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.

A Phase Model for Building a Customer Value Based Sales Tool – Case Mining and Metals Industry

The objective of this research is to observe the state of customer value management in Outotec Oyj, determine the key development areas and develop a phase model with which to guide the development of a customer value based sales tool. The study was conducted with a constructive research approach with the focus of identifying a problem and developing a solution for the problem. As a basis for the study, the current literature involving customer value assessment and solution and customer value selling was studied. The data was collected by conducting 16 interviews in two rounds within the company and it was analyzed by coding openly. First, seven important development areas were identified, out of which the most critical were “Customer value mindset inside the company” and “Coordination of customer value management activities”. Utilizing these seven areas three functionality requirements, “Preparation”, “Outotec’s value creation and communication” and “Documentation” and three development requirements for a customer value sales tool were identified. The study concluded with the formulation of a phase model for building a customer value based sales tool. The model included five steps that were defined as 1) Enable customer value utilization, 2) Connect with the customer, 3) Create customer value, 4) Define tool to facilitate value selling and 5) Develop sales tool. Further practical activities were also recommended as a guide for executing the phase model.

Hyperfine interactions in the new Fe-based superconducting structures and related magnetic phases

This thesis is devoted to the study of the hyperfine properties in iron-based superconductors and the synthesis of these compounds and related phases. During this work polycrystalline chalcogenide samples with stoichiometry 1:1 (FeTe1-χSχ, FeSe1-x) and pnictide samples with stoichiometry 1:2:2 (BaFe2(As1-χPχ)2, EuFe2(As1-x Px)2) were synthesized by solid-state reaction methods in vacuum and in a protecting Ar atmosphere. In several cases post-annealing in oxygen atmosphere was employed. The purity and superconducting properties of the obtained samples were checked with X-ray diffraction, SQUID and resistivity measurements. For studies of the magnetic properties of the investigated samples Mössbauer spectroscopy was used. Using low-temperature measurements around Tc and various values of the source velocity the hyperfine interactions were obtained and the magnetic and structural properties in the normal and superconducting states could be studied. Mössbauer measurements together with XRD characterization were also used for the detection of impurity phases. DFT calculations were used for the theoretical study of Mössbauer parameters for pnictide-based ᴻsamples BaFe2(As1-xPx)2 and EuFe2(As1-xPx)2.

Novel virtual environment and real-time simulation based methods for improving life-cycle efficiency of non-road mobile machinery

Virtual environments and real-time simulators (VERS) are becoming more and more important tools in research and development (R&D) process of non-road mobile machinery (NRMM). The virtual prototyping techniques enable faster and more cost-efficient development of machines compared to use of real life prototypes. High energy efficiency has become an important topic in the world of NRMM because of environmental and economic demands. The objective of this thesis is to develop VERS based methods for research and development of NRMM. A process using VERS for assessing effects of human operators on the life-cycle efficiency of NRMM was developed. Human in the loop simulations are ran using an underground mining loader to study the developed process. The simulations were ran in the virtual environment of the Laboratory of Intelligent Machines of Lappeenranta University of Technology. A physically adequate real-time simulation model of NRMM was shown to be reliable and cost effective in testing of hardware components by the means of hardware-in-the-loop (HIL) simulations. A control interface connecting integrated electro-hydraulic energy converter (IEHEC) with virtual simulation model of log crane was developed. IEHEC consists of a hydraulic pump-motor and an integrated electrical permanent magnet synchronous motorgenerator. The results show that state of the art real-time NRMM simulators are capable to solve factors related to energy consumption and productivity of the NRMM. A significant variation between the test drivers is found. The results show that VERS can be used for assessing human effects on the life-cycle efficiency of NRMM. HIL simulation responses compared to that achieved with conventional simulation method demonstrate the advances and drawbacks of various possible interfaces between the simulator and hardware part of the system under study. Novel ideas for arranging the interface are successfully tested and compared with the more traditional one. The proposed process for assessing the effects of operators on the life-cycle efficiency will be applied for wider group of operators in the future. Driving styles of the operators can be analysed statistically from sufficient large result data. The statistical analysis can find the most life-cycle efficient driving style for the specific environment and machinery. The proposed control interface for HIL simulation need to be further studied. The robustness and the adaptation of the interface in different situations must be verified. The future work will also include studying the suitability of the IEHEC for different working machines using the proposed HIL simulation method.

