Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

Pulmonar recruitment in acute respiratory distress syndrome. What is the best strategy?

Supporting patients with acute respiratory distress syndrome (ARDS), using a protective mechanical ventilation strategy characterized by low tidal volume and limitation of positive end-expiratory pressure (PEEP) is a standard practice in the intensive care unit. However, these strategies can promote lung de-recruitment, leading to the cyclic closing and reopening of collapsed alveoli and small airways. Recruitment maneuvers (RM) can be used to augment other methods, like positive end-expiratory pressure and positioning, to improve aerated lung volume. Clinical practice varies widely, and the optimal method and patient selection for recruitment maneuvers have not been determined, considerable uncertainty remaining regarding the appropriateness of RM. This review aims to discuss recent findings about the available types of RM, and compare the effectiveness, indications and adverse effects among them, as well as their impact on morbidity and mortality in ARDS patients. Recent developments include experimental and clinical evidence that a stepwise extended recruitment maneuver may cause an improvement in aerated lung volume and decrease the biological impact seen with the traditionally used sustained inflation, with less adverse effects. Prone positioning can reduce mortality in severe ARDS patients and may be an useful adjunct to recruitment maneuvers and advanced ventilatory strategies, such noisy ventilation and BIVENT, which have been useful in providing lung recruitment.

Lectio praecursoria : Systems between information and knowledge : in a memory management model of an extended enterprise

VTT Jouni Meriluodon valtio-opin alaan kuuluva väitöskirja Systems between information and knowledge : in a memory management model of an extended enterprise tarkastettiin 21.6.2011 Helsingin yliopistossa.

Molecular epidemiology and extended-spectrum β-lactamases production of Klebsiella pneumoniae isolated from three dairy herds

The objectives of this study were to isolate Klebsiella pneumoniae from different sources in three dairy cattle herds, to use the pulsed-field gel electrophoresis (PFGE) to measure genotypic similarities between isolates within a dairy herd, to verify the production of extended-spectrum β-lactamases (ESBLs) by the double-disk synergy test (DDST), and to use the PCR to detect the main ESBLs subgroups genes. Three dairy farms were selected based on previous mastitis outbreaks caused by K. pneumoniae. Milk samples were collected from lactating cows and from the bulk tank. Swabs were performed in different locations, including milking parlors, waiting room, soil, animal's hind limbs and rectum. K. pneumoniae was isolated from 27 cases of intramammary infections (IMI) and from 41 swabs. For farm A isolates from IMI and bulk tank were considered of the same PGFE subtype. One isolate from a bulk tank, three from IMI cases and four from environmental samples were positive in the DDST test. All eight DDST positive isolates harbored the bla shv gene, one harbored the bla tem gene, and three harbored the bla ctx-m gene, including the bulk tank isolate. Our study confirms that ESBL producing bacteria is present in different locations in dairy farms, and may be responsible for IMI. The detection of ESBLs on dairy herds could be a major concern for both public and animal health.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional least-square method (LS). Among these the most important are: there are no restrictions on linearity or in the form which the parameters appears in the mathematical model describing the system, and it is not required that these parameters be time invariant. The EKF uses the statistical properties of the process and the observation noise, to produce estimates based on the mean square error of the estimates themselves. Differently, the LS minimizes a cost function based on the plant output behavior. Results for the estimation of some longitudinal aerodynamic derivatives from simulated data are presented.

Algorithm to determine the intersection curves between bezier surfaces by the solution of multivariable polynomial system and the differential marching method

The determination of the intersection curve between Bézier Surfaces may be seen as the composition of two separated problems: determining initial points and tracing the intersection curve from these points. The Bézier Surface is represented by a parametric function (polynomial with two variables) that maps a point in the tridimensional space from the bidimensional parametric space. In this article, it is proposed an algorithm to determine the initial points of the intersection curve of Bézier Surfaces, based on the solution of polynomial systems with the Projected Polyhedral Method, followed by a method for tracing the intersection curves (Marching Method with differential equations). In order to allow the use of the Projected Polyhedral Method, the equations of the system must be represented in terms of the Bernstein basis, and towards this goal it is proposed a robust and reliable algorithm to exactly transform a multivariable polynomial in terms of power basis to a polynomial written in terms of Bernstein basis .

Numerical Simulation of the Cavitation in the Hydrodynamic Lubrication of Journal Bearings: a Parallel Algorithm

In this paper we present an algorithm for the numerical simulation of the cavitation in the hydrodynamic lubrication of journal bearings. Despite the fact that this physical process is usually modelled as a free boundary problem, we adopted the equivalent variational inequality formulation. We propose a two-level iterative algorithm, where the outer iteration is associated to the penalty method, used to transform the variational inequality into a variational equation, and the inner iteration is associated to the conjugate gradient method, used to solve the linear system generated by applying the finite element method to the variational equation. This inner part was implemented using the element by element strategy, which is easily parallelized. We analyse the behavior of two physical parameters and discuss some numerical results. Also, we analyse some results related to the performance of a parallel implementation of the algorithm.

