Dissertation with title: Housing Financing in the Czech Republic using mortgage loan deals with analysis of mortgage loans. Work aims to acquaint with issue and options of financing of housing. First part is focused on legislation and theory of mortgage loans, where is also characterised process of mortgage loans. Further is followed up comparison mortgage loans with loans from building savings, their advantages and disadvantages when are used to financing of housing. In the second part are presented macroeconomic indicators, that affect state of mortgage market. In the third part are analysed mortgage products of four selected banks , that are Hypotecni banky a.s , Komercni banky a.s, Raiffeisenbank a.s. and České spořitelny a.s. Last part is devoted to model example of two hypothetical clients, where it is carried out comparison of the mortgage loan in the selected banks in terms of fundamental parameters and also the APR. Goal of dissertation is to provide recommendation within this form financing of housing and choose the best variant of mortgage loan for hypothetical clients.

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Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finns det för nuvarande inget system som kommer i närheten av den mänskliga förmågan att resonera om omvärlden. För att förenkla problemet har vissa forskare föreslagit en alternativ lösning till helt självständiga robotar som verkar i mänskliga miljöer. Alternativet är att kombinera människors och maskiners förmågor. Exempelvis så kan en person verka på en avlägsen plats, som kanske inte är tillgänglig för personen i fråga på grund av olika orsaker, genom att använda fjärrstyrning. Vid fjärrstyrning skickar operatören kommandon till en robot som verkar som en förlängning av operatörens egen kropp. Segmentering och identifiering av rörelser skapade av en operatör kan användas för att tillhandahålla korrekt assistans vid fjärrstyrning eller samarbete mellan människa och maskin. Assistansen sker ofta inom ramen för virtuella fixturer där eftergivenheten hos fixturen kan justeras under exekveringen för att tillhandahålla ökad prestanda i form av ökad precision och minskad tid för att utföra uppgiften. Den här avhandlingen fokuserar på två aspekter av samarbete mellan människa och maskin. Klassificering av en operatörs rörelser till ett på förhand specificerat tillstånd under en manipuleringsuppgift och assistans under manipuleringsuppgiften baserat på virtuella fixturer. Den specifika tillämpningen som behandlas är manipuleringsuppgifter där en mänsklig operatör styr en robotmanipulator i ett fjärrstyrt eller samarbetande system. En metod för att följa förloppet av en uppgift medan den utförs genom att använda virtuella fixturer presenteras. Istället för att följa en på förhand specificerad plan så har operatören möjlighet att undvika oväntade hinder och avvika från modellen. För att möjliggöra detta estimeras kontinuerligt sannolikheten att operatören följer en viss trajektorie (deluppgift). Estimatet används sedan för att justera eftergivenheten hos den virtuella fixturen så att ett beslut om hur rörelsen ska fixeras kan tas medan uppgiften utförs. En flerlagers dold Markovmodell (eng. layered hidden Markov model) används för att modellera mänskliga färdigheter. En gestemklassificerare som klassificerar en operatörs rörelser till olika grundläggande handlingsprimitiver, eller gestemer, evalueras. Gestemklassificerarna används sedan i en flerlagers dold Markovmodell för att modellera en simulerad fjärrstyrd manipuleringsuppgift. Klassificeringsprestandan utvärderas med avseende på brus, antalet gestemer, typen på den dolda Markovmodellen och antalet tillgängliga träningssekvenser. Den flerlagers dolda Markovmodellen tillämpas sedan på data från en trajektorieföljningsuppgift i 2D och 3D med en robotmanipulator för att ge både kvalitativa och kvantitativa resultat. Resultaten tyder på att den flerlagers dolda Markovmodellen är väl lämpad för att modellera trajektorieföljningsuppgifter och att den flerlagers dolda Markovmodellen är robust med avseende på felklassificeringar i de underliggande gestemklassificerarna. / Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. The majority of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. One reason for this is the inability of today’s robot systems to understand and reason about the world. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there is currently no system that comes close to achieving the reasoning capabilities of humans. In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. For example, using teleoperation a human can operate at a remote site, which may not be accessible for the operator for a number of reasons, by issuing commands to a remote agent that will act as an extension of the operator’s body. Segmentation and recognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online in order to improve the performance in terms of execution time and overall precision. Acquiring, representing and modeling human skills are key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several sub-tasks in order to provide manageable modeling. This thesis is focused on two aspects of human-machine collaborative systems. Classfication of an operator’s motion into a predefined state of a manipulation task and assistance during a manipulation task based on virtual fixtures. The particular applications considered consists of manipulation tasks where a human operator controls a robotic manipulator in a cooperative or teleoperative mode. A method for online task tracking using adaptive virtual fixtures is presented. Rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory sub-task) is estimated and used to automatically adjusts the compliance of a virtual fixture, thus providing an online decision of how to fixture the movement. A layered hidden Markov model is used to model human skills. A gestem classifier that classifies the operator’s motions into basic action-primitives, or gestemes, is evaluated. The gestem classifiers are then used in a layered hidden Markov model to model a simulated teleoperated task. The classification performance is evaluated with respect to noise, number of gestemes, type of the hidden Markov model and the available number of training sequences. The layered hidden Markov model is applied to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the layered hidden Markov model is suitable for modeling teleoperative trajectory-tracking tasks and that the layered hidden Markov model is robust with respect to misclassifications in the underlying gestem classifiers. / QC 20101102