Solid-state reference and ion-selective electrodes : towards portable potentiometric sensing

Potentiometric sensors are very attractive tools for chemical analysis because of their simplicity, low power consumption and low cost. They are extensively used in clinical diagnostics and in environmental monitoring. Modern applications of both fields require improvements in the conventional construction and in the performance of the potentiometric sensors, as the trends are towards portable, on-site diagnostics and autonomous sensing in remote locations. The aim of this PhD work was to improve some of the sensor properties that currently hamper the implementation of the potentiometric sensors in modern applications. The first part of the work was concentrated on the development of a solid-state reference electrode (RE) compatible with already existing solid-contact ion-selective electrodes (ISE), both of which are needed for all-solid-state potentiometric sensing systems. A poly(vinyl chloride) membrane doped with a moderately lipophilic salt, tetrabutylammonium-tetrabutylborate (TBA-TBB), was found to show a satisfactory stability of potential in sample solutions with different concentrations. Its response time was nevertheless slow, as it required several minutes to reach the equilibrium. The TBA-TBB membrane RE worked well together with solid-state ISEs in several different situations and on different substrates enabling a miniature design. Solid contacts (SC) that mediate the ion-to-electron transduction are crucial components of well-functioning potentiometric sensors. This transduction process converting the ionic conduction of an ion-selective membrane to the electronic conduction in the circuit was studied with the help of electrochemical impedance spectroscopy (EIS). The solid contacts studied were (i) the conducting polymer (CP) poly(3,4-ethylienedioxythiophene) (PEDOT) and (ii) a carbon cloth having a high surface area. The PEDOT films were doped with a large immobile anion poly(styrene sulfonate) (PSS-) or with a small mobile anion Cl-. As could be expected, the studied PEDOT solid-contact mediated the ion-toelectron transduction more efficiently than the bare glassy carbon substrate, onto which they were electropolymerized, while the impedance of the PEDOT films depended on the mobility of the doping ion and on the ions in the electrolyte. The carbon cloth was found to be an even more effective ion-to-electron transducer than the PEDOT films and it also proved to work as a combined electrical conductor and solid contact when covered with an ion-selective membrane or with a TBA-TBB-based reference membrane. The last part of the work was focused on improving the reproducibility and the potential stability of the SC-ISEs, a problem that culminates to the stability of the standard potential E°. It was proven that the E° of a SC-ISE with a conducting polymer as a solid contact could be adjusted by reducing or oxidizing the CP solid contact by applying current pulses or a potential to it, as the redox state of the CP solid-contact influences the overall potential of the ISE. The slope and thus the analytical performance of the SC-ISEs were retained despite the adjustment of the E°. The shortcircuiting of the SC-ISE with a conventional large-capacitance RE was found to be a feasible instrument-free method to control the E°. With this method, the driving force for the oxidation/reduction of the CP was the potential difference between the RE and the SC-ISE, and the position of the adjusted potential could be controlled by choosing a suitable concentration for the short-circuiting electrolyte. The piece-to-piece reproducibility of the adjusted potential was promising, and the day-today reproducibility for a specific sensor was excellent. The instrumentfree approach to control the E° is very attractive considering practical applications.

Review of water electrolysis technologies and design of renewable hydrogen production systems

