Comparison of human and computer-based selective cleaning

Abstract

Design of an Embedded Computer Board for Networking Applications

Tämä diplomityö tehtiin Convergens Oy:lle. Convergens on elektroniikan suunnittelutoimisto, joka on erikoistunut sulautettuihin järjestelmiin sekä tietoliikennetekniikkaan. Diplomityön tavoitteena oli suunnitella tietokonekortti tietoliikennesovelluksia varten asiakkaalle, jolta vaatimusmäärittelyt tulivat. Työ on rajattu koskemaan laitteen prototyypin suunnittelua. Työssä suunnitellaan pääasiassa WLAN-tukiaseman tietokone. Tukiasema onasennettavissa toimistoihin, varastoihin, kauppoihin sekä myös liikkuvaan ajoneuvoon. Suunnittelussa on otettu nämä asiat huomioon, ja laitteen akun pystyy lataamaan muun muassa auton akulla. Langattomat tekniikat ovat voimakkaasti yleistymässä, ja tämän työn tukiasema tarjoaakin varteenotettavan vaihtoehdon lukuisilla ominaisuuksillaan. Mukana on mm. GPS, Bluetooth sekä Ethernet-valmius. Langattomien tekniikoiden lisäksi myös sulautetut järjestelmät ovat voimakkaasti yleistymässä, ja nykyään mikroprosessoreita löytääkin lähesmistä vain. Tässä projektissa käytetty prosessori on nopeutensa puolesta kilpailukykyinen, ja siitä löytyy useita eri rajapintoja. Jatkossa tietokonekortille on myös tulossa WiMAX-tuki, joka lisää tukiaseman tulevaisuuden arvoa asiakkaalle. Projektiin valittu Freescalen MPC8321E-prosessori on PowerPC-arkkitehtuuriin perustuva ja juuri markkinoille ilmestynyt. Tämä toi mukanaan lisähaasteen, sillä kyseisestä prosessorista ei ollut vielä kaikkea tietoa saatavilla. Mekaniikka toi omat haasteensa mukanaan, sillä se rajoitti piirilevyn koonniin, että ylimääräistä piirilevytilaa ei juurikaan jäänyt. Tämän takia esimerkiksi DDR-muistit olivat haastavia reitittää, sillä muistivetojen on oltava melko samanpituisia keskenään. Käyttöjärjestelmänä projektissa käytetään Linuxia. Suunnittelu alkoi keväällä 2007 ja toimiva prototyyppi oli valmis alkusyksystä. Prototyypin testaus osoitti, että tietokonekortti kykenee täyttämään kaikki asiakkaan vaatimukset. Prototyypin testauksessa löytyneet viat ja optimoinnit on tarkoitus korjata tuotantomalliin, joten se antaa hyvän pohjan jatkosuunnittelua varten.

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.

Automated formulation of optimisation models for steel beam structures

Over 70% of the total costs of an end product are consequences of decisions that are made during the design process. A search for optimal cross-sections will often have only a marginal effect on the amount of material used if the geometry of a structure is fixed and if the cross-sectional characteristics of its elements are property designed by conventional methods. In recent years, optimalgeometry has become a central area of research in the automated design of structures. It is generally accepted that no single optimisation algorithm is suitable for all engineering design problems. An appropriate algorithm, therefore, mustbe selected individually for each optimisation situation. Modelling is the mosttime consuming phase in the optimisation of steel and metal structures. In thisresearch, the goal was to develop a method and computer program, which reduces the modelling and optimisation time for structural design. The program needed anoptimisation algorithm that is suitable for various engineering design problems. Because Finite Element modelling is commonly used in the design of steel and metal structures, the interaction between a finite element tool and optimisation tool needed a practical solution. The developed method and computer programs were tested with standard optimisation tests and practical design optimisation cases. Three generations of computer programs are developed. The programs combine anoptimisation problem modelling tool and FE-modelling program using three alternate methdos. The modelling and optimisation was demonstrated in the design of a new boom construction and steel structures of flat and ridge roofs. This thesis demonstrates that the most time consuming modelling time is significantly reduced. Modelling errors are reduced and the results are more reliable. A new selection rule for the evolution algorithm, which eliminates the need for constraint weight factors is tested with optimisation cases of the steel structures that include hundreds of constraints. It is seen that the tested algorithm can be used nearly as a black box without parameter settings and penalty factors of the constraints.

