GAMS MINLP Solver comparisons and some improvements to the AlphaECP algorithm

I doktorsavhandlingen undersöks förmågan att lösa hos ett antal lösare för optimeringsproblem och ett antal svårigheter med att göra en rättvis lösarjämförelse avslöjas. Dessutom framläggs några förbättringar som utförts på en av lösarna som heter GAMS/AlphaECP. Optimering innebär, i det här sammanhanget, att finna den bästa möjliga lösningen på ett problem. Den undersökta klassen av problem kan karaktäriseras som svårlöst och förekommer inom ett flertal industriområden. Målet har varit att undersöka om det finns en lösare som är universellt snabbare och hittar lösningar med högre kvalitet än någon av de andra lösarna. Det kommersiella optimeringssystemet GAMS (General Algebraic Modeling System) och omfattande problembibliotek har använts för att jämföra lösare. Förbättringarna som presenterats har utförts på GAMS/AlphaECP lösaren som baserar sig på skärplansmetoden Extended Cutting Plane (ECP). ECP-metoden har utvecklats främst av professor Tapio Westerlund på Anläggnings- och systemteknik vid Åbo Akademi.

Strategic investment appraisal; extended real options approach

Tutkielma keskittyy lisäämään investointiarviointiprosessien rationaalisuutta strategisten investointien arvioinnissa duopoli- / oligopolimarkkinoilla. Tutkielman päätavoitteena on selvittää kuinka peliteorialla laajennettu reaalioptioperusteinen investointien arviointimenetelmä, laajennettu reaalioptiokehikko, voisi mahdollisesti parantaa analyysien tarkkuutta. Tutkimus lähestyy ongelmaa investoinnin ajoituksen sekä todellisten investoinnin arvoattribuuttien riippuvuuksien kautta. Laajennettu reaalioptiokehikko on investointien analysointi- ja johtamistyökalu, joka tarjoaa osittain rajoitetun (sisältää tällä hetkellä ainoastaan parametrisen ja peliteoreettisen epävarmuuden) optimaalisen arvovälin investoinnin todellisesta arvosta. Kehikossa, ROA kartoittaa mahdolliset strategiset hyödyt tunnistamalla investointiinliittyvät eri optiot ja epävarmuudet, peliteoria korostaa ympäristön luomia paineita investointiin liittyvän epävarmuuden hallitsemisessa. Laajennettu reaalioptiokehikko tarjoaa rationaalisemman arvion strategisen investoinnin arvosta, koska se yhdistää johdonmukaisemmin option toteutuksen ja siten myös optioiden aika-arvon, yrityksen todellisiin rajoitettuihin (rajoituksena muiden markkinatoimijoiden toimet) polkuriippuvaisiin kyvykkyyksiin.

The Boundaries of the Firm from the Extended Transaction Cost Economics Point of View - Empirical Study

Tutkimuksen tavoitteena oli tutkia yrityksen rajoja laajennetun transaktiokustannusteorian näkökulmasta. Tutkimus oli empiirinen tutkimus, jossa tutkittiin viittä toimialaa. Tutkimuksen tavoitteena oli verrata paperiteollisuutta teräs-, kemian-, ICT- ja energiateollisuuteen. Aineisto empiiriseen osioon kerättiin puolistrukturoiduilla teemahaastatteluilla. Tutkimus osoitti, että laajennettu transaktiokustannusteoria soveltuu hyvinyrityksen rajojen määrittelyyn. Staattinen transaktiokustannusteorian selitysaste ei ole riittävä, joten dynaaminen laajennus on tarpeellinen. Tutkimuksessa ilmeni, että paperiteollisuudella verrattuna muihin toimialoihin on suurimmat haasteet tehokkaiden rajojen määrittämisessä.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Lossless Compression Methods for Multispectral Images: Development and Comparisons

Main purpose of this thesis is to introduce a new lossless compression algorithm for multispectral images. Proposed algorithm is based on reducing the band ordering problem to the problem of finding a minimum spanning tree in a weighted directed graph, where set of the graph vertices corresponds to multispectral image bands and the arcs’ weights have been computed using a newly invented adaptive linear prediction model. The adaptive prediction model is an extended unification of 2–and 4–neighbour pixel context linear prediction schemes. The algorithm provides individual prediction of each image band using the optimal prediction scheme, defined by the adaptive prediction model and the optimal predicting band suggested by minimum spanning tree. Its efficiency has been compared with respect to the best lossless compression algorithms for multispectral images. Three recently invented algorithms have been considered. Numerical results produced by these algorithms allow concluding that adaptive prediction based algorithm is the best one for lossless compression of multispectral images. Real multispectral data captured from an airplane have been used for the testing.

