Underwater Remote Welding Technology for Offshore Structures

The construction of offshore structures, equipment and devices requires a high level of mechanical reliability in terms of strength, toughness and ductility. One major site for mechanical failure, the weld joint region, needs particularly careful examination, and weld joint quality has become a major focus of research in recent times. Underwater welding carried out offshore faces specific challenges affecting the mechanical reliability of constructions completed underwater. The focus of this thesis is on improvement of weld quality of underwater welding using control theory. This research work identifies ways of optimizing the welding process parameters of flux cored arc welding (FCAW) during underwater welding so as to achieve desired weld bead geometry when welding in a water environment. The weld bead geometry has no known linear relationship with the welding process parameters, which makes it difficult to determine a satisfactory weld quality. However, good weld bead geometry is achievable by controlling the welding process parameters. The doctoral dissertation comprises two sections. The first part introduces the topic of the research, discusses the mechanisms of underwater welding and examines the effect of the water environment on the weld quality of wet welding. The second part comprises four research papers examining different aspects of underwater wet welding and its control and optimization. Issues considered include the effects of welding process parameters on weld bead geometry, optimization of FCAW process parameters, and design of a control system for the purpose of achieving a desired bead geometry that can ensure a high level of mechanical reliability in welded joints of offshore structures. Artificial neural network systems and a fuzzy logic controller, which are incorporated in the control system design, and a hybrid of fuzzy and PID controllers are the major control dynamics used. This study contributes to knowledge of possible solutions for achieving similar high weld quality in underwater wet welding as found with welding in air. The study shows that carefully selected steels with very low carbon equivalent and proper control of the welding process parameters are essential in achieving good weld quality. The study provides a platform for further research in underwater welding. It promotes increased awareness of the need to improve the quality of underwater welding for offshore industries and thus minimize the risk of structural defects resulting from poor weld quality.

A Topological Approach to Fuzzy Sets

This thesis presents a topological approach to studying fuzzy setsby means of modifier operators. Modifier operators are mathematical models, e.g., for hedges, and we present briefly different approaches to studying modifier operators. We are interested in compositional modifier operators, modifiers for short, and these modifiers depend on binary relations. We show that if a modifier depends on a reflexive and transitive binary relation on U, then there exists a unique topology on U such that this modifier is the closure operator in that topology. Also, if U is finite then there exists a lattice isomorphism between the class of all reflexive and transitive relations and the class of all topologies on U. We define topological similarity relation "≈" between L-fuzzy sets in an universe U, and show that the class LU/ ≈ is isomorphic with the class of all topologies on U, if U is finite and L is suitable. We consider finite bitopological spaces as approximation spaces, and we show that lower and upper approximations can be computed by means of α-level sets also in the case of equivalence relations. This means that approximations in the sense of Rough Set Theory can be computed by means of α-level sets. Finally, we present and application to data analysis: we study an approach to detecting dependencies of attributes in data base-like systems, called information systems.

Fuzzy Principal Components Analysis

Due to the large number of characteristics, there is a need to extract the most relevant characteristicsfrom the input data, so that the amount of information lost in this way is minimal, and the classification realized with the projected data set is relevant with respect to the original data. In order to achieve this feature extraction, different statistical techniques, as well as the principal components analysis (PCA) may be used. This thesis describes an extension of principal components analysis (PCA) allowing the extraction ofa finite number of relevant features from high-dimensional fuzzy data and noisy data. PCA finds linear combinations of the original measurement variables that describe the significant variation in the data. The comparisonof the two proposed methods was produced by using postoperative patient data. Experiment results demonstrate the ability of using the proposed two methods in complex data. Fuzzy PCA was used in the classificationproblem. The classification was applied by using the similarity classifier algorithm where total similarity measures weights are optimized with differential evolution algorithm. This thesis presents the comparison of the classification results based on the obtained data from the fuzzy PCA.