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

"Fixtures form an important factor in traditional and modern flexible manufacturing systems, since fixture design directly affects manufacturing quality and productivity. Hence, it is necessary to evaluate quality of fixture design. The fixturing accessibility refers to machining feature accessibility and loading /unloading accessibility. The development of Computer Aided Fixture Design (CAFD) has simplified this task. Fixture design activities include setup planning, fixture planning and fixture configuration design. Fixture design verification comes next. Fixturing accessibility using Computer Aided Fixture Design is part of the verification process and has not received much attention till date. Machining feature accessibility analysis involves the evaluation of possible interference between fixture components and the cutting tool, which moves with pre-programmed tool path, while the loading and unloading accessibility relates to the ease with which the operator attaches/detaches the workpiece from the surrounding manufacturing environment. This research has three main focuses. The first focus is to evaluate machining feature accessibility, by integrating fixture designs in SolidWorks and the NC programming in Esprit. The main goals are evaluation of fixture design for any kind of interference between tool/workpiece/fixtures and enable Esprit to indicate interference, if any. The next step is to modify the fixture design accordingly and thus, finally obtain an interference free fixture design by reiteration. The second and third focuses deal with analysis of loading and unloading accessibility. A simulation based approach is applied to evaluate loading/unloading paths for different workpiece-fixture setups and checking interference in a dynamic mode. Then the third focus is to develop analysis method and criteria of comparisons of fixturing accessibility in different fixture designs. Thus, this research establishes methods of analysis for accessibilities in fixture design. Also, the guidelines for good fixture design will prove to be of great use to both, the beginners as well as the experienced fixture designers in this field. "

The present master’s thesis deals with the appropriate measuring methods to determine the technical state of the machine tool for production of pressure tanks of Common Rail injection system. The main topic of this thesis is also a constructional adjustment of problem clamping components whose function affects the quality of the pressure tank.

