tuotevalikoima- ja hinnoittelupäätöksissä

Tässä diplomityössä tutkitaan, miten verkkokaupan kävijävirran käyttäytymistä analysoimalla voidaan tehdä perusteltuja, tarkoituksenmukaisiin nimikkeisiin ja niiden parametreihin kohdistuvia päätöksiä tilanteessa, jossa laajamittaisemmat historiatiedot toteutuneesta myynnistä puuttuvat. Teoriakatsauksen perusteella muodostettiin ratkaisumalli, joka perustuu potentiaalisten kysyntäajurien muodostamiseen ja testaamiseen. Testisarjan perusteella valittavaa ajuria käytetään estimoimaan nimikkeiden kysyntää, jolloin sitä voidaan käyttää toteutuneen myynnin sijasta esimerkiksi Pareto-analyysissä. Näin huomio on mahdollista keskittää rajattuun määrään merkitykseltään suuria nimikkeitä ja niiden yksityiskohtaisiin parametreihin, joilla on merkitystä asiakkaan ostopäätöstilanteissa. Lisäksi voidaan tunnistaa nimikkeitä, joiden ongelmana on joko huono verkkonäkyvyys tai yhteensopimattomuus asiakastarpeiden kanssa. Ajurien testaamisperiaatteena käytetään kertymäfunktioiden yhdenmukaisuustarkastelua, joka rakentuu kolmesta peräkkäisestä vaiheesta; visuaalisesta tarkastelusta, kahden otoksen 2-suuntaisesta Kolmogorov-Smirnov-yhteensopivuustestistä ja Pearsonin korrelaatiotestistä. Mallia ja sen avulla tuotettua kysynnän ajuria testattiin veneilyalan kuluttaja-asiakkaille suunnatussa verkkokaupassa, jossa sillä tunnistettiin Pareto-jakauman alkupäästä runsaasti nimikkeitä, joiden parametreissa oli myynnin kannalta epäedullisia tekijöitä. Jakauman toisessa päässä tunnistettiin satoja nimikkeitä, joiden ongelmana on ilmeisesti joko huono verkkonäkyvyys tai nimikkeiden yhteensopimattomuus asiakastarpeiden kanssa.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Analytic evaluation of three-phase short circuit demagnetization and hysteresis loss risk in rotor-surface-magnet Permanent-Magnet Synchronous Machines

Permanent magnet synchronous machines (PMSM) have become widely used in applications because of high efficiency compared to synchronous machines with exciting winding or to induction motors. This feature of PMSM is achieved through the using the permanent magnets (PM) as the main excitation source. The magnetic properties of the PM have significant influence on all the PMSM characteristics. Recent observations of the PM material properties when used in rotating machines revealed that in all PMSMs the magnets do not necessarily operate in the second quadrant of the demagnetization curve which makes the magnets prone to hysteresis losses. Moreover, still no good analytical approach has not been derived for the magnetic flux density distribution along the PM during the different short circuits faults. The main task of this thesis is to derive simple analytical tool which can predict magnetic flux density distribution along the rotor-surface mounted PM in two cases: during normal operating mode and in the worst moment of time from the PM’s point of view of the three phase symmetrical short circuit. The surface mounted PMSMs were selected because of their prevalence and relatively simple construction. The proposed model is based on the combination of two theories: the theory of the magnetic circuit and space vector theory. The comparison of the results in case of the normal operating mode obtained from finite element software with the results calculated with the proposed model shows good accuracy of model in the parts of the PM which are most of all prone to hysteresis losses. The comparison of the results for three phase symmetrical short circuit revealed significant inaccuracy of the proposed model compared with results from finite element software. The analysis of the inaccuracy reasons was provided. The impact on the model of the Carter factor theory and assumption that air have permeability of the PM were analyzed. The propositions for the further model development are presented.

Aircraft tracking and classification with vhf passive bistatic radar

Since the times preceding the Second World War the subject of aircraft tracking has been a core interest to both military and non-military aviation. During subsequent years both technology and configuration of the radars allowed the users to deploy it in numerous fields, such as over-the-horizon radar, ballistic missile early warning systems or forward scatter fences. The latter one was arranged in a bistatic configuration. The bistatic radar has continuously re-emerged over the last eighty years for its intriguing capabilities and challenging configuration and formulation. The bistatic radar arrangement is used as the basis of all the analyzes presented in this work. The aircraft tracking method of VHF Doppler-only information, developed in the first part of this study, is solely based on Doppler frequency readings in relation to time instances of their appearance. The corresponding inverse problem is solved by utilising a multistatic radar scenario with two receivers and one transmitter and using their frequency readings as a base for aircraft trajectory estimation. The quality of the resulting trajectory is then compared with ground-truth information based on ADS-B data. The second part of the study deals with the developement of a method for instantaneous Doppler curve extraction from within a VHF time-frequency representation of the transmitted signal, with a three receivers and one transmitter configuration, based on a priori knowledge of the probability density function of the first order derivative of the Doppler shift, and on a system of blocks for identifying, classifying and predicting the Doppler signal. The extraction capabilities of this set-up are tested with a recorded TV signal and simulated synthetic spectrograms. Further analyzes are devoted to more comprehensive testing of the capabilities of the extraction method. Besides testing the method, the classification of aircraft is performed on the extracted Bistatic Radar Cross Section profiles and the correlation between them for different types of aircraft. In order to properly estimate the profiles, the ADS-B aircraft location information is adjusted based on extracted Doppler frequency and then used for Bistatic Radar Cross Section estimation. The classification is based on seven types of aircraft grouped by their size into three classes.