Revenue Logic inSoftware Business Context

Tutkimuksen tavoitteena oli selvittää ohjelmistotoimialan avaintekijöitä, jotka vaikuttavat yrityksen ansaintalogiikkaan sekä lisätä tietoisuutta ansaintalogiikan muodostumisesta pienissä ja keskisuurissa ohjelmistoyrityksissä. Tutkimuksen teoreettisessa osassa keskityttiin tarkastelemaan ansaintalogiikan, strategian ja liiketoimintamallin käsitteiden suhteita sekä arvioitiin toimialan osatekijöiden, hinnoitteluperiaatteiden ja ansaintamallien vaikutusta ansainnan muodostumiseen ohjelmistotoimialalla. Ohjelmistotuote ja - palveluliiketoimintaa koskien oli merkityksellistä tutkia tuotteistamisasteen ja arvoketjujen vaikutusta ansaintalogiikan muodostumisessa sekä esitellä erilaisia, tyypillisiä ohjelmistotoimialalla käytettäviä hinnoittelumenetelmiä. Työn empiirisessä osassa tarkasteltiin 23 suomalaisen ohjelmistoalan yrityksen ansaintalogiikkaa. Tiedot kerättiin haastatteluin ja analysoitiin laadullisen tutkimuksen keinoin. Tutkimustulokset korostivat ansaintalogiikan 'epämääräisyyttä' terminä mutta osoittivat, että ydinliiketoimintaan keskittyminen, tuote-, palvelu-, tai projektiliiketoiminnan osaaminen, tuotteistusaste ja kanavavalinnat ovat avaintekijöitä ansaintalogiikanmuodostumisessa. Ansaintalogiikan muodostamiseen liittyy paljon yrityksen sisäisiä ja ulkoisia haasteita sekä muutospaineita, eikä ohjelmistotoimialalla ole todennettavissa yhtä yleismaailmallista, menestyksen takaavaa ansaintalogiikkaa.

Fuzzy subgroups, algebraic and topological points of view and complex analysis

Fuzzy subsets and fuzzy subgroups are basic concepts in fuzzy mathematics. We shall concentrate on fuzzy subgroups dealing with some of their algebraic, topological and complex analytical properties. Explorations are theoretical belonging to pure mathematics. One of our ideas is to show how widely fuzzy subgroups can be used in mathematics, which brings out the wealth of this concept. In complex analysis we focus on Möbius transformations, combining them with fuzzy subgroups in the algebraic and topological sense. We also survey MV spaces with or without a link to fuzzy subgroups. Spectral space is known in MV algebra. We are interested in its topological properties in MV-semilinear space. Later on, we shall study MV algebras in connection with Riemann surfaces. In fact, the Riemann surface as a concept belongs to complex analysis. On the other hand, Möbius transformations form a part of the theory of Riemann surfaces. In general, this work gives a good understanding how it is possible to fit together different fields of mathematics.

Conditions for different autonomy regimes in the world : a fuzzy-set application

Avhandlingen behandlar temat territoriell autonomi ur ett globalt perspektiv. Syftet är dels att kartlägga de territoriella autonomierna i världen och dels att visa hur en ny metod som fuzzy-set kan användas inom ämnesområdet jämförande politik. Forskningsproblemet är att försöka finna de bakgrundsfaktorer som förklarar uppkomsten av territoriell autonomi som sådant. Territoriella autonomier ses som särlösningar inom stater. Dessa regioner har erhållit en specialställning i förhållande till andra regioner inom respektive stat och även i förhållande till centralmakten i övrigt. Regionerna kan därför ses som undantag inom det enhetliga federala, regionala eller decentraliserade systemet inom en viss stat ifråga. En kartläggning visar att det finns 65 specialregioner fördelade på 25 stater i världen. De flesta av dessa utgörs av öar. Resultaten visar att det finns två vägar vilka leder till territoriell autonomi i allmänhet. Den ena vägen är en kombination av etnisk särprägel och liten befolkningsmängd, medan den andra vägen utgörs av kombinationen av historiska orsaker och geografiskt avstånd. Båda vägar är lika giltiga och förutsättningen är en demokratisk miljö.

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.