Tuning the design procedures for laser processed microwave mechanics

This research has been focused at the development of a tuned systematic design methodology, which gives the best performance in a computer aided environment and utilises a cross-technological approach, specially tested with and for laser processed microwave mechanics. A tuned design process scheme is also presented. Because of the currently large production volumes of microwave and radio frequency mechanics even slight improvements of design methodologies or manufacturing technologies would give reasonable possibilities for cost reduction. The typical number of required iteration cycles could be reduced to one fifth of normal. The research area dealing with the methodologies is divided firstly into a function-oriented, a performance-oriented or a manufacturability-oriented product design. Alternatively various approaches can be developed for a customer-oriented, a quality-oriented, a cost-oriented or an organisation-oriented design. However, the real need for improvements is between these two extremes. This means that the effective methodology for the designers should not be too limited (like in the performance-oriented design) or too general (like in the organisation-oriented design), but it should, include the context of the design environment. This is the area where the current research is focused. To test the developed tuned design methodology for laser processing (TDMLP) and the tuned optimising algorithm for laser processing (TOLP), seven different industrial product applications for microwave mechanics have been designed, CAD-modelled and manufactured by using laser in small production series. To verify that the performance of these products meets the required level and to ensure the objectiveness ofthe results extensive laboratory tests were used for all designed prototypes. As an example a Ku-band horn antenna can be laser processed from steel in 2 minutes at the same time obtaining a comparable electrical performance of classical aluminium units or the residual resistance of a laser joint in steel could be limited to 72 milliohmia.

Mass transfer and particleinteractions in granular systems and suspension flows

The purpose of this study was to investigate some important features of granular flows and suspension flows by computational simulation methods. Granular materials have been considered as an independent state ofmatter because of their complex behaviors. They sometimes behave like a solid, sometimes like a fluid, and sometimes can contain both phases in equilibrium. The computer simulation of dense shear granular flows of monodisperse, spherical particles shows that the collisional model of contacts yields the coexistence of solid and fluid phases while the frictional model represents a uniform flow of fluid phase. However, a comparison between the stress signals from the simulations and experiments revealed that the collisional model would result a proper match with the experimental evidences. Although the effect of gravity is found to beimportant in sedimentation of solid part, the stick-slip behavior associated with the collisional model looks more similar to that of experiments. The mathematical formulations based on the kinetic theory have been derived for the moderatesolid volume fractions with the assumption of the homogeneity of flow. In orderto make some simulations which can provide such an ideal flow, the simulation of unbounded granular shear flows was performed. Therefore, the homogeneous flow properties could be achieved in the moderate solid volume fractions. A new algorithm, namely the nonequilibrium approach was introduced to show the features of self-diffusion in the granular flows. Using this algorithm a one way flow can beextracted from the entire flow, which not only provides a straightforward calculation of self-diffusion coefficient but also can qualitatively determine the deviation of self-diffusion from the linear law at some regions nearby the wall inbounded flows. Anyhow, the average lateral self-diffusion coefficient, which was calculated by the aforementioned method, showed a desirable agreement with thepredictions of kinetic theory formulation. In the continuation of computer simulation of shear granular flows, some numerical and theoretical investigations were carried out on mass transfer and particle interactions in particulate flows. In this context, the boundary element method and its combination with the spectral method using the special capabilities of wavelets have been introduced as theefficient numerical methods to solve the governing equations of mass transfer in particulate flows. A theoretical formulation of fluid dispersivity in suspension flows revealed that the fluid dispersivity depends upon the fluid properties and particle parameters as well as the fluid-particle and particle-particle interactions.

Level Set Methods in Image Processing

This thesis gives an overview of the use of the level set methods in the field of image science. The similar fast marching method is discussed for comparison, also the narrow band and the particle level set methods are introduced. The level set method is a numerical scheme for representing, deforming and recovering structures in an arbitrary dimensions. It approximates and tracks the moving interfaces, dynamic curves and surfaces. The level set method does not define how and why some boundary is advancing the way it is but simply represents and tracks the boundary. The principal idea of the level set method is to represent the N dimensional boundary in the N+l dimensions. This gives the generality to represent even the complex boundaries. The level set methods can be powerful tools to represent dynamic boundaries, but they can require lot of computing power. Specially the basic level set method have considerable computational burden. This burden can be alleviated with more sophisticated versions of the level set algorithm like the narrow band level set method or with the programmable hardware implementation. Also the parallel approach can be used in suitable applications. It is concluded that these methods can be used in a quite broad range of image applications, like computer vision and graphics, scientific visualization and also to solve problems in computational physics. Level set methods and methods derived and inspired by it will be in the front line of image processing also in the future.