An Inspection System to Identify Fatigue Damage on Steel-Bridge Structures

Crack formation and growth in steel bridge structural elements may be due to loading oscillations. The welded elements are liable to internal discontinuities along welded joints and sensible to stress variations. The evaluation of the remaining life of a bridge is needed to make cost-effective decisions regarding inspection, repair, rehabilitation, and replacement. A steel beam model has been proposed to simulate crack openings due to cyclic loads. Two possible alternatives have been considered to model crack propagation, which the initial phase is based on the linear fracture mechanics. Then, the model is extended to take into account the elastoplastic fracture mechanic concepts. The natural frequency changes are directly related to moment of inertia variation and consequently to a reduction in the flexural stiffness of a steel beam. Thus, it is possible to adopt a nondestructive technique during steel bridge inspection to quantify the structure eigenvalue variation that will be used to localize the grown fracture. A damage detection algorithm is developed for the proposed model and the numerical results are compared with the solutions achieved by using another well know computer code.

Evolver: The Evolution of The Extended EPrints Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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.

Extended hydrogen photoproduction by nitrogen-fixing cyanobacteria

Cyanobacteria are the only prokaryotic organisms performing oxygenic photosynthesis. They comprise a diverse and versatile group of organisms in aquatic and terrestrial environments. Increasing genomic and proteomic data launches wide possibilities for their employment in various biotechnical applications. For example, cyanobacteria can use solar energy to produce H2. There are three different enzymes that are directly involved in cyanobacterial H2 metabolism: nitrogenase (nif) which produces hydrogen as a byproduct in nitrogen fixation; bidirectional hydrogenase (hox) which functions both in uptake and in production of H2; and uptake hydrogenase (hup) which recycles the H2 produced by nitrogenase back for the utilization of the cell. Cyanobacterial strains from University of Helsinki Cyanobacteria Collection (UHCC), isolated from the Baltic Sea and Finnish lakes were screened for efficient H2 producers. Screening about 400 strains revealed several promising candidates producing similar amounts of H2 (during light) as the ΔhupL mutant of Anabaena PCC 7120, which is specifically engineered to produce higher amounts of H2 by the interruption of uptake hydrogenase. The optimal environmental conditions for H2 photoproduction were significantly different between various cyanobacterial strains. All suitable strains revealed during screening were N2-fixing, filamentous and heterocystous. The top ten H2 producers were characterized for the presence and activity of the enzymes involved in H2 metabolism. They all possess the genes encoding the conventional nitrogenase (nifHDK1). However, the high H2 photoproduction rates of these strains were shown not to be directly associated with the maximum capacities of highly active nitrogenase or bidirectional hydrogenase. Most of the good producers possessed a highly active uptake hydrogenase, which has been considered as an obstacle for efficient H2 production. Among the newly revealed best H2 producing strains, Calothrix 336/3 was chosen for further, detailed characterization. Comparative analysis of the structure of the nif and hup operons encoding the nitrogenase and uptake hydrogenase enzymes respectively showed minor differences between Calothrix 336/3 and other N2-fixing model cyanobacteria. Calothrix 336/3 is a filamentous, N2-fixing cyanobacterium with ellipsoidal, terminal heterocysts. A common feature of Calothrix 336/3 is that the cells readily adhere to substrates. To make use of this feature, and to additionally improve H2 photoproduction capacity of the Calothrix 336/3 strain, an immobilization technique was applied. The effects of immobilization within thin alginate films were evaluated by examining the photoproduction of H2 of immobilized Calothrix 336/3 in comparison to model strains, the Anabaena PCC 7120 and its ΔhupL mutant. In order to achieve optimal H2 photoproduction, cells were kept under nitrogen starved conditions (Ar atmosphere) to ensure the selective function of nitrogenase in reducing protons to H2. For extended H2 photoproduction, cells require CO2 for maintenance of photosynthetic activity and recovery cycles to fix N2. Application of regular H2 production and recovery cycles, Ar or air atmospheres respectively, resulted in prolongation of H2 photoproduction in both Calothrix 336/3 and the ΔhupL mutant of Anabaena PCC 7120. However, recovery cycles, consisting of air supplemented with CO2, induced a strong C/N unbalance in the ΔhupL mutant leading to a decrease in photosynthetic activity, although total H2 yield was still higher compared to the wild-type strain. My findings provide information about the diversity of cyanobacterial H2 capacities and mechanisms and provide knowledge of the possibilities of further enhancing cyanobacterial H2 production.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

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.

Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization /

Solid state nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for studying structural and dynamical properties of disordered and partially ordered materials, such as glasses, polymers, liquid crystals, and biological materials. In particular, twodimensional( 2D) NMR methods such as ^^C-^^C correlation spectroscopy under the magicangle- spinning (MAS) conditions have been used to measure structural constraints on the secondary structure of proteins and polypeptides. Amyloid fibrils implicated in a broad class of diseases such as Alzheimer's are known to contain a particular repeating structural motif, called a /5-sheet. However, the details of such structures are poorly understood, primarily because the structural constraints extracted from the 2D NMR data in the form of the so-called Ramachandran (backbone torsion) angle distributions, g{^,'4)), are strongly model-dependent. Inverse theory methods are used to extract Ramachandran angle distributions from a set of 2D MAS and constant-time double-quantum-filtered dipolar recoupling (CTDQFD) data. This is a vastly underdetermined problem, and the stability of the inverse mapping is problematic. Tikhonov regularization is a well-known method of improving the stability of the inverse; in this work it is extended to use a new regularization functional based on the Laplacian rather than on the norm of the function itself. In this way, one makes use of the inherently two-dimensional nature of the underlying Ramachandran maps. In addition, a modification of the existing numerical procedure is performed, as appropriate for an underdetermined inverse problem. Stability of the algorithm with respect to the signal-to-noise (S/N) ratio is examined using a simulated data set. The results show excellent convergence to the true angle distribution function g{(j),ii) for the S/N ratio above 100.