An electric system based on renewable energy faces challenges concerning the storage and utilization of energy due to the intermittent and seasonal nature of renewable energy sources. Wind and solar photovoltaic power productions are variable and difficult to predict, and thus electricity storage will be needed in the case of basic power production. Hydrogen’s energetic potential lies in its ability and versatility to store chemical energy, to serve as an energy carrier and as feedstock for various industries. Hydrogen is also used e.g. in the production of biofuels. The amount of energy produced during hydrogen combustion is higher than any other fuel’s on a mass basis with a higher-heating-value of 39.4 kWh/kg. However, even though hydrogen is the most abundant element in the universe, on Earth most hydrogen exists in molecular forms such as water. Therefore, hydrogen must be produced and there are various methods to do so. Today, the majority hydrogen comes from fossil fuels, mainly from steam methane reforming, and only about 4 % of global hydrogen comes from water electrolysis. Combination of electrolytic production of hydrogen from water and supply of renewable energy is attracting more interest due to the sustainability and the increased flexibility of the resulting energy system. The preferred option for intermittent hydrogen storage is pressurization in tanks since at ambient conditions the volumetric energy density of hydrogen is low, and pressurized tanks are efficient and affordable when the cycling rate is high. Pressurized hydrogen enables energy storage in larger capacities compared to battery technologies and additionally the energy can be stored for longer periods of time, on a time scale of months. In this thesis, the thermodynamics and electrochemistry associated with water electrolysis are described. The main water electrolysis technologies are presented with state-of-the-art specifications. Finally, a Power-to-Hydrogen infrastructure design for Lappeenranta University of Technology is presented. Laboratory setup for water electrolysis is specified and factors affecting its commissioning in Finland are presented.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

What is the current state of cloud service research and its promise for the future? A systematic literature review

The information technology (IT) industry has recently witnessed the proliferation of cloud services, which have allowed IT service providers to deliver on-demand resources to customers over the Internet. This frees both service providers and consumers from traditional IT-related burdens such as capital and operating expenses and allows them to respond rapidly to new opportunities in the market. Due to the popularity and growth of cloud services, numerous researchers have conducted studies on various aspects of cloud services, both positive and negative. However, none of those studies have connected all relevant information to provide a holistic picture of the current state of cloud service research. This study aims to investigate that current situation and propose the most promising future directions. In order to determine achieve these goals, a systematic literature review was conducted on studies with a primary focus on cloud services. Based on carefully crafted inclusion criteria, 52 articles from highly credible online sources were selected for the review. To define the main focus of the review and facilitate the analysis of literature, a conceptual framework with five main factors was proposed. The selected articles were organized under the factors of the proposed framework and then synthesized using a narrative technique. The results of this systematic review indicate that the impacts of cloud services on enterprises were the factor best covered by contemporary research. Researchers were able to present valuable findings about how cloud services impact various aspects of enterprises such as governance, performance, and security. By contrast, the role of service provider sub-contractors in the cloud service market remains largely uninvestigated, as do cloud-based enterprise software and cloud-based office systems for consumers. Moreover, the results also show that researchers should pay more attention to the integration of cloud services into legacy IT systems to facilitate the adoption of cloud services by enterprise users. After the literature synthesis, the present study proposed several promising directions for cloud service research by outlining research questions for the underexplored areas of cloud services, in order to facilitate the development of cloud service markets in the future.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

The TTIP, ISDS and the shift towards transparency in investor-state arbitration

The negotiations between the EU and the US over the Transatlantic Trade and Investment Partnership (TTIP) have generated a lot of discussion about investor-state dispute settlement (ISDS). This discussion provided the inspiration for this thesis, with the TTIP in the background, setting the scene. In this thesis I study the nature of ISDS and the principle of transparency within investor-state arbitration. I aim to determine whether the use of ISDS is restricted to international arbitration and whether ISDS can be considered to constitute a system or regime. Furthermore, I consider whether the introduction of the UNCITRAL Rules on Transparency in Treaty-based Investor-State Arbitration (2014, the UNCITRAL Transparency Rules) changes investor-state arbitration in relation to transparency. To achieve this, I examine ISDS provisions in several different international investment agreements (IIAs) and evaluate the ways in which transparency is incorporated into investment law. Moreover, I compare the provisions on transparency and confidentiality in institutional arbitration rules with the UNCITRAL Transparency Rules. I have formed several conclusions, including that the ISDS provisions may contain methods other than international arbitration and that ISDS does not constitute a system. Furthermore, the UNCITRAL Transparency Rules do change – theoretically, at least – investor-state arbitration to become more transparent. Whether the UNCITRAL Transparency Rules will make investor-state arbitration fully transparent depends on the actions of the contracting state parties when negotiating new IIAs and whether they choose to incorporate the UNCITRAL Transparency Rules in the IIAs already concluded.