Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.

Quantitative Analysis of Novel Prostate Cancer Markers in Tissue

Prostate cancer is a heterogeneous disease affecting an increasing number of men all over the world, but particularly in the countries with the Western lifestyle. The best biomarker assay currently available for the diagnosis of the disease, the measurement of prostate specific antigen (PSA) levels from blood, lacks specificity, and even when combined with invasive tests such as digital rectal exam and prostate tissue biopsies, these methods can both miss cancers, and lead to overdiagnosis and subsequent overtreatment of cancers. Moreover, they cannot provide an accurate prognosis for the disease. Due to the high prevalence of indolent prostate cancers, the majority of men affected by prostate cancer would be able to live without any medical intervention. Their latent prostate tumors would not cause any clinical symptoms during their lifetime, but few are willing to take the risk, as currently there are no methods or biomarkers to reliably differentiate the indolent cancers from the aggressive, lethal cases that really are in need of immediate medical treatment. This doctoral work concentrated on validating 12 novel candidate genes for use as biomarkers for prostate cancer by measuring their mRNA expression levels in prostate tissue and peripheral blood of men with cancer as well as unaffected individuals. The panel of genes included the most prominent markers in the current literature: PCA3 and the fusion gene TMPRSS2-ERG, in addition to BMP-6, FGF-8b, MSMB, PSCA, SPINK1, and TRPM8; and the kallikrein-related peptidase genes 2, 3, 4, and 15. Truly quantitative reverse-transcription PCR assays were developed for each of the genes for the purpose, time-resolved fluorometry was applied in the real-time detection of the amplification products, and the gene expression data were normalized by using artificial internal RNA standards. Cancer-related, statistically significant differences in gene transcript levels were found for TMPRSS2-ERG, PCA3, and in a more modest scale, for KLK15, PSCA, and SPINK1. PCA3 RNA was found in the blood of men with metastatic prostate cancer, but not in localized cases of cancer, suggesting limitations for using this method for early cancer detection in blood. TMPRSS2-ERG mRNA transcripts were found more frequently in cancerous than in benign prostate tissues, but they were present also in 51% of the histologically benign prostate tissues of men with prostate cancer, while being absent in specimens from men without any signs of prostate cancer. PCA3 was shown to be 5.8 times overexpressed in cancerous tissue, but similarly to the fusion gene mRNA, its levels were upregulated also in the histologically benign regions of the tissue if the corresponding prostate was harboring carcinoma. These results indicate a possibility to utilize these molecular assays to assist in prostate cancer risk evaluation especially in men with initially histologically negative biopsies.

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.

Adaptive Dynamics of Resource Specialization

Ecological specialization in resource utilization has various facades ranging from nutritional resources via host use of parasites or phytophagous insects to local adaptation in different habitats. Therefore, the evolution of specialization affects the evolution of most other traits, which makes it one of the core issues in the theory of evolution. Hence, the evolution of specialization has gained enormous amounts of research interest, starting already from Darwin’s Origin of species in 1859. Vast majority of the theoretical studies has, however, focused on the mathematically most simple case with well-mixed populations and equilibrium dynamics. This thesis explores the possibilities to extend the evolutionary analysis of resource usage to spatially heterogeneous metapopulation models and to models with non-equilibrium dynamics. These extensions are enabled by the recent advances in the field of adaptive dynamics, which allows for a mechanistic derivation of the invasion-fitness function based on the ecological dynamics. In the evolutionary analyses, special focus is set to the case with two substitutable renewable resources. In this case, the most striking questions are, whether a generalist species is able to coexist with the two specialist species, and can such trimorphic coexistence be attained through natural selection starting from a monomorphic population. This is shown possible both due to spatial heterogeneity and due to non-equilibrium dynamics. In addition, it is shown that chaotic dynamics may sometimes inflict evolutionary suicide or cyclic evolutionary dynamics. Moreover, the relations between various ecological parameters and evolutionary dynamics are investigated. Especially, the relation between specialization and dispersal propensity turns out to be counter-intuitively non-monotonous. This observation served as inspiration to the analysis of joint evolution of dispersal and specialization, which may provide the most natural explanation to the observed coexistence of specialist and generalist species.

Clinical and genetic markers in disease progression – a study among subjects with obstructive lung diseases

Asthma, COPD, and asthma and COPD overlap syndrome (ACOS) are chronic pulmonary diseases with an obstructive component. In COPD, the obstruction is irreversible and the disease is progressive. The aim of the study was to define and analyze factors that affected disease progression and patients’ well-being, prognosis and mortality in Chronic Airway Disease (CAD) cohort. The main focus was on COPD and ACOS patients. Retrospective data from medical records was combined with genetic and prospective follow-up data. Smoking is the biggest risk factor for COPD and even after the diagnosis of the disease, smoking plays an important role in disease development and patient’s prognosis. Sixty percent of the COPD patients had succeeded in smoking cessation. Patients who had managed to quit smoking had lower mortality rates and less psychiatric diseases and alcohol abuse although they were older and had more cardiovascular diseases than patients who continued smoking. Genetic polymorphism rs1051730 in the nicotinic acethylcholine receptor gene (CHRNA3/5) associated with heavy smoking, cancer prevalence and mortality in two Finnish independent cohorts consisting of COPD patients and male smokers. Challenges in smoking cessation and higher mortality rates may be partly due to individual patient’s genetic composition. Approximately 50% of COPD patients are physically inactive and the proportion was higher among current smokers. Physically active and inactive patients didn’t differ from each other in regard to age, gender or comorbidities. Bronchial obstruction explained inactivity only in severe disease. Subjective sensation of dyspnea, however, had very strong association to inactivity and was also associated to low health related quality of life (HRQoL). ACOS patients had a significantly lower HRQoL than either the patients with asthma or with COPD even though they were younger than COPD patients, had better lung functions and smaller tobacco exposure.

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.

Real-time adaptive control of ultra-fast laser scribing process with spectrometer online monitoring

There exist several researches and applications about laser welding monitoring and parameter control but not a single one have been created for controlling of laser scribing processes. Laser scribing is considered to be very fast and accurate process and thus it would be necessary to develop accurate turning and monitoring system for such a process. This research focuses on finding out whether it would be possible to develop real-time adaptive control for ultra-fast laser scribing processes utilizing spectrometer online monitoring. The thesis accurately presents how control code for laser parameter tuning is developed using National Instrument's LabVIEW and how spectrometer is being utilized in online monitoring. Results are based on behavior of the control code and accuracy of the spectrometer monitoring when scribing different steel materials. Finally control code success is being evaluated and possible development ideas for future are presented.