Modeling Energy Futures Returns With Markov Regime Switching Model

Financial time series have a tendency of abruptly changing their behavior and maintain this behavior for several consecutive periods, and commodity futures returns are not an exception. This quality proposes that nonlinear models, as opposed to linear models, can more accurately describe returns and volatility. Markov regime switching models are able to match this behavior and have become a popular way to model financial time series. This study uses Markov regime switching model to describe the behavior of energy futures returns on a commodity level, because studies show that commodity futures are a heterogeneous asset class. The purpose of this thesis is twofold. First, determine how many regimes characterize individual energy commodities’ returns in different return frequencies. Second, study the characteristics of these regimes. We extent the previous studies on the subject in two ways: We allow for the possibility that the number of regimes may exceed two, as well as conduct the research on individual commodities rather than on commodity indices or subgroups of these indices. We use daily, weekly and monthly time series of Brent crude oil, WTI crude oil, natural gas, heating oil and gasoil futures returns over 1994–2014, where available, to carry out the study. We apply the likelihood ratio test to determine the sufficient number of regimes for each commodity and data frequency. Then the time series are modeled with Markov regime switching model to obtain the return distribution characteristics of each regime, as well as the transition probabilities of moving between regimes. The results for the number of regimes suggest that daily energy futures return series consist of three to six regimes, whereas weekly and monthly returns for all energy commodities display only two regimes. When the number of regimes exceeds two, there is a tendency for the time series of energy commodities to form groups of regimes. These groups are usually quite persistent as a whole because probability of a regime switch inside the group is high. However, individual regimes in these groups are not persistent and the process oscillates between these regimes frequently. Regimes that are not part of any group are generally persistent, but show low ergodic probability, i.e. rarely prevail in the market. This study also suggests that energy futures return series characterized with two regimes do not necessarily display persistent bull and bear regimes. In fact, for the majority of time series, bearish regime is considerably less persistent. Rahoituksen aikasarjoilla on taipumus arvaamattomasti muuttaa käyttäytymistään ja jatkaa tätä uutta käyttäytymistä useiden periodien ajan, eivätkä hyödykefutuurien tuotot tee tähän poikkeusta. Tämän ominaisuuden johdosta lineaaristen mallien sijasta epälineaariset mallit pystyvät tarkemmin kuvailemaan esimerkiksi tuottojen jakauman parametreja. Markov regiiminvaihtomallit pystyvät vangitsemaan tämän ominaisuuden ja siksi niistä on tullut suosittuja rahoituksen aikasarjojen mallintamisessa. Tämä tutkimus käyttää Markov regiiminvaihtomallia kuvaamaan yksittäisten energiafutuurien tuottojen käyttäytymistä, sillä tutkimukset osoittavat hyödykefutuurien olevan hyvin heterogeeninen omaisuusluokka. Tutkimuksen tarkoitus on selvittää, kuinka monta regiimiä tarvitaan kuvaamaan energiafutuurien tuottoja eri tuottofrekvensseillä ja mitkä ovat näiden regiimien ominaisuudet. Aiempaa tutkimusta aiheesta laajennetaan määrittämällä regiimien lukumäärä tilastotieteellisen testauksen menetelmin sekä tutkimalla energiafutuureja yksittäin; ei indeksi- tai alaindeksitasolla. Tutkimuksessa käytetään päivä-, viikko- ja kuukausiaikasarjoja Brent-raakaöljyn, WTI-raakaöljyn, maakaasun, lämmitysöljyn ja polttoöljyn tuotoista aikaväliltä 1994–2014, siltä osin kuin aineistoa on saatavilla. Likelihood ratio -testin avulla estimoidaan kaikille aikasarjoille regiimien määrä,jonka jälkeen Markov regiiminvaihtomallia hyödyntäen määritetään yksittäisten regiimientuottojakaumien ominaisuudet sekä regiimien välinen transitiomatriisi. Tulokset regiimien lukumäärän osalta osoittavat, että energiafutuurien päiväkohtaisten tuottojen aikasarjoissa regiimien lukumäärä vaihtelee kolmen ja kuuden välillä. Viikko- ja kuukausituottojen kohdalla kaikkien energiafutuurien prosesseissa regiimien lukumäärä on kaksi. Kun regiimejä on enemmän kuin kaksi, on prosessilla taipumus muodostaa regiimeistä koostuvia ryhmiä. Prosessi pysyy ryhmän sisällä yleensä pitkään, koska todennäköisyys siirtyä ryhmään kuuluvien regiimien välillä on suuri. Yksittäiset regiimit ryhmän sisällä eivät kuitenkaan ole kovin pysyviä. Näin ollen prosessi vaihtelee ryhmän sisäisten regiimien välillä tiuhaan. Regiimit, jotka eivät kuulu ryhmään, ovat yleensä pysyviä, mutta prosessi ajautuu niihin vain harvoin, sillä todennäköisyys siirtyä muista regiimeistä niihin on pieni. Tutkimuksen tulokset osoittavat myös, että prosesseissa, joita ohjaa kaksi regiimiä, nämä regiimit eivät välttämättä ole pysyvät bull- ja bear-markkinatilanteet. Tulokset osoittavat sen sijaan, että bear-markkinatilanne on energiafutuureissa selvästi vähemmän pysyvä.