Arbetet är ett utredningsarbete som går ut på att försöka lokalisera felkällor och göra förbättringar på en testutrustning som mäter rodervinklar på akterdelen på en robot. Rapporten innehåller en översiktlig bild över den tidigare metoden och dess felkällor som hittas vid test av den tidigare metoden. Utredningen utmanar också många utav antagandena som är gjorda för beräkningarna av den tidigare metoden. Detta utförs för att kunna bekräfta eller dementera antagandena. Detta görs i form av matematiska modeller som testar olika delar av metoden. Varje del i rapporten består av en beskrivning av vad kapitlet avser följt av felkällorna som upptäckts i metoden när den testas i modellen. Det framtagna metodförslaget utsätts samma prövning som den tidigare metoden för att utreda skillnaderna. I resultatet kan man se de slutsatser som dragit av varje del av utförandet. / The task is an investigation to try and locate errors and make improvements on a test equipment that measures rudder angles on the rear-end of a robot. The report contains an overview of the previous method and the errors that is found by testing it. The investigation also challenges many of the assumptions made when the previous method was made. This was made in order to either confirm or deny the assumptions. This is done by the use of mathematical models to simulate different parts of the method. Each part of the report consists of a description of the section followed by explaining the discovered errors that was found by testing the method in the models. The new produced method suggestion is exposed to the same tests as the previous method to discern the differences. The conclusions made from the sections can be found in the results.

Detta produktutvecklingsprojekt är utfört av Fredric Ericsson och Hampus Hammarbäck som examensarbete i Maskinteknik på Linnéuniversitetet. Projektet utfördes i samarbete med uppdragsgivarna Alutrade och Masterlite. Det följer hur en fasadlampa som ska belysa skyltar konstrueras och fokuserar på att systematisera konstruktionsprocessen. Bakgrunden till projektet ligger i brister hos den befintliga armaturen. Målet är att konstruera en armatur som är mer konkurrenskraftig än den befintliga. För att uppnå målet studerades problemet och modern produktutvecklingsteori tillämpas. Produktionsmetoderna är begränsade till huvudsakligen strängpressning av aluminium och delvis stansning av uppdragsgivaren, vilket låser produkten till att huvudsakligen bestå av aluminiumprofiler. Det resulterar i en kostnadseffektiv konstruktion av en armatur till en fasadlampa med en väldokumenterad och systematiserad konstruktionsprocess. / This product development project is made by Fredric Ericsson and Hampus Hammarbäck as a thesis project in Mechanical engineering with focus on product development at Linnaeus University. The project was in collaboration between the students and the outsourcing companies Alutrade and Masterlite. It follows the process of designing a flood light fixture for illuminating signs and focuses on systematization of the design process. The reason to the project is flaws in the outdated current fixture. The aim of the project is to design a fixture that is more competitive in the lightning market. This was done by studying the problem and applying product development theory. The production methods is limited to mainly aluminium extrusion and metal sheet punching by the outsourcer which therefore limits the design options to consist mainly by aluminium profiles. It results in a cost effective solution of a flood light fixture with a well documented and systemized design process.

This diploma thesis deals with the design and implementation of a comprehensive solution for the collection of production indicators in the commercial vehicle sector, called Automotive. The design is done by analyzing the state and identifying the bottlenecks of the OEE (Total Equipment Effectivenes). Further, the thesis describes the general theory of efficiency, availability and FPY (First Pass Yield). Realization and visualization of several production equipment variants for raw data collection from production lines in the manufacturing company IMI International CZ s.r.o. - a leading manufacturer of pneumatic components for the automotive and automation industry.

This report is a Bachelor thesis and has been done within the area of mechanical engineering at Linköping university. The thesis has been performed as a project upon request from the company Bosch Thermoteknik AB in Tranås, Sweden. The company produces heat pumps, and in the process, brazing is used as a method to build the pipe modules the pumps contain. The brazing is currently made in terms of craftmanship in the production at the company. The purpose of this project was to investigate the possibilities to automate a brazing process and present a possible solution the company can use. The solution should mainly be based on the use of an industrial robot together with an equipment for induction heating.An iterative concept-generating process for production development was used as method to perform the project. Based on the method, a result consisting of two main areas were acquired. The first area was a study of the prerequisites for the project – the process demands and a review of the technique the resources used. The other area was the concept solution for how a brazing process can be automated.The concept itself was built by two areas. One physical part in the shape of a fixture to carry the pipe-modules during the process as well as tools and aids for the robot function. The other area was the program that forms the robot’s function. The program was based on solutions to enable identification of the modules, positioning of the robot arm with tool and a verification part to ensure the brazing has been done. The robot program was founded on a written program script to control the robot.