Evolutionary computing in search-based software engineering

This master’s thesis aims to study and represent from literature how evolutionary algorithms are used to solve different search and optimisation problems in the area of software engineering. Evolutionary algorithms are methods, which imitate the natural evolution process. An artificial evolution process evaluates fitness of each individual, which are solution candidates. The next population of candidate solutions is formed by using the good properties of the current population by applying different mutation and crossover operations. Different kinds of evolutionary algorithm applications related to software engineering were searched in the literature. Applications were classified and represented. Also the necessary basics about evolutionary algorithms were presented. It was concluded, that majority of evolutionary algorithm applications related to software engineering were about software design or testing. For example, there were applications about classifying software production data, project scheduling, static task scheduling related to parallel computing, allocating modules to subsystems, N-version programming, test data generation and generating an integration test order. Many applications were experimental testing rather than ready for real production use. There were also some Computer Aided Software Engineering tools based on evolutionary algorithms.

Segmentation model for small and medium size business sector: Case Oy Compaq Computer Ab

Markkinasegmentointi nousi esiin ensi kerran jo 50-luvulla ja se on ollut siitä lähtien yksi markkinoinnin peruskäsitteistä. Suuri osa segmentointia käsittelevästä tutkimuksesta on kuitenkin keskittynyt kuluttajamarkkinoiden segmentointiin yritys- ja teollisuusmarkkinoiden segmentoinnin jäädessä vähemmälle huomiolle. Tämän tutkimuksen tavoitteena on luoda segmentointimalli teollismarkkinoille tietotekniikan tuotteiden ja palveluiden tarjoajan näkökulmasta. Tarkoituksena on selvittää mahdollistavatko case-yrityksen nykyiset asiakastietokannat tehokkaan segmentoinnin, selvittää sopivat segmentointikriteerit sekä arvioida tulisiko tietokantoja kehittää ja kuinka niitä tulisi kehittää tehokkaamman segmentoinnin mahdollistamiseksi. Tarkoitus on luoda yksi malli eri liiketoimintayksiköille yhteisesti. Näin ollen eri yksiköiden tavoitteet tulee ottaa huomioon eturistiriitojen välttämiseksi. Tutkimusmetodologia on tapaustutkimus. Lähteinä tutkimuksessa käytettiin sekundäärisiä lähteitä sekä primäärejä lähteitä kuten case-yrityksen omia tietokantoja sekä haastatteluita. Tutkimuksen lähtökohtana oli tutkimusongelma: Voiko tietokantoihin perustuvaa segmentointia käyttää kannattavaan asiakassuhdejohtamiseen PK-yritys sektorilla? Tavoitteena on luoda segmentointimalli, joka hyödyntää tietokannoissa olevia tietoja tinkimättä kuitenkaan tehokkaan ja kannattavan segmentoinnin ehdoista. Teoriaosa tutkii segmentointia yleensä painottuen kuitenkin teolliseen markkinasegmentointiin. Tarkoituksena on luoda selkeä kuva erilaisista lähestymistavoista aiheeseen ja syventää näkemystä tärkeimpien teorioiden osalta. Tietokantojen analysointi osoitti selviä puutteita asiakastiedoissa. Peruskontaktitiedot löytyvät mutta segmentointia varten tietoa on erittäin rajoitetusti. Tietojen saantia jälleenmyyjiltä ja tukkureilta tulisi parantaa loppuasiakastietojen saannin takia. Segmentointi nykyisten tietojen varassa perustuu lähinnä sekundäärisiin tietoihin kuten toimialaan ja yrityskokoon. Näitäkään tietoja ei ole saatavilla kaikkien tietokannassa olevien yritysten kohdalta.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Communication challenges in virtual teams – a case study of implementing computer-mediated communication technologies in inter-organisational virtual teams

This thesis seeks to answer, if communication challenges in virtual teams can be overcome with the help of computer-mediated communication. Virtual teams are becoming more common work method in many global companies. In order for virtual teams to reach their maximum potential, effective asynchronous and synchronous methods for communication are needed. The thesis covers communication in virtual teams, as well as leadership and trust building in virtual environments with the help of CMC. First, the communication challenges in virtual teams are identified by using a framework of knowledge sharing barriers in virtual teams by Rosen et al. (2007) Secondly, the leadership and trust in virtual teams are defined in the context of CMC. The performance of virtual teams is evaluated in the case study by exploiting these three dimensions. With the help of a case study of two virtual teams, the practical issues related to selecting and implementing communication technologies as well as overcoming knowledge sharing barriers is being discussed. The case studies involve a complex inter-organisational setting, where four companies are working together in order to maintain a new IT system. The communication difficulties are related to inadequate communication technologies, lack of trust and the undefined relationships of the stakeholders and the team members. As a result, it is suggested that communication technologies are needed in order to improve the virtual team performance, but are not however solely capable of solving the communication challenges in virtual teams. In addition, suitable leadership and trust between team members are required in order to improve the knowledge sharing and communication in virtual teams.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.