A quantitative view on fuzzy numbers

Since its introduction, fuzzy set theory has become a useful tool in the mathematical modelling of problems in Operations Research and many other fields. The number of applications is growing continuously. In this thesis we investigate a special type of fuzzy set, namely fuzzy numbers. Fuzzy numbers (which will be considered in the thesis as possibility distributions) have been widely used in quantitative analysis in recent decades. In this work two measures of interactivity are defined for fuzzy numbers, the possibilistic correlation and correlation ratio. We focus on both the theoretical and practical applications of these new indices. The approach is based on the level-sets of the fuzzy numbers and on the concept of the joint distribution of marginal possibility distributions. The measures possess similar properties to the corresponding probabilistic correlation and correlation ratio. The connections to real life decision making problems are emphasized focusing on the financial applications. We extend the definitions of possibilistic mean value, variance, covariance and correlation to quasi fuzzy numbers and prove necessary and sufficient conditions for the finiteness of possibilistic mean value and variance. The connection between the concepts of probabilistic and possibilistic correlation is investigated using an exponential distribution. The use of fuzzy numbers in practical applications is demonstrated by the Fuzzy Pay-Off method. This model for real option valuation is based on findings from earlier real option valuation models. We illustrate the use of number of different types of fuzzy numbers and mean value concepts with the method and provide a real life application.

Automation Concept for Electrically Excited LV Synchronous Motor: Implementation into AC500 Programmable Logic Environment

This bachelor’s thesis is a part of the research project realized in the summer 2011 in Lappeenranta University of Technology. The goal of the project was to develop an automation concept for controlling the electrically excited synchronous motor. Thesis concentrates on the implementation of the automation concept into the ABB’s AC500 programmable logic enviroment. The automation program was developed as a state machine with the ABB’s PS501 Control Builder software. For controlling the automation program is developed a fieldbus control and with CodeSys Visualization Tool a local control with control panel. The fieldbus control is done to correspond the ABB drives communication profile and the local control is implemented with a function block which feeds right control words into the statemachine. A field current control of the synchronous motor is realized as a method presented in doctoral thesis of Olli Pyrhönen (Pyrhönen 1998). The Method combines stator flux and torque based openloop control and power factor based feedback control.

Bankruptcy prediction using fuzzy methods

In this thesis, a classi cation problem in predicting credit worthiness of a customer is tackled. This is done by proposing a reliable classi cation procedure on a given data set. The aim of this thesis is to design a model that gives the best classi cation accuracy to e ectively predict bankruptcy. FRPCA techniques proposed by Yang and Wang have been preferred since they are tolerant to certain type of noise in the data. These include FRPCA1, FRPCA2 and FRPCA3 from which the best method is chosen. Two di erent approaches are used at the classi cation stage: Similarity classi er and FKNN classi er. Algorithms are tested with Australian credit card screening data set. Results obtained indicate a mean classi cation accuracy of 83.22% using FRPCA1 with similarity classi- er. The FKNN approach yields a mean classi cation accuracy of 85.93% when used with FRPCA2, making it a better method for the suitable choices of the number of nearest neighbors and fuzziness parameters. Details on the calibration of the fuzziness parameter and other parameters associated with the similarity classi er are discussed.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

"Fuzzy front end" ja sen menestyksekäs johtaminen teollisuusyrityksessä

Työssä käsitellään innovaatioprosessin ensimmäistä ”fuzzy front end” -vaihetta, jota työssä kutsutaan front end -vaiheeksi. Front end -vaihe on innovaatioprosessin alustava tutkimus ja suunnittelu vaihe ennen teknistä kehittämisvaihetta. Front end -vaihetta on tutkittu innovaatioprosessin osista vähiten, sekä se on useimmille yrityksillä sumea ja vaikeasti käsitettävä. Tutkimusten mukaan front end -vaiheen osaaminen on kuitenkin erittäin merkittävä tekijä yrityksen innovatiivisuudelle. Työssä avataan innovaatioprosessin sisältöä ja tavoitteita, sekä vertaillaan käytössä olevia malleja front end -vaiheen rakenteesta. Työssä selvitetään avaintekijöitä front end -vaiheen menestykseen ja tehokkuuteen. Lisäksi käsitellään johtamisen tekijöitä, jotka edesauttavat onnistumaan front end -vaiheessa.

Fuzzy tolerance/equivalence relations from intersection, union and complement operators and their application to a clustering method

This master thesis work introduces the fuzzy tolerance/equivalence relation and its application in cluster analysis. The work presents about the construction of fuzzy equivalence relations using increasing generators. Here, we investigate and research on the role of increasing generators for the creation of intersection, union and complement operators. The objective is to develop different varieties of fuzzy tolerance/equivalence relations using different varieties of increasing generators. At last, we perform a comparative study with these developed varieties of fuzzy tolerance/equivalence relations in their application to a clustering method.