Development of phase retrieval algorithm and techniques for optical spectrum analysis

Coherent anti-Stokes Raman scattering is the powerful method of laser spectroscopy in which significant successes are achieved. However, the non-linear nature of CARS complicates the analysis of the received spectra. The objective of this Thesis is to develop a new phase retrieval algorithm for CARS. It utilizes the maximum entropy method and the new wavelet approach for spectroscopic background correction of a phase function. The method was developed to be easily automated and used on a large number of spectra of different substances.. The algorithm was successfully tested on experimental data.

Multi-sensor Simultaneous Localization and Mapping

Simultaneous localization and mapping(SLAM) is a very important problem in mobile robotics. Many solutions have been proposed by different scientists during the last two decades, nevertheless few studies have considered the use of multiple sensors simultane¬ously. The solution is on combining several data sources with the aid of an Extended Kalman Filter (EKF). Two approaches are proposed. The first one is to use the ordinary EKF SLAM algorithm for each data source separately in parallel and then at the end of each step, fuse the results into one solution. Another proposed approach is the use of multiple data sources simultaneously in a single filter. The comparison of the computational com¬plexity of the two methods is also presented. The first method is almost four times faster than the second one.

[TESTI] unicode Extended-B

Kalajuttůja, Hattǔja

Genetic Basis and Diagnostics of Extended-Spectrum Beta-Lactamases among Enterobacteriaceae in Finland

Since the introduction of antibiotic agents, the amount and prevalence of Beta-lactam resistant enterobacteria has become an increasing problem. Many enterobacteria are opportunistic pathogens that easily acquire resistance mechanisms and genes, which make the situation menacing. These bacteria have acquired resistance and can hydrolyse extended spectrum cephalosporins and penicillins by producing enzymes called extended-spectrum Beta-lactamases (ESBLs). ESBL-producing bacteria are most commonly found in the gastro-intestinal tract of colonised patients. These resistant strains can be found in both health-care associated and community-acquired isolates. The detection and treatment of infections caused by bacteria producing ESBLs are problematic. This study investigated the genetic basis of extended-spectrum Beta-lactamases in Enterobacteriaceae, especially in Escherichia coli and Klebsiella pneumoniae isolates. A total of 994 Finnish Enterobacteriaceae strains, collected at 26 hospital laboratories, during 2000 and 2007 were analysed. For the genetic basis studies, PCR, sequencing and pyrosequencing methods were optimised. In addition, international standard methods, the agar dilution and disk diffusion methods were performed for the resistance studies, and the susceptibility of these strains was tested for antimicrobial agents that are used for treating patients. The genetic analysis showed that bla_CTX-M was the most prevalent gene among the E. coli isolates, while blaS_HV-12 was the most common Beta-lactamase gene in K. pneumoniae. The susceptibility testing results showed that about 60% of the strains were multidrug resistant. The prevalence of ESBL-producing isolates in Finland has been increasing since 2000. However, the situation in Finland is still much better than in many other European countries.

Inversion and assimilation methods with applications in geophysical remote sensing

Stratospheric ozone can be measured accurately using a limb scatter remote sensing technique at the UV-visible spectral region of solar light. The advantages of this technique includes a good vertical resolution and a good daytime coverage of the measurements. In addition to ozone, UV-visible limb scatter measurements contain information about NO2, NO3, OClO, BrO and aerosols. There are currently several satellite instruments continuously scanning the atmosphere and measuring the UVvisible region of the spectrum, e.g., the Optical Spectrograph and Infrared Imager System (OSIRIS) launched on the Odin satellite in February 2001, and the Scanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) launched on Envisat in March 2002. Envisat also carries the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which also measures limb-scattered sunlight under bright limb occultation conditions. These conditions occur during daytime occultation measurements. The global coverage of the satellite measurements is far better than any other ozone measurement technique, but still the measurements are sparse in the spatial domain. Measurements are also repeated relatively rarely over a certain area, and the composition of the Earth’s atmosphere changes dynamically. Assimilation methods are therefore needed in order to combine the information of the measurements with the atmospheric model. In recent years, the focus of assimilation algorithm research has turned towards filtering methods. The traditional Extended Kalman filter (EKF) method takes into account not only the uncertainty of the measurements, but also the uncertainty of the evolution model of the system. However, the computational cost of full blown EKF increases rapidly as the number of the model parameters increases. Therefore the EKF method cannot be applied directly to the stratospheric ozone assimilation problem. The work in this thesis is devoted to the development of inversion methods for satellite instruments and the development of assimilation methods used with atmospheric models.

Probiotic interventions in infancy: Benefit and Safety Assessment of Extended Applications

Immaturity of the gut barrier system in the newborn has been seen to underlie a number of chronic diseases originating in infancy and manifesting later in life. The gut microbiota and breast milk provide the most important maturing signals for the gut-related immune system and reinforcement of the gut mucosal barrier function. Recently, the composition of the gut microbiota has been proposed to be instrumental in control of host body weight and metabolism as well as the inflammatory state characterizing overweight and obesity. On this basis, inflammatory Western lifestyle diseases, including overweight development, may represent a potential target for probiotic interventions beyond the well documented clinical applications. The purpose of the present undertaking was to study the efficacy and safety of perinatal probiotic intervention. The material comprised two ongoing, prospective, double-blind NAMI (Nutrition, Allergy, Mucosal immunology and Intestinal microbiota) probiotic interventions. In the mother-infant nutrition and probiotic study altogether 256 women were randomized at their first trimester of pregnancy into a dietary intervention and a control group. The intervention group received intensive dietary counselling provided by a nutritionist, and were further randomized at baseline, double-blind, to receive probiotics (Lactobacillus rhamnosus GG and Bifidobacterium lactis) or placebo. The intervention period extended from the first trimester of pregnancy to the end of exclusive breastfeeding. In the allergy prevention study altogether 159 women were randomized, double-blind, to receive probiotics (Lactobacillus rhamnosus GG) or placebo 4 weeks before expected delivery, the intervention extending for 6 months postnatally. Additionally, patient data on all premature infants with very low birth weight (VLBW) treated in the Department of Paediatrics, Turku University Hospital, during the years 1997 - 2008 were utilized. The perinatal probiotic intervention reduced the risk of gestational diabetes mellitus (GDM) in the mothers and perinatal dietary counselling reduced that of fetal overgrowth in GDM-affected pregnancies. Early gut microbiota modulation with probiotics modified the growth pattern of the child by restraining excessive weight gain during the first years of life. The colostrum adiponectin concentration was demonstrated to be dependent on maternal diet and nutritional status during pregnancy. It was also higher in the colostrum received by normal-weight compared to overweight children at the age of 10 years. The early perinatal probiotic intervention and the postnatal probiotic intervention in VLBW infants were shown to be safe. To conclude, the findings in this study provided clinical evidence supporting the involvement of the initial microbial and nutritional environment in metabolic programming of the child. The manipulation of early gut microbial communities with probiotics might offer an applicable strategy to impact individual energy homeostasis and thus to prevent excessive body-weight gain. The results add weight to the hypothesis that interventions aiming to prevent obesity and its metabolic consequences later in life should be initiated as early as during the perinatal period.

Stress testing and risk evaluation of new capacity payment algorithm proposed by Market Council

In the Russian Wholesale Market, electricity and capacity are traded separately. Capacity is a special good, the sale of which obliges suppliers to keep their generating equipment ready to produce the quantity of electricity indicated by the System Operator. The purpose of the formation of capacity trading was the maintenance of reliable and uninterrupted delivery of electricity in the wholesale market. The price of capacity reflects constant investments in construction, modernization and maintenance of power plants. So, the capacity sale creates favorable conditions to attract investments in the energy sector because it guarantees the investor that his investments will be returned.

Solving Leontief Input-Output Model with Trapezoidal Fuzzy Numbers and Gauss-Seidel Algorithm

In this work a fuzzy linear system is used to solve Leontief input-output model with fuzzy entries. For solving this model, we assume that the consumption matrix from di erent sectors of the economy and demand are known. These assumptions heavily depend on the information obtained from the industries. Hence uncertainties are involved in this information. The aim of this work is to model these uncertainties and to address them by fuzzy entries such as fuzzy numbers and LR-type fuzzy numbers (triangular and trapezoidal). Fuzzy linear system has been developed using fuzzy data and it is solved using Gauss-Seidel algorithm. Numerical examples show the e ciency of this algorithm. The famous example from Prof. Leontief, where he solved the production levels for U.S. economy in 1958, is also further analyzed.

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.

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.